Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024] from Udemy

What's inside

Learning objectives

Master machine learning on python & r
Have a great intuition of many machine learning models
Make accurate predictions
Make powerful analysis
Make robust machine learning models
Create strong added value to your business
Use machine learning for personal purpose

Handle specific topics like reinforcement learning, nlp and deep learning
Handle advanced techniques like dimensionality reduction
Know which machine learning model to choose for each type of problem
Build an army of powerful machine learning models and know how to combine them to solve any problem
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Master machine learning on python & r
Have a great intuition of many machine learning models
Make accurate predictions
Make powerful analysis
Make robust machine learning models
Create strong added value to your business
Use machine learning for personal purpose
Handle specific topics like reinforcement learning, nlp and deep learning
Handle advanced techniques like dimensionality reduction
Know which machine learning model to choose for each type of problem
Build an army of powerful machine learning models and know how to combine them to solve any problem
Show more
Show less

Syllabus

Welcome to the course! Here we will help you get started in the best conditions.

Welcome Challenge!

See the power of Machine Learning in action as we create a Logistic Regression predictive model for a real-world marketing and sales use-case!

Get all the Datasets, Codes and Slides here

How to use the ML A-Z folder & Google Colab

In this video, Hadelin explains in details how to install R programming language and R studio on your computer so you can swiftly go through the rest of the course.

EXTRA: Use ChatGPT to Boost your ML Skills

-------------------- Part 1: Data Preprocessing --------------------

Welcome to Part 1 - Data Preprocessing

Understand the steps involved in Machine Learning: Data Pre-Processing (Import the data, Clean the data, Split into training & test sets, Feature Scaling), Modelling (Build the model, Train the model, Make predictions), and Evaluation (Calculate performance metrics, Make a verdict).

Understand why it's important to split the data into a training set and a test set, how they differ and what they are used for.

Two types of feature scaling: Normalization and Standardization. In the practical tutorials we focus on Standardisation and here we will discuss the intuition behind Normalisation.

Data Preprocessing in Python

Getting Started - Step 1

Getting Started - Step 2

Importing the Libraries

Importing the Dataset - Step 1

Importing the Dataset - Step 2

Importing the Dataset - Step 3

A short written summary of what needs to know in Object-oriented programming, e.g. class, object, and method.

Coding Exercise 1: Importing and Preprocessing a Dataset for Machine Learning

Taking care of Missing Data - Step 1

Taking care of Missing Data - Step 2

Coding Exercise 2: Handling Missing Data in a Dataset for Machine Learning

Encoding Categorical Data - Step 1

Encoding Categorical Data - Step 2

Encoding Categorical Data - Step 3

Coding Exercise 3: Encoding Categorical Data for Machine Learning

Splitting the dataset into the Training set and Test set - Step 1

Splitting the dataset into the Training set and Test set - Step 2

Splitting the dataset into the Training set and Test set - Step 3

Coding Exercise 4: Dataset Splitting and Feature Scaling

Feature Scaling - Step 1

Feature Scaling - Step 2

Feature Scaling - Step 3

Feature Scaling - Step 4

Coding exercise 5: Feature scaling for Machine Learning

Data Preprocessing in R

Getting Started

Dataset Description

Importing the Dataset

Taking care of Missing Data

Encoding Categorical Data

Data Preprocessing Template

Data Preprocessing Quiz

-------------------- Part 2: Regression --------------------

What is regression? 6 types of regression models are taught in this course.

Simple Linear Regression

The math behind Simple Linear Regression.

Finding the best fitting line with Ordinary Least Squares method to model the linear relationship between independent variable and dependent variable.

Simple Linear Regression in Python - Step 1a

Simple Linear Regression in Python - Step 1b

Simple Linear Regression in Python - Step 2a

Simple Linear Regression in Python - Step 2b

Simple Linear Regression in Python - Step 3

Simple Linear Regression in Python - Step 4a

Simple Linear Regression in Python - Step 4b

Simple Linear Regression in Python - Additional Lecture

Data preprocessing for Simple Linear Regression in R.

Fitting Simple Linear Regression (SLR) model to the training set using R function ‘lm’.

Predicting the test set results with the SLR model using R function ‘predict’ .

Visualizing the training set results and test set results with R package ‘ggplot2’.

Simple Linear Regression in R - Step 4b

Simple Linear Regression in R - Step 4c

Simple Linear Regression Quiz

Multiple Linear Regression

An application of Multiple Linear Regression: profit prediction for Startups.

The math behind Multiple Linear Regression: modelling the linear relationship between the independent (explanatory) variables and dependent (response) variable.

The 5 assumptions associated with a linear regression model: linearity, homoscedasticity, multivariate normality, independence (no autocorrelation), and lack of multicollinearity - plus an additional check for outliers.

Coding categorical variables in regression with dummy variables.

Dummy variable trap and how to avoid it.

Understanding the P-Value

An intuitive guide to 5 Stepwise Regression methods of building multiple linear regression models: All-in, Backward Elimination, Forward Selection, Bidirectional Elimination, and Score Comparison.

Multiple Linear Regression in Python - Step 1a

Multiple Linear Regression in Python - Step 1b

Multiple Linear Regression in Python - Step 2a

Multiple Linear Regression in Python - Step 2b

Multiple Linear Regression in Python - Step 3a

Multiple Linear Regression in Python - Step 3b

Multiple Linear Regression in Python - Step 4a

Multiple Linear Regression in Python - Step 4b

Multiple Linear Regression in Python - Backward Elimination

Multiple Linear Regression in Python - EXTRA CONTENT

Multiple Linear Regression in R - Step 1a

Multiple Linear Regression in R - Step 1b

Multiple Linear Regression in R - Step 2a

Multiple Linear Regression in R - Step 2b

Multiple Linear Regression in R - Step 3

Multiple Linear Regression in R - Backward Elimination - HOMEWORK !

Multiple Linear Regression in R - Backward Elimination - Homework Solution

Multiple Linear Regression in R - Automatic Backward Elimination

Multiple Linear Regression Quiz

Polynomial Regression

The math behind Polynomial Regression: modelling the non-linear relationship between independent variables and dependent variable.

Polynomial Regression in Python - Step 1a

Polynomial Regression in Python - Step 1b

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores Machine Learning, a field in demand by many industries

Covers both Python and R programming languages, increasing its appeal to a wider audience

Teaches advanced topics like Reinforcement Learning, NLP, and Deep Learning, catering to learners looking to specialize in these areas

Provides hands-on exercises based on real-world case studies, enhancing the practical applicability of the knowledge gained

Includes Python and R code templates that learners can download and use on their own projects, facilitating independent learning

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024] with these activities:

Review Linear Algebra and Calculus

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Strengthen your foundational knowledge in linear algebra and calculus, which are essential for understanding machine learning algorithms.

Browse courses on Linear Algebra

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Review your lecture notes and textbooks from previous courses in linear algebra and calculus.
Complete practice problems and exercises to test your understanding.
Use online resources such as Khan Academy or Brilliant to refresh your knowledge.

Introduction to Machine Learning with Python

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Review the fundamental concepts and techniques of machine learning by reading and understanding a foundational book on the topic.

View Machine and Deep Learning Algorithms and... on Amazon

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Read the first three chapters of the book to gain an overview of machine learning and its applications.
Complete the practice exercises at the end of each chapter to reinforce your understanding.
Summarize the key concepts and techniques you have learned in your own words.

Python Practice

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Brush up on your Python skills before starting the course to strengthen your understanding of the basics.

Browse courses on Python

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Review Python syntax and data structures.
Complete basic Python programming exercises.
Practice writing simple Python functions.

Ten other activities

Expand to see all activities and additional details

Show all 13 activities

Study Group

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Engage with fellow students to discuss course concepts, share insights, and support each other's learning.

Browse courses on Machine Learning

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Form a study group with other students taking the same course.
Establish regular meeting times and stick to them.
Take turns leading discussions and presenting on different topics.
Work together on practice problems and projects.

Coursera Specialization: Machine Learning

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Expand your knowledge of machine learning by completing a series of guided tutorials from a reputable educational platform.

Browse courses on Coursera

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Enroll in the Coursera Machine Learning Specialization.
Complete the video lectures and readings for each course in the specialization.
Participate in the discussion forums to ask questions and engage with other learners.

Guided Machine Learning Tutorials

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Explore additional Machine Learning tutorials beyond the course material to enhance your understanding.

Browse courses on Machine Learning

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Identify a reputable Machine Learning tutorial provider.
Choose tutorials that align with the course topics, such as regression and classification.
Follow the tutorials and complete the associated exercises.

Study Group

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Collaborate with other students to discuss course concepts, work on assignments, and prepare for exams.

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Form a study group with 2-3 other students in the course.
Meet regularly to discuss course materials, share perspectives, and work on problems together.
Use online tools such as video conferencing or shared documents to facilitate collaboration.

Kaggle Competitions

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Practice applying machine learning techniques to real-world problems and receive feedback on your performance.

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Identify a Kaggle competition that aligns with your interests and skill level.
Download the competition data and familiarize yourself with the problem statement.
Develop and train a machine learning model to solve the problem.
Submit your model to the competition and evaluate its performance.

Machine Learning Practice Problems

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Challenge yourself with practice problems to solidify your grasp of Machine Learning concepts.

Browse courses on Machine Learning

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Find Machine Learning practice problems online or in textbooks.
Attempt to solve the problems independently.
Review your solutions and identify areas for improvement.

Volunteer for Machine Learning Projects

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Gain practical experience by volunteering on Machine Learning projects or initiatives.

Browse courses on Machine Learning

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Identify organizations or individuals involved in Machine Learning projects.
Reach out to these organizations and express your interest in volunteering.
Contribute your skills and assist with Machine Learning-related activities.

Machine Learning Blog

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Enhance your understanding and communication skills by creating content that explains machine learning concepts and techniques.

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Choose a specific machine learning topic that you want to write about.
Research the topic thoroughly and gather relevant information from credible sources.
Write a clear and concise blog post that explains the topic in a way that is accessible to others.
Publish your blog post on a platform such as Medium or your own website.

Machine Learning Case Study

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Apply your Machine Learning skills by working on a personal project that addresses a real-world problem.

Browse courses on Machine Learning

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Identify a problem that can be solved using Machine Learning.
Gather and prepare the necessary data.
Develop a Machine Learning model to solve the problem.
Evaluate the performance of your model and make improvements.
Write a report or presentation summarizing your findings.

Create a Course Summary

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Organize and consolidate your course materials to enhance your understanding and retention of key concepts.

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Gather all your lecture notes, readings, assignments, and quizzes.
Create a structured outline or mind map to organize the materials.
Summarize the key points and concepts from each section of the course.
Review your course summary regularly to reinforce your learning.

Career center

Learners who complete Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024] will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying Machine Learning models. This course provides a comprehensive understanding of Machine Learning, helping you master model training, deployment, and evaluation. With hands-on experience in both Python and R, you can confidently build robust models, automate tasks, and drive innovation in this exciting field.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists predict future trends and solve complex business problems using data-driven insights. Being the cornerstone of data science, this course in Machine Learning can significantly boost your analytical skills, enabling you to create robust models, draw accurate predictions, and present powerful analyses. By comprehending the nuances of regression and various Machine Learning models, you can confidently contribute to data-driven decision-making in this in-demand role.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts transform raw data into meaningful insights, enabling businesses to make informed decisions. This course in Machine Learning equips you with the skills to analyze data, identify patterns, and extract valuable information. By mastering data preprocessing, regression techniques, and model evaluation, you can excel as a Data Analyst and contribute to problem-solving and decision-making in various industries.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians collect, analyze, interpret, and present data. This course in Machine Learning complements your statistical knowledge by introducing advanced techniques such as regression, model evaluation, and predictive analytics. By mastering these concepts, you can enhance your ability to draw meaningful conclusions from data and contribute to data-driven decision-making in various fields.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment recommendations. This course in Machine Learning strengthens your quantitative toolkit by introducing regression techniques, model building, and predictive analytics. By leveraging Python and R, you can develop robust models, forecast market trends, and enhance your performance in this competitive field.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

Research Scientists conduct scientific research and develop new technologies. This course in Machine Learning provides a solid foundation in data analysis, modeling, and predictive analytics. By mastering these techniques, you can contribute to groundbreaking research, develop innovative solutions, and advance scientific knowledge in fields such as healthcare, finance, and environmental science.

See salaries and explore the career path for Research Scientist

Data Engineer

Data Engineers design, build, and maintain data pipelines. This course in Machine Learning complements your technical skills by providing a solid understanding of data analysis, modeling, and predictive analytics. By mastering these concepts, you can contribute to the development of robust data pipelines, ensure data quality, and enable data-driven decision-making.

See salaries and explore the career path for Data Engineer

Business Analyst

Business Analysts bridge the gap between business and technology, translating business requirements into technical solutions. This course in Machine Learning empowers you with data analysis skills, enabling you to understand data patterns, build predictive models, and present data-driven insights. By leveraging Machine Learning techniques, you can add value to business decision-making and drive organizational success.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. This course in Machine Learning complements your analytical skills by introducing regression techniques, model evaluation, and predictive analytics. By mastering these concepts, you can contribute to optimizing operations, improving efficiency, and enhancing decision-making in various industries.

See salaries and explore the career path for Operations Research Analyst

Risk Manager

Risk Managers identify, assess, and mitigate risks to an organization. This course in Machine Learning enhances your understanding of statistical modeling and predictive analytics, which are essential for effective risk management. By mastering regression techniques, model evaluation, and data analysis, you can contribute to risk assessment, portfolio optimization, and decision-making in various industries.

See salaries and explore the career path for Risk Manager

Financial Analyst

Financial Analysts evaluate financial data and make investment recommendations. This course in Machine Learning provides a solid foundation in data analysis, modeling, and predictive analytics. By leveraging these techniques, you can enhance your ability to analyze financial data, forecast market trends, and make informed investment decisions.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries assess and manage financial risk. This course in Machine Learning enhances your understanding of statistical modeling and predictive analytics, which are essential skills for actuaries. By mastering regression techniques, model evaluation, and data analysis, you can contribute to risk assessment, product development, and pricing in the insurance and financial industries.

See salaries and explore the career path for Actuary

Software Engineer

Software Engineers design, develop, and maintain software systems. While not a traditional Machine Learning role, this course can enhance your skillset by providing a solid understanding of data structures, algorithms, and coding techniques. By mastering these concepts, you can contribute to the development of innovative software solutions that leverage Machine Learning.

See salaries and explore the career path for Software Engineer

Consultant

Consultants advise clients on a wide range of business issues. This course in Machine Learning may be useful for consultants who want to enhance their data analysis and problem-solving skills. By gaining a foundation in regression techniques, model evaluation, and predictive analytics, you can provide data-driven insights and recommendations to clients in various industries.

See salaries and explore the career path for Consultant

Product Manager

Product Managers oversee the development and launch of new products. While not directly related to Machine Learning, this course can enhance your understanding of data-driven decision-making. By learning about data analysis, regression techniques, and model evaluation, you can gain insights into customer behavior, market trends, and product performance.

See salaries and explore the career path for Product Manager