Machine Learning A-Z™: Hands-On Python & R In Data Science
- Description
- Curriculum
- FAQ
- Reviews
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.
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 is fun and exciting, but 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
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some 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.
Important updates (June 2020):
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CODES ALL UP TO DATE
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DEEP LEARNING CODED IN TENSORFLOW 2.0
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TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!
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Applications of Machine LearningReal-life examples of Machine Learning applications.
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BONUS #1: Learning Paths -
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BONUS #2: ML vs. DL vs. AI - What’s the Difference?
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BONUS #3: Regression Types
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Why Machine Learning is the Future
The course introduction, the instructors, and the importance of Machine Learning.
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Important notes, tips & tricks for this course
Important notes, tips & tricks for Machine Learning A-Z course.
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This PDF resource will help you a lot!
An important PDF. It contains the whole structure of Machine Learning A-Z course and the answers to important questions.
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GET ALL THE CODES AND DATASETS HERE!
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Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder
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Installing R and R Studio (Mac, Linux & Windows)
In this video, Kirill 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: Meet your instructors
Greetings from instructors, and an SDS podcast about some machine learning concepts & an overview of popular machine learning algorithms.
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Some Additional Resources
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FAQBot!
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Your Shortcut To Becoming A Better Data Scientist!
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Make sure you have your Machine Learning A-Z folder ready
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Getting Started
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Importing the Libraries
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Importing the Dataset
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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
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Encoding Categorical Data
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Splitting the dataset into the Training set and Test set
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Feature Scaling
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Simple Linear Regression Intuition - Step 1
The math behind Simple Linear Regression.
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Simple Linear Regression Intuition - Step 2
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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Make sure you have your Machine Learning A-Z folder ready
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Simple Linear Regression in Python - Step 1
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Simple Linear Regression in Python - Step 2
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Simple Linear Regression in Python - Step 3
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Simple Linear Regression in Python - Step 4
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Simple Linear Regression in Python - BONUS
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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 4
Visualizing the training set results and test set results with R package ‘ggplot2’.
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Simple Linear Regression
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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 - Step 1
The math behind Multiple Linear Regression: modelling the linear relationship between the independent (explanatory) variables and dependent (response) variable.
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Multiple Linear Regression Intuition - Step 2
The 5 assumptions associated with a linear regression model: linearity, homoscedasticity, multivariate normality, independence of error, and lack of multicollinearity.
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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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Make sure you have your Machine Learning A-Z folder ready
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Multiple Linear Regression in Python - Step 1
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Multiple Linear Regression in Python - Step 2
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Multiple Linear Regression in Python - Step 3
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Multiple Linear Regression in Python - Step 4
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Multiple Linear Regression in Python - Backward Elimination
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Multiple Linear Regression in Python - BONUS
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Multiple Linear Regression in R - Step 1
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Multiple Linear Regression in R - Step 2
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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
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Polynomial Regression Intuition
The math behind Polynomial Regression: modelling the non-linear relationship between independent variables and dependent variable.
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Make sure you have your Machine Learning A-Z folder ready
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Polynomial Regression in Python - Step 1
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Polynomial Regression in Python - Step 2
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Polynomial Regression in Python - Step 3
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Polynomial Regression in Python - Step 4
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Polynomial Regression in R - Step 1
Data preprocessing for Polynomial Regression in R.
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Polynomial Regression in R - Step 2
Fitting Polynomial Regression model and Linear Regression model to the dataset in R.
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Polynomial Regression in R - Step 3
Visualizing Linear Repression results and Polynomial Regression results and comparing the models' performance.
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Polynomial Regression in R - Step 4
Predicting new results with Linear Regression model and Polynomial Regression model.
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R Regression Template
Template for regression modelling in R.
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SVR Intuition (Updated!)
Understanding the intuition behind Support Vector Regression (SVR) for the linear case. Concepts like epsilon-insensitive tube and slack variables are explained in this tutorial.
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Heads-up on non-linear SVR
Some info about upcoming tutorials on Support Vector Machines (SVM), Kernel functions and non-Linear Support Vector Regression (SVR)
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Make sure you have your Machine Learning A-Z folder ready
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SVR in Python - Step 1
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SVR in Python - Step 2
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SVR in Python - Step 3
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SVR in Python - Step 4
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SVR in Python - Step 5
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SVR in R
Salary prediction with Support Vector Regression using R package ‘e1071’: data preprocessing, fitting, predicting, and visualizing the SVR results.
