Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023]
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Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by a Data Scientist and a Machine Learning expert so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.
Over 900,000 students world-wide trust this course.
We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course can be completed by either doing either the Python tutorials, or R tutorials, or both – Python & R. Pick the programming language that you need for your career.
This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:
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Part 1 – Data Preprocessing
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Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
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Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
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Part 4 – Clustering: K-Means, Hierarchical Clustering
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Part 5 – Association Rule Learning: Apriori, Eclat
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Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
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Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
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Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
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Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
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Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.
Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.
And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
Deep Learning A-Z™ 2023: Neural Networks, AI & ChatGPT Bonus
ChatGPT / AI Ethics: Ethical Intelligence in an AI World
Create & Sell AI Art with Midjourney. AI Art Generation
Building Recommender Systems with Machine Learning and AI
AI-102 Microsoft Azure AI Solution Complete Exam Prep-
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Welcome Challenge! -
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Machine Learning Demo - Get Excited!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!
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Get all the Datasets, Codes and Slides here
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How to use the ML A-Z folder & Google Colab
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Installing R and R Studio (Mac, Linux & Windows)
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.
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BONUS: Use ChatGPT to Boost your ML Skills
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Welcome to Part 1 - Data Preprocessing
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The Machine Learning process
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).
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Splitting the data into a Training and Test set
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.
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Feature Scaling
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.
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Getting Started - Step 1
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Getting Started - Step 2
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Importing the Libraries
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Importing the Dataset - Step 1
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Importing the Dataset - Step 2
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Importing the Dataset - Step 3
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For Python learners, summary of Object-oriented programming: classes & objects
A short written summary of what needs to know in Object-oriented programming, e.g. class, object, and method.
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Taking care of Missing Data - Step 1
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Taking care of Missing Data - Step 2
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Encoding Categorical Data - Step 1
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Encoding Categorical Data - Step 2
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Encoding Categorical Data - Step 3
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Splitting the dataset into the Training set and Test set - Step 1
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Splitting the dataset into the Training set and Test set - Step 2
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Splitting the dataset into the Training set and Test set - Step 3
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Feature Scaling - Step 1
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Feature Scaling - Step 2
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Feature Scaling - Step 3
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Feature Scaling - Step 4
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Getting Started
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Dataset Description
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Importing the Dataset
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Taking care of Missing Data
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Encoding Categorical Data
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Splitting the dataset into the Training set and Test set - Step 1
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Splitting the dataset into the Training set and Test set - Step 2
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Feature Scaling - Step 1
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Feature Scaling - Step 2
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Data Preprocessing Template
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Data Preprocessing Quiz
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Simple Linear Regression Intuition
The math behind Simple Linear Regression.
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Ordinary Least Squares
Finding the best fitting line with Ordinary Least Squares method to model the linear relationship between independent variable and dependent variable.
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Simple Linear Regression in Python - Step 1a
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Simple Linear Regression in Python - Step 1b
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Simple Linear Regression in Python - Step 2a
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Simple Linear Regression in Python - Step 2b
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Simple Linear Regression in Python - Step 3
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Simple Linear Regression in Python - Step 4a
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Simple Linear Regression in Python - Step 4b
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Simple Linear Regression in Python - Additional Lecture
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Simple Linear Regression in R - Step 1
Data preprocessing for Simple Linear Regression in R.
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Simple Linear Regression in R - Step 2
Fitting Simple Linear Regression (SLR) model to the training set using R function ‘lm’.
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Simple Linear Regression in R - Step 3
Predicting the test set results with the SLR model using R function ‘predict’ .
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Simple Linear Regression in R - Step 4a
Visualizing the training set results and test set results with R package ‘ggplot2’.
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Simple Linear Regression in R - Step 4b
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Simple Linear Regression Quiz
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Dataset + Business Problem Description
An application of Multiple Linear Regression: profit prediction for Startups.
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Multiple Linear Regression Intuition
The math behind Multiple Linear Regression: modelling the linear relationship between the independent (explanatory) variables and dependent (response) variable.
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Assumptions of Linear Regression
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.
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Multiple Linear Regression Intuition - Step 3
Coding categorical variables in regression with dummy variables.
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Multiple Linear Regression Intuition - Step 4
Dummy variable trap and how to avoid it.
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Understanding the P-Value
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Multiple Linear Regression Intuition - Step 5
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.
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Multiple Linear Regression in Python - Step 1a
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Multiple Linear Regression in Python - Step 1b
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Multiple Linear Regression in Python - Step 2a
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Multiple Linear Regression in Python - Step 2b
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Multiple Linear Regression in Python - Step 3a
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Multiple Linear Regression in Python - Step 3b
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Multiple Linear Regression in Python - Step 4a
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Multiple Linear Regression in Python - Step 4b
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Multiple Linear Regression in Python - Backward Elimination
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Multiple Linear Regression in Python - EXTRA CONTENT
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Multiple Linear Regression in R - Step 1a
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Multiple Linear Regression in R - Step 1b
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Multiple Linear Regression in R - Step 2a
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Multiple Linear Regression in R - Step 2b
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Multiple Linear Regression in R - Step 3
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Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
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Multiple Linear Regression in R - Backward Elimination - Homework Solution
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Multiple Linear Regression in R - Automatic Backward Elimination
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Multiple Linear Regression Quiz
