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#81

Video de treinamento e tutoriais online / US Recruiter Course Master The...

Última mensagem por joomlamz - 26 de Julho de 2024, 01:58

Last updated 10/2023 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.25 GB | Duration: 4h 43m

Master the Recruitment Process for Technical and Non-Technical Candidates Recruitment in the USA

What you'll learn
Understand the key elements of US culture, including values, communication styles, and work expectations.
Be able to adapt your recruitment and onboarding process to be more inclusive and welcoming for candidates from diverse backgrounds.
Understand the different types of US visas available to foreign workers and the requirements for each type.
Be able to assist candidates with the US immigration process, including completing visa applications and gathering supporting documentation.
Understand the basics of US taxation for foreign workers, including income tax, Social Security tax, and Medicare tax.
Be able to answer candidate questions about US taxation
Understand the different stages of the US recruitment process, from sourcing to onboarding.
Be able to execute all stages of the recruitment process effectively, including writing job descriptions, sourcing candidates, screening resumes, submissions
nderstand the different types of hiring in the US, such as full-time, part-time, contract, and temporary.
Learn how to cold call and email candidates effectively.
Understand the different ways to submit resumes to clients in the US.
Learn how to conduct effective interviews, both in-person and remotely.
Develop a strong understanding of the US job market and the latest trends in US recruitment.
Conduct a full and fair recruitment process for technical and non-technical candidates in the US.
Assist candidates with the US immigration process and US taxation.
Understand and adapt to US culture.
Build and maintain relationships with clients and candidates in the US.
Stay up-to-date on the latest trends in US recruitment.

Requirements
Basic understanding of the recruitment process
Ability to communicate effectively in English
Access to a computer and the internet

Description
The US Recruiter Course: Master the Recruitment Process [The Ultimate Course on US Recruitment]This comprehensive course on US IT recruitment will teach you everything you need to know to be successful in this competitive field. You will learn about US culture, employment law and regulations, immigration and visas, taxation, compensation and benefits, and US IT recruitment best practices. You will also learn how to use a variety of recruiting tools and technologies, and how to manage staffing vendors.This course is designed for offshore and onsite recruiters who want to learn more about the US IT recruitment market and how to be successful in it. It is also ideal for anyone who is interested in a career in US IT recruitment.Why You Should EnrollIf you are serious about a career in US IT recruitment, then this course is for you. You will learn from experienced recruiters who will teach you the skills and knowledge you need to be successful. You will also have the opportunity to network with other recruiters and hiring managers from the US IT industry.Here are just a few of the benefits of enrolling in this course:Learn about US culture, employment law and regulations, immigration and visas, taxation, compensation and benefits, and US IT recruitment best practices.Learn how to use a variety of recruiting tools and technologies.Learn how to manage staffing vendors.Network with other recruiters and hiring managers from the US IT industry.Gain the skills and knowledge you need to be successful in the US IT recruitment market.If you are ready to take your US IT recruitment career to the next level, then enroll in this course today!By the end of this course, you will be able to:Conduct a full and fair recruitment process for technical and non-technical candidates in the US.Assist candidates with the US immigration process and US taxation.Understand and adapt to US culture.Build and maintain relationships with clients and candidates in the US.Stay up-to-date on the latest trends in US recruitment.This course is ideal for anyone who is interested in a career as a US recruiter, including:Indian and US recruiters who want to learn about the US recruitment process.Onsite and offshore recruiters who want to work in the US.Recruiters who want to improve their recruitment process and attract and retain the best candidates.Recruiters who want to advance their careers in US recruitment.Course Outline:Module 1: Introduction to US CultureModule 2: US Employment Law and RegulationsModule 3: US Immigration & VisasModule 4: US TaxationModule 5: US Compensation and BenefitsModule 6: Introduction to US RecruitmentModule 7: Staffing Vendor ManagementModule 8: Candidate Sourcing & RecruitingModule 9: US Recruitment StrategiesModule 10: Recruitment Tools and TechnologiesModule 11: US Recruitment Best PracticesModule 12: Case Studies in US RecruitmentModule 13: Additional Resources for US RecruitersModule 14: Resume Preparation, Job Search & Interview ManagementEnroll today and start your journey to becoming a successful US recruiter!

Overview
Section 1: US Culture

Lecture 1 Introduction to US Culture

Lecture 2 US Culture for Offshore & Onsite Recruiters

Lecture 3 Tips for Recruiters

Lecture 4 US Time Zones & Federal Holidays

Lecture 5 US Postal Service - Zip Codes

Lecture 6 Case Study: The Benefits of US Cultural Knowledge for Offshore Recruiters

Section 2: US Employment Law and Regulations

Lecture 7 EEO & Non-Discrimination, FLSA, EPA, FMLA, ADA, ADEA Laws

Lecture 8 Employment Laws & Regulations Tips

Lecture 9 Different Laws & examples

Section 3: US Immigration & Visas

Lecture 10 Introduction to US Immigration

Lecture 11 Types of Visas and Work Permits

Lecture 12 Challenges in Hiring Foreign Workers

Lecture 13 Tips for Recruiters

Lecture 14 F1 Student - OPT EAD

Lecture 15 H1B Visa (Professional Work permit)

Lecture 16 Employment Authorization Document (EAD)

Lecture 17 Permanent Residence (Green Card) & US Citizenship

Lecture 18 Case Study with Examples

Section 4: US Payroll & Taxation

Lecture 19 US Payroll and Taxation

Lecture 20 US Taxation for Citizens, Residents & Foreign Workers - W2, 1099 & Corp to Corp

Lecture 21 W2 Form, 1099 Form and Corp to Corp Arrangements

Section 5: US Compensation and Benefits

Lecture 22 US Compensation and Benefits

Lecture 23 Salary & Rate Negotiation

Lecture 24 Case study

Section 6: Introduction to US IT Recruitment

Lecture 25 HR Function, Recruitment & Recruiter Job

Lecture 26 US Job Market

Lecture 27 The Different Types of US Employers

Lecture 28 Types of Hiring in the US

Lecture 29 US Security Clearances & Types

Lecture 30 Industries, Verticals and Levels of Hiring in the US

Lecture 31 End to End US Recruitment Process

Section 7: Staffing Vendor Management

Lecture 32 End to End Staffing Vendor Management

Lecture 33 Case Study

Section 8: RTLD Methodology for Requirement Understanding & Sourcing Resumes

Lecture 34 IT Roles and Groups

Lecture 35 Job Description & Resume Explanations based on Role, Technology, Level & Domain

Section 9: Candidate Sourcing Using Boolean, Google & X-ray Search

Lecture 36 Boolean Search

Lecture 37 Search ecosystem and search engine components

Lecture 38 Boolean String Operators Search on Google

Lecture 39 Google Advance Search Engine Operators and Filters

Lecture 40 PHP Development Explaination

Lecture 41 PHP - Google Advance Search String

Lecture 42 Recruitin.net - X-Ray Search Tool

Lecture 43 Boolean Search Builder Tool

Lecture 44 Boolean Search String Bank & Suggestion Tool

Lecture 45 Dice & Monseter Job Board - Boolean Search String Knowledge Base

Section 10: US Recruiting Process

Lecture 46 Recruitment Process

Lecture 47 Job Description Analysis - Step 1

Lecture 48 Job Posting - Step 2

Lecture 49 Resume Screening - Step 3

Lecture 50 Selection & Qualifying Candidate - Step 4

Lecture 51 Resume Submission Process - Step 5

Lecture 52 Review, feedback and interview process

Lecture 53 After Submission Internal Process

Lecture 54 Recruiter Performance

Lecture 55 Recruitment Performance Metrix

Lecture 56 Day to Day Task of a Recruiter

Lecture 57 Case Studies of Successful US Recruitment Campaigns

Section 11: US Recruitment Strategies

Lecture 58 Referral Management

Lecture 59 Social Media Recruiting for Recruiters

Lecture 60 LinkedIn Profile & Personal Branding

Section 12: US Recruitment Best Practices

Lecture 61 Building Relationships with US Hiring Managers

Lecture 62 Ethical US Recruitment Practices

Lecture 63 Case Studies of Challenges in US Recruitment and How to Overcome Them

Section 13: Additional Resources for US Recruiters

Lecture 64 US Recruitment Professional Organizations

Lecture 65 US Recruitment Books and Conferences

Lecture 66 US Recruitment Blogs and Websites

Lecture 67 Case Study: Knowledge and Awareness

Section 14: Resume Preparation, Job Search & Interview Management

Lecture 68 How to become Recruiter

Lecture 69 Recruiter Career Path

Lecture 70 Resume Preparation

Lecture 71 Job Search

Lecture 72 Interview Management

Indian and US recruiters,Onsite and offshore recruiters,Recruiters who want to learn about US recruitment best practices.,Recruiters who want to advance their careers in US recruitment,Domestic recruiters who want to start career in US Recruitment,BPO professionals want to start career in US IT Recruitment,Anyone who is interested in starting their career as Technical Recruiter in the US offshore,Anyone who wants to join US Staffing and Recruiting agency in India for US Staffing process

https://rapidgator.net/file/274047fad5a0b3902223f0f2b516d34a/US.Recruiter.Course.Master.the.Recruitment.Process.part1.rar
https://rapidgator.net/file/a2e3b737ba93e9cb78d0222d047250ce/US.Recruiter.Course.Master.the.Recruitment.Process.part2.rarF

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#82

Video de treinamento e tutoriais online / Migrating to AWS (2024)

Última mensagem por joomlamz - 26 de Julho de 2024, 01:56

Lançado em 7/2024
MP4 | Vídeo: h264, 1280x720 | Áudio: AAC, 44,1 KHz, 2 canais
Nível de habilidade: Intermédio | Género: eLearning | Idioma: Inglês + srt | Duração: 1h 55m | Tamanho: 222 MB

Há muito a considerar ao migrar dados, bases de dados e cargas de trabalho locais para a Amazon Web Services. Neste curso, Vicky Seno, instrutora certificada pela AWS, orienta-o no planeamento e implementação da migração utilizando as práticas recomendadas pela AWS e apresenta várias demonstrações práticas de migrações simuladas. Vicky aborda o AWS Cloud Adoption Framework (AWS CAF) e os passos que deve seguir para se preparar, planear e acompanhar a sua migração para a cloud. Partilha os seis Rs das estratégias de migração e, em seguida, orienta-o na execução da migração de dados propriamente dita. A migração de servidores anda de mãos dadas com a migração de dados, e Vicky oferece técnicas e demonstrações úteis. Termina com uma discussão aprofundada sobre o processo de migração de bases de dados, incluindo uma demonstração de um exemplo de migração de bases de dados com o AWS Database Migration Service.

Pagina inicial
https://www.linkedin.com/learning/migrating-to-aws-24333862

https://rapidgator.net/file/989012e7f60ef095a2f9c853853926db

https://nitroflare.com/view/320DF00E9B773BF

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#83

Video de treinamento e tutoriais online / Building a Chat Application wi...

Última mensagem por joomlamz - 26 de Julho de 2024, 01:47

Lançado em 7/2024
MP4 | Vídeo: h264, 1280x720 | Áudio: AAC, 44,1 KHz, 2 canais
Nível: Intermédio | Género: eLearning | Idioma: Inglês + vtt | Duração: 1h 40m | Tamanho: 247MB

O React Native permite criar uma variedade de aplicações. Este curso irá ensinar-lhe como construir uma aplicação de chat completa usando React Native.

A comunicação em tempo real é possível devido à ampla disponibilidade da Internet. Neste curso, Construir uma aplicação de chat com React Native, aprenderá a construir uma aplicação de chat completa. Primeiro, irá explorar os fundamentos dos WebSockets. A seguir, descobrirá como gerar um projeto utilizando Expo. Por fim, aprenderá como se ligar a um servidor e transmitir mensagens entre clientes. Ao concluir este curso, terá as competências e o conhecimento do React Native necessários para construir uma aplicação de chat completa.

Pagina inicial
https://www.pluralsight.com/courses/react-native-building-chat-application

https://rapidgator.net/file/33821de8eb79bd3e4ba2fe30300af90c

https://nitroflare.com/view/DC6E696364F9DEF

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#84

Video de treinamento e tutoriais online / Complete A.I. & Machine Learni...

Última mensagem por joomlamz - 26 de Julho de 2024, 01:45

Last updated 5/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 30.37 GB | Duration: 43h 55m

Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

What you'll learn
Become a Data Scientist and get hired
Master Machine Learning and use it on the job
Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
Use modern tools that big tech companies like Google, Apple, Amazon and Meta use
Present Data Science projects to management and stakeholders
Learn which Machine Learning model to choose for each type of problem
Real life case studies and projects to understand how things are done in the real world
Learn best practices when it comes to Data Science Workflow
Implement Machine Learning algorithms
Learn how to program in Python using the latest Python 3
How to improve your Machine Learning Models
Learn to pre process data, clean data, and analyze large data.
Build a portfolio of work to have on your resume
Developer Environment setup for Data Science and Machine Learning
Supervised and Unsupervised Learning
Machine Learning on Time Series data
Explore large datasets using data visualization tools like Matplotlib and Seaborn
Explore large datasets and wrangle data using Pandas
Learn NumPy and how it is used in Machine Learning
A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
Learn to use the popular library Scikit-learn in your projects
Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
Learn to perform Classification and Regression modelling
Learn how to apply Transfer Learning

Requirements
No prior experience is needed (not even Math and Statistics). We start from the very basics.
A computer (Linux/Windows/Mac) with internet connection.
Two paths for those that know programming and those that don't.
All tools used in this course are free for you to use.

Description
Become a complete A.I., Data Scientist and Machine Learning engineer! Join a live online community of 900,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Meta, + other top tech companies. You will go from zero to mastery!Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want. The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don't worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!The topics covered in this course are:- Data Exploration and Visualizations- Neural Networks and Deep Learning- Model Evaluation and Analysis- Python 3- Tensorflow 2.0- Numpy- Scikit-Learn- Data Science and Machine Learning Projects and Workflows- Data Visualization in Python with MatPlotLib and Seaborn- Transfer Learning- Image recognition and classification- Train/Test and cross validation- Supervised Learning: Classification, Regression and Time Series- Decision Trees and Random Forests- Ensemble Learning- Hyperparameter Tuning- Using Pandas Data Frames to solve complex tasks- Use Pandas to handle CSV Files- Deep Learning / Neural Networks with TensorFlow 2.0 and Keras- Using Kaggle and entering Machine Learning competitions- How to present your findings and impress your boss- How to clean and prepare your data for analysis- K Nearest Neighbours- Support Vector Machines- Regression analysis (Linear Regression/Polynomial Regression)- How Hadoop, Apache Spark, Kafka, and Apache Flink are used- Setting up your environment with Conda, MiniConda, and Jupyter Notebooks- Using GPUs with Google ColabBy the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.Here's the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don't know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don't really explain things well enough for you to go off on your own and solve real life machine learning problems. Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don't know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows. Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career. You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!Click "Enroll Now" and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!Taught By:Daniel Bourke:A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen.I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more.Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups.Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims.My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?".Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views.I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible.My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know.Questions are always welcome.Andrei Neagoie:Andrei is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc... He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life. Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don't know where to start when learning a complex subject matter, or even worse, most people don't have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student's valuable time. Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities. Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way. Taking his experience in educational psychology and coding, Andrei's courses will take you on an understanding of complex subjects that you never thought would be possible. See you inside the course!

Overview
Section 1: Introduction
Lecture 1 Course Outline
Lecture 2 Join Our Online Classroom!
Lecture 3 Exercise: Meet Your Classmates & Instructor
Lecture 4 Asking Questions + Getting Help
Lecture 5 Your First Day
Section 2: Machine Learning 101
Lecture 6 What Is Machine Learning?
Lecture 7 AI/Machine Learning/Data Science
Lecture 8 ZTM Resources
Lecture 9 Exercise: Machine Learning Playground
Lecture 10 How Did We Get Here?
Lecture 11 Exercise: YouTube Recommendation Engine
Lecture 12 Types of Machine Learning
Lecture 13 Are You Getting It Yet?
Lecture 14 What Is Machine Learning? Round 2
Lecture 15 Section Review
Lecture 16 Monthly Coding Challenges, Free Resources and Guides
Section 3: Machine Learning and Data Science Framework
Lecture 17 Section Overview
Lecture 18 Introducing Our Framework
Lecture 19 6 Step Machine Learning Framework
Lecture 20 Types of Machine Learning Problems
Lecture 21 Types of Data
Lecture 22 Types of Evaluation
Lecture 23 Features In Data
Lecture 24 Modelling - Splitting Data
Lecture 25 Modelling - Picking the Model
Lecture 26 Modelling - Tuning
Lecture 27 Modelling - Comparison
Lecture 28 Overfitting and Underfitting Definitions
Lecture 29 Experimentation
Lecture 30 Tools We Will Use
Lecture 31 Optional: Elements of AI
Section 4: The 2 Paths
Lecture 32 The 2 Paths
Lecture 33 Python + Machine Learning Monthly
Lecture 34 Endorsements On LinkedIN
Section 5: Data Science Environment Setup
Lecture 35 Section Overview
Lecture 36 Introducing Our Tools
Lecture 37 What is Conda?
Lecture 38 Conda Environments
Lecture 39 Mac Environment Setup
Lecture 40 Mac Environment Setup 2
Lecture 41 Windows Environment Setup
Lecture 42 Windows Environment Setup 2
Lecture 43 Linux Environment Setup
Lecture 44 Sharing your Conda Environment
Lecture 45 Jupyter Notebook Walkthrough
Lecture 46 Jupyter Notebook Walkthrough 2
Lecture 47 Jupyter Notebook Walkthrough 3
Section 6: Pandas: Data Analysis
Lecture 48 Section Overview
Lecture 49 Downloading Workbooks and Assignments
Lecture 50 Pandas Introduction
Lecture 51 Series, Data Frames and CSVs
Lecture 52 Data from URLs
Lecture 53 Quick Note: Upcoming Videos
Lecture 54 Describing Data with Pandas
Lecture 55 Selecting and Viewing Data with Pandas
Lecture 56 Quick Note: Upcoming Videos
Lecture 57 Selecting and Viewing Data with Pandas Part 2
Lecture 58 Manipulating Data
Lecture 59 Manipulating Data 2
Lecture 60 Manipulating Data 3
Lecture 61 Assignment: Pandas Practice
Lecture 62 How To Download The Course Assignments
Section 7: NumPy
Lecture 63 Section Overview
Lecture 64 NumPy Introduction
Lecture 65 Quick Note: Correction In Next Video
Lecture 66 NumPy DataTypes and Attributes
Lecture 67 Creating NumPy Arrays
Lecture 68 NumPy Random Seed
Lecture 69 Viewing Arrays and Matrices
Lecture 70 Manipulating Arrays
Lecture 71 Manipulating Arrays 2
Lecture 72 Standard Deviation and Variance
Lecture 73 Reshape and Transpose
Lecture 74 Dot Product vs Element Wise
Lecture 75 Exercise: Nut Butter Store Sales
Lecture 76 Comparison Operators
Lecture 77 Sorting Arrays
Lecture 78 Turn Images Into NumPy Arrays
Lecture 79 Exercise: Imposter Syndrome
Lecture 80 Assignment: NumPy Practice
Lecture 81 Optional: Extra NumPy resources
Section 8: Matplotlib: Plotting and Data Visualization
Lecture 82 Section Overview
Lecture 83 Matplotlib Introduction
Lecture 84 Importing And Using Matplotlib
Lecture 85 Anatomy Of A Matplotlib Figure
Lecture 86 Scatter Plot And Bar Plot
Lecture 87 Histograms And Subplots
Lecture 88 Subplots Option 2
Lecture 89 Quick Tip: Data Visualizations
Lecture 90 Plotting From Pandas DataFrames
Lecture 91 Quick Note: Regular Expressions
Lecture 92 Plotting From Pandas DataFrames 2
Lecture 93 Plotting from Pandas DataFrames 3
Lecture 94 Plotting from Pandas DataFrames 4
Lecture 95 Plotting from Pandas DataFrames 5
Lecture 96 Plotting from Pandas DataFrames 6
Lecture 97 Plotting from Pandas DataFrames 7
Lecture 98 Customizing Your Plots
Lecture 99 Customizing Your Plots 2
Lecture 100 Saving And Sharing Your Plots
Lecture 101 Assignment: Matplotlib Practice
Section 9: Scikit-learn: Creating Machine Learning Models
Lecture 102 Section Overview
Lecture 103 Scikit-learn Introduction
Lecture 104 Quick Note: Upcoming Video
Lecture 105 Refresher: What Is Machine Learning?
Lecture 106 Quick Note: Upcoming Videos
Lecture 107 Scikit-learn Cheatsheet
Lecture 108 Typical scikit-learn Workflow
Lecture 109 Optional: Debugging Warnings In Jupyter
Lecture 110 Getting Your Data Ready: Splitting Your Data
Lecture 111 Quick Tip: Clean, Transform, Reduce
Lecture 112 Getting Your Data Ready: Convert Data To Numbers
Lecture 113 Note: Update to next video (OneHotEncoder can handle NaN/None values)
Lecture 114 Getting Your Data Ready: Handling Missing Values With Pandas
Lecture 115 Extension: Feature Scaling
Lecture 116 Note: Correction in the upcoming video (splitting data)
Lecture 117 Getting Your Data Ready: Handling Missing Values With Scikit-learn
Lecture 118 NEW: Choosing The Right Model For Your Data
Lecture 119 NEW: Choosing The Right Model For Your Data 2 (Regression)
Lecture 120 Quick Note: Decision Trees
Lecture 121 Quick Tip: How ML Algorithms Work
Lecture 122 Choosing The Right Model For Your Data 3 (Classification)
Lecture 123 Fitting A Model To The Data
Lecture 124 Making Predictions With Our Model
Lecture 125 predict() vs predict_proba()
Lecture 126 NEW: Making Predictions With Our Model (Regression)
Lecture 127 NEW: Evaluating A Machine Learning Model (Score) Part 1
Lecture 128 NEW: Evaluating A Machine Learning Model (Score) Part 2
Lecture 129 Evaluating A Machine Learning Model 2 (Cross Validation)
Lecture 130 Evaluating A Classification Model 1 (Accuracy)
Lecture 131 Evaluating A Classification Model 2 (ROC Curve)
Lecture 132 Evaluating A Classification Model 3 (ROC Curve)
Lecture 133 Reading Extension: ROC Curve + AUC
Lecture 134 Evaluating A Classification Model 4 (Confusion Matrix)
Lecture 135 NEW: Evaluating A Classification Model 5 (Confusion Matrix)
Lecture 136 Evaluating A Classification Model 6 (Classification Report)
Lecture 137 NEW: Evaluating A Regression Model 1 (R2 Score)
Lecture 138 NEW: Evaluating A Regression Model 2 (MAE)
Lecture 139 NEW: Evaluating A Regression Model 3 (MSE)
Lecture 140 Machine Learning Model Evaluation
Lecture 141 NEW: Evaluating A Model With Cross Validation and Scoring Parameter
Lecture 142 NEW: Evaluating A Model With Scikit-learn Functions
Lecture 143 Improving A Machine Learning Model
Lecture 144 Tuning Hyperparameters
Lecture 145 Tuning Hyperparameters 2
Lecture 146 Tuning Hyperparameters 3
Lecture 147 Note: Metric Comparison Improvement
Lecture 148 Quick Tip: Correlation Analysis
Lecture 149 Saving And Loading A Model
Lecture 150 Saving And Loading A Model 2
Lecture 151 Putting It All Together
Lecture 152 Putting It All Together 2
Lecture 153 Scikit-Learn Practice
Section 10: Supervised Learning: Classification + Regression
Lecture 154 Milestone Projects!
Section 11: Milestone Project 1: Supervised Learning (Classification)
Lecture 155 Section Overview
Lecture 156 Project Overview
Lecture 157 Project Environment Setup
Lecture 158 Optional: Windows Project Environment Setup
Lecture 159 Step 1~4 Framework Setup
Lecture 160 Note: Code update for next video
Lecture 161 Getting Our Tools Ready
Lecture 162 Exploring Our Data
Lecture 163 Finding Patterns
Lecture 164 Finding Patterns 2
Lecture 165 Finding Patterns 3
Lecture 166 Preparing Our Data For Machine Learning
Lecture 167 Choosing The Right Models
Lecture 168 Experimenting With Machine Learning Models
Lecture 169 Tuning/Improving Our Model
Lecture 170 Tuning Hyperparameters
Lecture 171 Tuning Hyperparameters 2
Lecture 172 Tuning Hyperparameters 3
Lecture 173 Quick Note: Confusion Matrix Labels
Lecture 174 Evaluating Our Model
Lecture 175 Note: Code change in upcoming video
Lecture 176 Evaluating Our Model 2
Lecture 177 Evaluating Our Model 3
Lecture 178 Finding The Most Important Features
Lecture 179 Reviewing The Project
Section 12: Milestone Project 2: Supervised Learning (Time Series Data)
Lecture 180 Section Overview
Lecture 181 Project Overview
Lecture 182 Downloading the data for the next two projects
Lecture 183 Project Environment Setup
Lecture 184 Step 1~4 Framework Setup
Lecture 185 Exploring Our Data
Lecture 186 Exploring Our Data 2
Lecture 187 Feature Engineering
Lecture 188 Turning Data Into Numbers
Lecture 189 Filling Missing Numerical Values
Lecture 190 Filling Missing Categorical Values
Lecture 191 Fitting A Machine Learning Model
Lecture 192 Splitting Data
Lecture 193 Challenge: What's wrong with splitting data after filling it?
Lecture 194 Custom Evaluation Function
Lecture 195 Reducing Data
Lecture 196 RandomizedSearchCV
Lecture 197 Improving Hyperparameters
Lecture 198 Preproccessing Our Data
Lecture 199 Making Predictions
Lecture 200 Feature Importance
Section 13: Data Engineering
Lecture 201 Data Engineering Introduction
Lecture 202 What Is Data?
Lecture 203 What Is A Data Engineer?
Lecture 204 What Is A Data Engineer 2?
Lecture 205 What Is A Data Engineer 3?
Lecture 206 What Is A Data Engineer 4?
Lecture 207 Types Of Databases
Lecture 208 Quick Note: Upcoming Video
Lecture 209 Optional: OLTP Databases
Lecture 210 Optional: Learn SQL
Lecture 211 Hadoop, HDFS and MapReduce
Lecture 212 Apache Spark and Apache Flink
Lecture 213 Kafka and Stream Processing
Section 14: Neural Networks: Deep Learning, Transfer Learning and TensorFlow 2
Lecture 214 Section Overview
Lecture 215 Deep Learning and Unstructured Data
Lecture 216 Setting Up With Google
Lecture 217 Setting Up Google Colab
Lecture 218 Google Colab Workspace
Lecture 219 Uploading Project Data
Lecture 220 Setting Up Our Data
Lecture 221 Setting Up Our Data 2
Lecture 222 Importing TensorFlow 2
Lecture 223 Optional: TensorFlow 2.0 Default Issue
Lecture 224 Using A GPU
Lecture 225 Optional: GPU and Google Colab
Lecture 226 Optional: Reloading Colab Notebook
Lecture 227 Loading Our Data Labels
Lecture 228 Preparing The Images
Lecture 229 Turning Data Labels Into Numbers
Lecture 230 Creating Our Own Validation Set
Lecture 231 Preprocess Images
Lecture 232 Preprocess Images 2
Lecture 233 Turning Data Into Batches
Lecture 234 Turning Data Into Batches 2
Lecture 235 Visualizing Our Data
Lecture 236 Preparing Our Inputs and Outputs
Lecture 237 Optional: How machines learn and what's going on behind the scenes?
Lecture 238 Building A Deep Learning Model
Lecture 239 Building A Deep Learning Model 2
Lecture 240 Building A Deep Learning Model 3
Lecture 241 Building A Deep Learning Model 4
Lecture 242 Summarizing Our Model
Lecture 243 Evaluating Our Model
Lecture 244 Preventing Overfitting
Lecture 245 Training Your Deep Neural Network
Lecture 246 Evaluating Performance With TensorBoard
Lecture 247 Make And Transform Predictions
Lecture 248 Transform Predictions To Text
Lecture 249 Visualizing Model Predictions
Lecture 250 Visualizing And Evaluate Model Predictions 2
Lecture 251 Visualizing And Evaluate Model Predictions 3
Lecture 252 Saving And Loading A Trained Model
Lecture 253 Training Model On Full Dataset
Lecture 254 Making Predictions On Test Images
Lecture 255 Submitting Model to Kaggle
Lecture 256 Making Predictions On Our Images
Lecture 257 Finishing Dog Vision: Where to next?
Section 15: Storytelling + Communication: How To Present Your Work
Lecture 258 Section Overview
Lecture 259 Communicating Your Work
Lecture 260 Communicating With Managers
Lecture 261 Communicating With Co-Workers
Lecture 262 Weekend Project Principle
Lecture 263 Communicating With Outside World
Lecture 264 Storytelling
Lecture 265 Communicating and sharing your work: Further reading
Section 16: Career Advice + Extra Bits
Lecture 266 Endorsements On LinkedIn
Lecture 267 Quick Note: Upcoming Video
Lecture 268 What If I Don't Have Enough Experience?
Lecture 269 Learning Guideline
Lecture 270 Quick Note: Upcoming Videos
Lecture 271 JTS: Learn to Learn
Lecture 272 JTS: Start With Why
Lecture 273 Quick Note: Upcoming Videos
Lecture 274 CWD: Git + Github
Lecture 275 CWD: Git + Github 2
Lecture 276 Contributing To Open Source
Lecture 277 Contributing To Open Source 2
Lecture 278 Exercise: Contribute To Open Source
Lecture 279 Coding Challenges
Section 17: Learn Python
Lecture 280 What Is A Programming Language
Lecture 281 Python Interpreter
Lecture 282 How To Run Python Code
Lecture 283 Latest Version Of Python
Lecture 284 Our First Python Program
Lecture 285 Python 2 vs Python 3
Lecture 286 Exercise: How Does Python Work?
Lecture 287 Learning Python
Lecture 288 Python Data Types
Lecture 289 How To Succeed
Lecture 290 Numbers
Lecture 291 Math Functions
Lecture 292 DEVELOPER FUNDAMENTALS: I
Lecture 293 Operator Precedence
Lecture 294 Exercise: Operator Precedence
Lecture 295 Optional: bin() and complex
Lecture 296 Variables
Lecture 297 Expressions vs Statements
Lecture 298 Augmented Assignment Operator
Lecture 299 Strings
Lecture 300 String Concatenation
Lecture 301 Type Conversion
Lecture 302 Escape Sequences
Lecture 303 Formatted Strings
Lecture 304 String Indexes
Lecture 305 Immutability
Lecture 306 Built-In Functions + Methods
Lecture 307 Booleans
Lecture 308 Exercise: Type Conversion
Lecture 309 DEVELOPER FUNDAMENTALS: II
Lecture 310 Exercise: Password Checker
Lecture 311 Lists
Lecture 312 List Slicing
Lecture 313 Matrix
Lecture 314 List Methods
Lecture 315 List Methods 2
Lecture 316 List Methods 3
Lecture 317 Common List Patterns
Lecture 318 List Unpacking
Lecture 319 None
Lecture 320 Dictionaries
Lecture 321 DEVELOPER FUNDAMENTALS: III
Lecture 322 Dictionary Keys
Lecture 323 Dictionary Methods
Lecture 324 Dictionary Methods 2
Lecture 325 Tuples
Lecture 326 Tuples 2
Lecture 327 Sets
Lecture 328 Sets 2
Section 18: Learn Python Part 2
Lecture 329 Breaking The Flow
Lecture 330 Conditional Logic
Lecture 331 Indentation In Python
Lecture 332 Truthy vs Falsey
Lecture 333 Ternary Operator
Lecture 334 Short Circuiting
Lecture 335 Logical Operators
Lecture 336 Exercise: Logical Operators
Lecture 337 is vs ==
Lecture 338 For Loops
Lecture 339 Iterables
Lecture 340 Exercise: Tricky Counter
Lecture 341 range()
Lecture 342 enumerate()
Lecture 343 While Loops
Lecture 344 While Loops 2
Lecture 345 break, continue, pass
Lecture 346 Our First GUI
Lecture 347 DEVELOPER FUNDAMENTALS: IV
Lecture 348 Exercise: Find Duplicates
Lecture 349 Functions
Lecture 350 Parameters and Arguments
Lecture 351 Default Parameters and Keyword Arguments
Lecture 352 return
Lecture 353 Exercise: Tesla
Lecture 354 Methods vs Functions
Lecture 355 Docstrings
Lecture 356 Clean Code
Lecture 357 *args and **kwargs
Lecture 358 Exercise: Functions
Lecture 359 Scope
Lecture 360 Scope Rules
Lecture 361 global Keyword
Lecture 362 nonlocal Keyword
Lecture 363 Why Do We Need Scope?
Lecture 364 Pure Functions
Lecture 365 map()
Lecture 366 filter()
Lecture 367 zip()
Lecture 368 reduce()
Lecture 369 List Comprehensions
Lecture 370 Set Comprehensions
Lecture 371 Exercise: Comprehensions
Lecture 372 Python Exam: Testing Your Understanding
Lecture 373 Modules in Python
Lecture 374 Quick Note: Upcoming Videos
Lecture 375 Optional: PyCharm
Lecture 376 Packages in Python
Lecture 377 Different Ways To Import
Lecture 378 Next Steps
Lecture 379 Bonus Resource: Python Cheatsheet
Section 19: Extra: Learn Advanced Statistics and Mathematics for FREE!
Lecture 380 Statistics and Mathematics
Section 20: Where To Go From Here?
Lecture 381 Become An Alumni
Lecture 382 Thank You
Lecture 383 Thank You Part 2
Section 21: BONUS SECTION
Lecture 384 Special Bonus Lecture
Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science and Python,You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable,Anyone who wants to learn these topics from industry experts that don't only teach, but have actually worked in the field,You're looking for one single course to teach you about Machine learning and Data Science and get you caught up to speed with the industry,You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really "getting it",You want to learn to use Deep learning and Neural Networks with your projects,You want to add value to your own business or company you work for, by using powerful Machine Learning tools.

Homepage
https://www.udemy.com/course/complete-machine-learning-and-data-science-zero-to-mastery/

#85

Video de treinamento e tutoriais online / TTC - Queen of the Sciences: A...

Última mensagem por joomlamz - 26 de Julho de 2024, 01:44

Last updated 7/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 24 Lessons ( 12h 17m ) | Size: 10.3 GB

In the 17th century, the great scientist and mathematician Galileo Galilei noted that the book of nature "cannot be understood unless one first learns to comprehend the language and read the characters in which it is written. It is written in the language of mathematics, and its characters are triangles, circles, and other geometric figures, without which it is not humanly possible to understand a single word of it."

For at least 4,000 years of recorded history, humans have engaged in the study of mathematics. Our progress in this field is a gripping narrative, a never-ending search for hidden patterns in numbers, a philosopher's quest for the ultimate meaning of mathematical relationships, a chronicle of amazing progress in practical fields like engineering and economics, a tale of astonishing scientific discoveries, a fantastic voyage into realms of abstract beauty, and a series of fascinating personal profiles of individuals such as

Archimedes, the greatest of all Greek mathematicians, who met his death in 212 B.C. at the hands of a Roman soldier while he was engrossed in a problem

Evariste Galois, whose stormy life in 19th-century radical French politics was cut short by a duel at age 20—but not before he laid the foundations for a new branch of modern algebra called Galois theory

Srinivasa Ramanujan, an impoverished college dropout in India who sent his extraordinary equations to the famous English mathematician G. H. Hardy in 1913 and was subsequently recognized as a genius

An inquiring mind is all you need to embark on this supreme intellectual adventure in The Queen of the Sciences: A History of Mathematics, which contains 24 illuminating lectures taught by award-winning Professor of Mathematics David M. Bressoud.

The "Queen of the Sciences"
The history of mathematics concerns one of the most magnificent, surprising, and powerful of all human achievements. In the early 19th century, the noted German mathematician Carl Friedrich Gauss called mathematics the "queen of the sciences" because it was so successful at uncovering the nature of physical reality. Gauss's observation is even more accurate in today's age of quantum physics, string theory, chaos theory, information technology, and other mathematics-intensive disciplines that have transformed the way we understand and deal with the world.
The Queen of the Sciences takes you from ancient Mesopotamia—where the Pythagorean theorem was already in use more than 1,000 years before the Greek thinker Pythagoras traditionally proved it—to the Human Genome Project, which uses sophisticated mathematical techniques to decipher the 3 billion letters of the human genetic code.
Along the way, you meet a remarkable range of individuals whose love of numbers, patterns, and shapes created the grand edifice that is mathematics. These include astrologers, lawyers, a poet, a cult leader, a tax assessor, the author of the most popular textbook ever written, a high school teacher, a blind grandfather, an artist, and several prodigies who died too young.

You find the problems and ideas that preoccupied them can be stated with the utmost simplicity
Is there a method for finding all the prime numbers below a given number? (Eratosthenes, c. 200 B.C.)
The equation xn + yn = zn has no whole-number solutions where n is greater than 2. (Pierre de Fermat, 1637)
What would it mean if space is non-Euclidean; that is, if it is not flat as described by Euclid? (János Bolyai, 1832)
The second of these propositions, called Fermat's last theorem, is one of the most famous in mathematics. It was followed by this postscript in the book where Fermat jotted it down: "I have a truly marvelous demonstration, which this margin is too narrow to contain." Since Fermat never wrote out his proof, his statement served as a tantalizing challenge to succeeding generations of mathematicians.

The difficult road to a proof of Fermat's last theorem is a theme that surfaces throughout the last half of this course. Among other intriguing facts, you learn that Circle Limit III, a mathematically inspired woodcut by the Dutch artist M. C. Escher, relates directly to the technique that eventually showed the way to a solution by mathematician Andrew Wiles in 1994.

See Mathematics in Context
Professor Bressoud begins the course by defining mathematics as the study of the abstraction of patterns. Mathematics arises from patterns observed in the world, usually patterns expressed in terms of number and spatial relationships. Furthermore, it is a human endeavor found in every culture extending back as far as records go.
The Queen of the Sciences focuses on the European tradition that grew out of early mathematics in Mesopotamia, Egypt, and Greece. The first eight lectures examine these foundations and the contributions of India, China, and the Islamic world, which played important roles in the development of European mathematical achievements. For example
The earliest recorded use of zero as a placeholder was found in a Hindu temple in Cambodia constructed in A.D. 683. Zero had been used a few decades earlier by the Indian astronomer Brahmagupta not as a placeholder but as a number that could be manipulated.
An approximation for pi of 355/113 was developed in the 5th century by the Chinese astronomer Zu Chongzhi. Correct to seven decimal places, this approximation would remain the most accurate estimate for more than 1,000 years.
The first treatise on al-jabr (restoring) and al-muqabala (comparing)—the process of solving an algebraic equation—was written in A.D. 825 by the Islamic mathematician Abu Jafar al-Kwarizmi. Al-jabr eventually would become the word "algebra" and al-Kwarizmi would become the word "algorithm."
The next eight lectures show how Western Europe, beginning in the late Middle Ages, gathered existing mathematical ideas and refined them into new and powerful tools. The heart of this section is five lectures on the 17th century, when the separate threads of geometry, algebra, and trigonometry began to meld into a cohesive whole, one whose fruits included the creation of calculus by Isaac Newton and Gottfried Wilhelm Leibniz.
Calculus is another recurring theme throughout this course, making its first appearance in the method of exhaustion developed by the ancient Greeks. In the early 17th century, John Napier initiated the idea of logarithms, which added to the examples from which the general rules of calculus emerged. You discover how, in his ceaseless toying with his new invention, Napier chanced on a base that is the equivalent to the modern base of the natural logarithm used in calculus: the famous number now known as e (2.71828 ... ).
After studying the 18th-century contributions of Leonhard Euler—possibly the greatest mathematician who ever lived—you look at how art has influenced geometry and all of mathematics. You investigate mosaics from the Alhambra, prints by M. C. Escher and Albrecht Dürer, and other intriguing shapes and forms.

In the final eight lectures, you explore selected mathematical developments of the past 200 years, including
Joseph Fourier's solution in the early 1800s to the problem of modeling heat flow, which led to a powerful technique called Fourier analysis for making sense of a wide range of complex physical phenomena
Bernhard Riemann's new system of geometry in the mid-1800s, which provided a framework for the revolutionary conception of space developed by Albert Einstein in his general theory of relativity
Grigori Perelman's recent, startling solution to the Poincaré conjecture proposed by Henri Poincaré in 1904, which earned Perelman the prestigious Fields Medal (which the reclusive Russian mathematician declined)

Learn with an Experienced Teacher
Experienced in teaching mathematics to students of all levels, Professor Bressoud was a Peace Corps volunteer in the West Indies before earning his Ph.D., where he taught mathematics and science to intermediate students. In addition, he has written numerous articles on mathematics education and related issues, including four textbooks that draw heavily on the history of mathematics.
His depth of knowledge and passion for teaching mathematics—which earned him the Mathematical Association of America's Allegheny Mountain Section Distinguished Teaching Award—make your journey through the story of mathematics all the more riveting and exciting.
Mathematics has exhibited an inexhaustible power to illuminate aspects of the universe that have been cloaked in mystery. In charting the storied history of its evolution, The Queen of the Sciences not only illustrates how these mysteries were revealed but exposes, with a wealth of insight, the enormous efforts that went into deciphering our natural world.

Homepage
https://www.thegreatcourses.com/courses/queen-of-the-sciences-a-history-of-mathematics

#86

Video de treinamento e tutoriais online / Php 8.2 Crash Course With Pdo,...

Última mensagem por joomlamz - 26 de Julho de 2024, 01:43

Php 8.2 Crash Course With Pdo, Security And Payment Gateways
Published 4/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.48 GB | Duration: 19h 34m

Learn all the fundamentals of PHP with PDO, PHP security, Payment Gateway Integration and Basic admin panel setup.

What you'll learn

Learn PHP from scratch

Learn MySQL from scratch

Learn the using of PDO in PHP

Visual Studio Code Editor and plugins in that

Variables, Data types, Strings in php

Operators in php

Array, Multi-dimensional Array and Array functions

Conditions in php (if, elseif, else)

Loops (for, while and dowhile),Nested Loop

Functions in php (builtin and user-defined)

Switch case using in php

$_POST, $_GET, $_REQUEST

Session and Cookies

Using Regular Expression in PHP

File Upload and Validation, File I/O

Database Basic, Query Language

Connecting Database in PHP

Form Validation Technique

Basic CRUD Operation

Login, Registration and Forget Password System

Email verification in registration

Payment Method Integration - PayPal

Payment Method Integration - Stripe

Security Protection against SQL Injection

Security Protection against XSS

Security Protection against SQL Injection

Requirements

Basic knowledge in HTML

Basic knowledge in CSS

Basic knowledge in jаvascript

Basic knowledge in Bootstrap

Basic knowledge in Code Editor like VS Code or any

Description

In this course, I have taught the students the most popular programming language PHP from scratch using the latest version PHP 8.2. After showing all the basics of PHP using PDO, I also have shown the details of the PHP security with example, Payment Gateway Integration, Complete Authentication System, and Admin Panel Setup with mastering.If you are new to programming, you want to learn programming but do not understand from where you can start, this course is for you. I have shown everything in very details and you will enjoy the learning. Features of this course:Learning the basics of PHPRegular Expression in PHP with ExamplePagination System Building from ScratchLearning the MySQL database from scratchWorking with PHP FormsForm Validation TechniqueEmail Sending from ScratchFile Upload System and ValidationSessions and CookiesFile I/O OperationQuery LanguagesShowing different clausesSecurity - Folder Content ProtectionSecurity - Least PrivelegesSecurity - SQL InjectionSecurity - XSS AttackBuilding authentication systemRegistration System with Email ValidationLogin and Forget Password SystemReset Password SystemLogout SystemAdmin Panel HTML template download and mastering with PHPComplete Admin panel authentication system with all featuresPayment method integration with PayPalPayment method integration with Stripe

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Prerequisites

Lecture 2 Installing Xampp (Local Server)

Lecture 3 Shift between php 7 and php 8 in XAMPP (Local Server)

Lecture 4 Installing Laragon (Local Server)

Lecture 5 Shift between php 7 and php 8 in Laragon (Local Server)

Lecture 6 Install PHP 8.2 in Laragon

Lecture 7 Code Editors

Section 3: Visual Studio Code Editor

Lecture 8 VS Code Installation

Lecture 9 Opening Folders (Single and Multiple)

Lecture 10 Autosave

Lecture 11 Installing Extensions

Lecture 12 Export Settings

Lecture 13 Reset VS Code

Lecture 14 Creating and Working with Snippets

Lecture 15 Extension - Bootstrap 5 Quick Snippets

Lecture 16 Extension - Auto Rename Tag

Lecture 17 Extension - Material Icon Theme

Lecture 18 Extension - Vscode Great Icons

Lecture 19 Extension - Live Server

Lecture 20 Extension - Live Server Preview

Lecture 21 Extension - HTML CSS Support

Lecture 22 Color Scheme

Lecture 23 Working with Terminal

Lecture 24 Settings Sync

Section 4: PHP Fundamentals - A to Z

Lecture 25 What is PHP

Lecture 26 PHP Syntax

Lecture 27 Comments

Lecture 28 Variables

Lecture 29 Variable Scope

Lecture 30 Echo and Print

Lecture 31 Data Types

Lecture 32 Type Casting

Lecture 33 String - Part 1

Lecture 34 String - Part 2

Lecture 35 String - Part 3

Lecture 36 Number

Lecture 37 Math

Lecture 38 Constant

Lecture 39 Operator - Part 1

Lecture 40 Operator - Part 2

Lecture 41 Operator - Part 3

Lecture 42 Conditional Statement (if, elseif, else)

Lecture 43 Switch

Lecture 44 Loop - Part 1

Lecture 45 Loop - Part 2

Lecture 46 Nested Loop - Part 1

Lecture 47 Nested Loop - Part 2

Lecture 48 Array - Part 1

Lecture 49 Array - Part 2

Lecture 50 Array - Part 3

Lecture 51 Array Function

Lecture 52 Function

Lecture 53 Superglobals

Lecture 54 Form, $_GET, $_POST, $_REQUEST

Lecture 55 Cookie - Part 1

Lecture 56 Cookie - Part 2

Lecture 57 Session

Lecture 58 Include, Require

Lecture 59 File Upload and Validation - Part 1

Lecture 60 File Upload and Validation - Part 2

Lecture 61 File IO

Lecture 62 Form Validation

Lecture 63 Sending Email

Lecture 64 Regular Expression - Part 1

Lecture 65 Regular Expression - Part 2

Section 5: MySQL Database

Lecture 66 What is MySQL

Lecture 67 Connect MySQL with PHP

Lecture 68 Create Database and Table

Lecture 69 Inserting Data

Lecture 70 Prepared Statement

Lecture 71 Select Data

Lecture 72 Where Clause

Lecture 73 Update Data

Lecture 74 Delete Data

Lecture 75 Order By Clause

Lecture 76 LIMIT Clause

Lecture 77 JOIN Clause

Lecture 78 UNION Clause

Lecture 79 ALTER Table

Lecture 80 GROUP BY and HAVING

Section 6: PHP Security

Lecture 81 Folder Content Protection

Lecture 82 Least Privilege

Lecture 83 SQL Injection

Lecture 84 XSS Attack

Section 7: Pagination

Lecture 85 Pagination

Section 8: Authentication System

Lecture 86 Template Setup

Lecture 87 Registration

Lecture 88 Registration - Email Verification

Lecture 89 Login

Lecture 90 Forget Password

Lecture 91 Reset Password

Lecture 92 Source Codes

Section 9: Admin Panel - Template Setup

Lecture 93 Admin Panel HTML template using Bootstrap 5

Lecture 94 Converting into PHP files

Lecture 95 Login and Logout

Lecture 96 Edit Profile

Lecture 97 Forget Password

Lecture 98 Reset Password

Lecture 99 Source Codes

Section 10: Payment Method Integration

Lecture 100 PayPal - Part 1

Lecture 101 PayPal - Part 2

Lecture 102 Stripe - Part 1

Lecture 103 Stripe - Part 2

Lecture 104 Source Codes

Persons who want to learn php 8.2 from scratch,Persons who want to learn mysql from scratch,Persons who are looking for a good job in web development sector,Persons who want to have a remote job in php,Persons who want to start his freelancing career using php

#87

Video de treinamento e tutoriais online / Cypress Mastery :Software Auto...

Última mensagem por joomlamz - 26 de Julho de 2024, 01:42

Published 6/2024
Created by Akin Oladeji
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 66 Lectures ( 15h 12m ) | Size: 7.2 GB

Unlocking the Power of Cypress for Seamless Web Application Testing and Beyond

What you'll learn:
Understand the fundamentals of software automation and its importance in modern development workflows.
Gain proficiency in setting up Cypress for automated testing, including installation, configuration, and project structure.
Learn to create robust test scripts using Cypress and jаvascript, covering key concepts such as test organization, assertions, and fixtures.
Develop the skills to execute, debug, and analyze automated tests effectively, enabling continuous integration and delivery practices.

Requirements:
No prior programming experience required, familiarity with web browsing and computer usage, and access to a computer with a reliable internet connection for downloading software and completing hands-on exercises, and a willingness to learn and explore new tools and technologies in the field of software testing and automation.

Description:
Dive into the world of software automation testing with jаvascript using our comprehensive Cypress Mastery course. Whether you're a beginner or seasoned professional, this course equips you with the knowledge and skills to become a Cypress automation expert. Through a series of engaging lessons and hands-on exercises, you'll learn everything from the basics to advanced techniques of Cypress testing.Discover how to write robust test scripts using jаvascript, execute end-to-end tests with ease, and optimize your testing workflow for maximum efficiency. With practical guidance and real-world examples, you'll gain a deep understanding of Cypress's capabilities and how to leverage them effectively in your projects.From setting up Cypress and writing your first test to mastering advanced features like fixtures and custom commands, this course covers it all. By the end, you'll have the confidence and expertise to tackle any testing challenge with Cypress, propelling your career forward in the dynamic field of software automation testing. Join us on this journey to Cypress Mastery and unlock new opportunities in your career.Embark on a transformative journey in software automation testing with our Cypress Mastery course. Gain invaluable skills in Cypress testing, from setup to advanced techniques, and propel your career to new heights in the dynamic realm of software testing.

Who this course is for:
This course is ideal for beginners interested in software testing and automation, individuals looking to transition into software development roles, quality assurance professionals seeking to enhance their skills, and anyone wanting to learn Cypress and jаvascript for automated testing purposes.

Homepage
https://www.udemy.com/course/cypress-mastery-software-automation-testing-with-jаvascript/

#88

Video de treinamento e tutoriais online / Custom Fine-Tuning GPT-2 & Sta...

Última mensagem por joomlamz - 26 de Julho de 2024, 01:41

Published 6/2024
Created by Shadi Ghaith
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 17 Lectures ( 2h 15m ) | Size: 1.1 GB

Use basic PyTorch training loop to fine-tune pretrained GPT-2 & StarCoder transformers as basic ChatGPT-like assistants.

What you'll learn:
Master basic PyTorch to fine-tune GPT-2 and StarCoder 2 for chat applications.
Design and prepare custom datasets for training conversational models.
Implement training loops, manage datasets, and optimize model performance.
Utilize trained models to generate real-time, context-aware dialogues.
Apply fine-tuning techniques across multiple domains and models.

Requirements:
Basic Knowledge of Python: Students should be comfortable with Python programming basics, as the course involves scripting in Python using PyTorch.
Understanding of Machine Learning Basics: A fundamental understanding of machine learning concepts like models, datasets, and training loops is necessary.
Familiarity with PyTorch: Prior experience with PyTorch is required.
No Specialized Hardware Required: While a GPU can speed up training, it is not mandatory as the models can be trained with CPU-only setups.

Description:
Embark on a comprehensive journey into the realm of AI-driven chatbots with our detailed course focused on fine-tuning transformer models like GPT-2 and StarCoder 2 using PyTorch. This course is meticulously designed for both beginners and experienced practitioners who wish to leverage the power of advanced AI models to develop sophisticated chat assistants tailored to a variety of uses, from everyday conversational interfaces to specialized coding assistants.Throughout this course, you will gain hands-on experience with the essentials of transformer technology, starting with the basics of fine-tuning techniques and progressing through the intricate process of preparing custom datasets. You will learn to fine-tune and configure models effectively, ensuring that they can handle real-world conversational flows and engage users with contextually aware interactions. The course also covers the crucial aspects of training loop implementation, optimization of model parameters, and bringing your chatbot to life in a real-time environment.This course is ideally suited for aspiring AI developers, data scientists keen on NLP, software developers looking to integrate AI functionalities into applications, tech educators seeking to expand their academic offerings, and hobbyists passionate about the cutting-edge of technology. By the end of this course, participants will be equipped with the know-how to not only comprehend the functionalities of GPT-2 and StarCoder 2 but to also innovate and implement their own AI chat solutions, pushing the boundaries of what conversational AI can achieve.Join us to transform your understanding of artificial intelligence and take your skills in building and deploying AI-driven chatbots to the next level. Whether you are looking to enhance your professional skills or simply explore a fascinating aspect of AI, this course will provide you with the knowledge and tools necessary to succeed.

Who this course is for:
Data Scientists and Machine Learning Enthusiasts: Professionals who have foundational knowledge in machine learning and wish to expand their expertise into the realm of natural language processing and conversational AI.
Software Developers: Coders and developers who want to integrate AI-driven chat functionalities into their applications and need to understand the underlying technology to customize it effectively.
Students in Computer Science: University or college students studying computer science, artificial intelligence, or related fields who are eager to apply theoretical knowledge in real-world projects, particularly in enhancing user interaction through AI.

https://www.udemy.com/course/custom-fine-tuning-gpt-2-starcoder2-in-pytorch-for-chatbots/

https://rapidgator.net/file/404fe35bad183e69c8089fb6b62fc107
https://rapidgator.net/file/5b7c4d0116f85af04695a44bc91ac867

https://ddownload.com/hb4gvndl0m92
https://ddownload.com/5vx4brmx9qwl

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#89

Video de treinamento e tutoriais online / F# in Action, Video Edition

Última mensagem por joomlamz - 26 de Julho de 2024, 01:39

Published 5/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 9h 54m | Size: 1.58 GB

F# is engineered to make functional programming practical and accessible. This book will get you started writing your first simple, robust, and high performing functional code.
F# lets you keep your code simple even in the most complex applications—and it's the perfect language for taking your first steps in functional programming. This practical, example-driven guide shows you how to build professional applications the

F# way.
In F# in Action you will learn how to
Write performant and robust systems with succinct F# code
Model domains quickly, easily and accurately with F#'s type system
Design solutions using functional programming patterns
Ingest and process disparate data sources
Develop data-driven web applications

Unit test F# code
Effectively model data using a variety of techniques
Use scripts to rapidly explore domains
F# in Action is based on author and Microsoft F# MVP Isaac Abraham's years of experience working with developers as an F# consultant. It upgrades .NET development skills with the core principles of functional programming, and you'll soon see how F#'s functional-first approach makes it easy to learn this powerful paradigm.

About the Technology
F# is a uniquely powerful programming language. Its "light touch" approach to functional programming helps you deliver error-free code without a lot of complex math and academic theory. Simply put, F# exists to help you write better software faster, and this book will show you how.

About the Book
F# in Action teaches you to write professional quality applications in F#. For each concept, feature, and technique you'll find hands-on examples, starting with simple data transformations and progressing all the way to a full-size web app. Throughout the book, you'll take advantage of battle-tested .NET tools to take on a wide range of tasks—from data analysis to interoperability with C#.

What's Inside
Model domains with F#'s type system
Ingest and process disparate data sources
Unit test F# code
Use scripts to rapidly explore domains
About the Reader
For readers comfortable with any OO or FP language. Prior .NET knowledge not required!

About the Author
Isaac Abraham is an experienced .NET developer, trainer, and Microsoft MVP for his contributions to the .NET community. Technical editor on this book was Michael Ciccotti.
Quotes
A delightful introduction to practical programming with functions, data, and types. A journey that will help you create lasting value in your software career.
- Don Syme, Designer and Architect of the F# programming language
Learning F# will make you a better programmer. This book is the perfect way to get started.
- Scott Wlaschin, F# for fun and profit
Full of hard-earned wisdom, insights, and tips and tricks. A modern introduction to F#, based on real-life experience.
- Mark Seemann, author of Dependency Injection and Code That Fits in Your Head
A practical F# guide, written by an F# expert.
- Tomas Petricek, Charles University

https://rapidgator.net/file/1b38c210886fccb4556160395ae1a54a
https://rapidgator.net/file/0ee1940f3295fa8b53b337318a5e8101

https://ddownload.com/rlx8m19tyf1f
https://ddownload.com/dd3g0skfw78c

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#90

Video de treinamento e tutoriais online / Gamma Master Course: 2024 Gamm...

Última mensagem por joomlamz - 26 de Julho de 2024, 01:38

Publicado em 6/2024
MP4 | Vídeo: h264, 1280x720 | Áudio: AAC, 44,1 KHz, 2 canais
Idioma: Inglês | Duração: 1h 58m | Tamanho: 885MB

Dominar o Gamma em 2024: do básico ao brilho, aproveitando a IA para a criação de apresentações e criação de sites

O que aprenderá
Navegando na gama (inscrição, login, características do plano de preços)
Crie uma apresentação Gamma utilizando o AI Automation (Inteligência Artificial)
Comece a partir de um modelo ou de uma apresentação importada
Crie uma apresentação de raiz (oportunidade 100% criativa)

Requisitos
Não são necessários pré-requisitos, uma vez que este curso não requer formação prévia

Descrição
Claude Master Course: AI Chat AssistantSaiba como utilizar o Gamma com uma importante empresa de educação e formação em IA especializada na utilização de Inteligência Artificial (IA). Os nossos cursos são perfeitos para principiantes e são abrangentes, pelo que nada se perde.BENEFÍCIOS DE TRABALHAR CONNOSCO:Curso interativo completo com imensos recursos que o ajudarão a aprender a utilizar o Gamma em 1 diaTemos milhares de clientes, por isso entenda o planeamento, implementação, e necessidades de formação que coincidem com casos de utilização diferenciados de IA, incluindo a curadoria de apresentações B2B e B2CPoupe muito dinheiro utilizando ferramentas de IA sozinhoCriámos este curso para principiantes e isto garante que não há ambiguidade com o funcionamento desta ferramenta, uma vez que a formação é abrangenteSe necessitar de assistência após o curso, teremos todo o prazer em ajudá-lo com perguntas, instruções de utilização do Gamma e consultoriaESTE CURSO É PERFEITO SE QUER APRENDER COMO:Começar Gamma (inscrição e navegação)Reveja a inspiração do Gamma para despertar a criatividadeComece do zero apresentação...onde a criatividade depende 100% de siUtilize a IA para criar a sua apresentação desde o design até ao textoAproveite um modelo que corresponda ao que precisa com menos edições necessáriasCrie um site utilizando o Gamma com a ajuda da IA

Para quem é este curso
Aqueles que desejam criar uma apresentação comercial ou pessoal e gostariam de utilizar a IA para agilizar o processo

https://rapidgator.net/file/e1516ed4456904a8fbec9d9f7c72d68c

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