Module 1 Section 1: Welcome to Machine Learning A-Z  
Unit 1 Welcome to the course!
Unit 2 Applications of Machine Learning
Unit 3 Why Machine Learning is the Future
Unit 4 Installing R and R Studio (MAC & Windows)
Unit 5 Installing Python and Anaconda (MAC & Windows)
Module 2 Section 2: ---------------------- Part 1 - Data Processing ----------------------  
Unit 1 Welcome to Part 1 - Data Preprocessing
Unit 2 Get the dataset
Unit 3 Importing the Libraries
Unit 4 Importing the Dataset
Unit 5 Missing Data
Unit 6 Categorical Data
Unit 7 Splitting the Dataset into the Training set and Test set
Unit 8 Feature Scaling
Unit 9 And here is our Data Preprocessing Template !
Unit 10 Quiz 1 - Data Preprocessing
Module 3 Section 3: ---------------------- Part 2 - Regression ----------------------  
Unit 1 Welcome to Part 2 - Regression
Module 4 Section 4: Simple Linear Regression  
Unit 1 How to get the dataset
Unit 2 Dataset + Business Problem Description
Unit 3 Simple Linear Regression Intuition - Step 1
Unit 4 Simple Linear Regression Intuition - Step 2
Unit 5 Simple Linear Regression in Python - Step 1
Unit 6 Simple Linear Regression in Python - Step 2
Unit 7 Simple Linear Regression in Python - Step 3
Unit 8 Simple Linear Regression in Python - Step 4
Unit 9 Simple Linear Regression in R - Step 1
Unit 10 Simple Linear Regression in R - Step 2
Unit 11 Simple Linear Regression in R - Step 3
Unit 12 Simple Linear Regression in R - Step 4
Unit 13 Quiz 2 - Simple Linear Regression
Module 5 Section 5: Multiple Linear Regression  
Unit 1 How to get the dataset
Unit 2 Dataset + Business Problem Description
Unit 3 Multiple Linear Regression Intuition - Step 1
Unit 4 Multiple Linear Regression Intuition - Step 2
Unit 5 Multiple Linear Regression Intuition - Step 3
Unit 6 Multiple Linear Regression Intuition - Step 4
Unit 7 Multiple Linear Regression Intuition - Step 5
Unit 8 Multiple Linear Regression in Python - Step 1
Unit 9 Multiple Linear Regression in Python - Step 2
Unit 10 Multiple Linear Regression in Python - Step 3
Unit 11 Multiple Linear Regression in Python - Backward Elimination - Preparation
Unit 12 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !
Unit 13 Multiple Linear Regression in Python - Backward Elimination - Homework Solution
Unit 14 Multiple Linear Regression in R - Step 1
Unit 15 Multiple Linear Regression in R - Step 2
Unit 16 Multiple Linear Regression in R - Step 3
Unit 17 Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
Unit 18 Multiple Linear Regression in R - Backward Elimination - Homework Solution
Unit 19 Quiz 3 - Multiple Linear Regression
Module 6 Section 6: Polynomial Regression  
Unit 1 Polynomial Regression Intuition
Unit 2 How to get the dataset
Unit 3 Polynomial Regression in Python - Step 1
Unit 4 Polynomial Regression in Python - Step 2
Unit 5 Polynomial Regression in Python - Step 3
Unit 6 Polynomial Regression in Python - Step 4
Unit 7 Python Regression Template
Unit 8 Polynomial Regression in R - Step 1
Unit 9 Polynomial Regression in R - Step 2
Unit 10 Polynomial Regression in R - Step 3
Unit 11 Polynomial Regression in R - Step 4
Unit 12 R Regression Template
Module 7 Section 7: Support Vector Regression (SVR)  
Unit 1 How to get the dataset
Unit 2 SVR in Python
Unit 3 SVR in R
Module 8 Section 8: Decision Tree Regression  
Unit 1 Decision Tree Regression Intuition
Unit 2 How to get the dataset
Unit 3 Random Forest Regression in Python
Unit 4 Decision Tree Regression in R
Module 9 Section 9: Random Forest Regression  
Unit 1 Random Forest Regression Intuition
Unit 2 How to get the dataset
Unit 3 Random Forest Regression in Python
Unit 4 Random Forest Regression in R
Module 10 Section 10: Evaluating Regression Models Performance  
Unit 1 R-Squared Intuition
Unit 2 Adjusted R-Squared Intuition
Unit 3 Evaluating Regression Models Performance - Homework's Final Part
Unit 4 Interpreting Linear Regression Coefficients
Module 11 Section 11: ---------------------- Part 3: Classification ----------------------  
Unit 1 Welcome to Part 3 - Classification
Module 12 Section 12: Logistic Regression  
Unit 1 Logistic Regression Intuition
Unit 2 How to get the dataset
Unit 3 Logistic Regression in Python - Step 1
Unit 4 Logistic Regression in Python - Step 2
Unit 5 Logistic Regression in Python - Step 3
Unit 6 Logistic Regression in Python - Step 4
Unit 7 Logistic Regression in Python - Step 5
Unit 8 Python Classification Template
Unit 9 Logistic Regression in R - Step 1
Unit 10 Logistic Regression in R - Step 2
Unit 11 Logistic Regression in R - Step 3
Unit 12 Logistic Regression in R - Step 4
Unit 13 Logistic Regression in R - Step 5
Unit 14 R Classification Template
Unit 15 Quiz 4 – Logistic Regression
Module 13 Section 13: k-Nearest Neighbors (k-NN)  
Unit 1 K-Nearest Neighbor Intuition
Unit 2 How to get the dataset
Unit 3 K-NN in Python
Unit 4 K-NN in R
Unit 5 Quiz 5 - K-Nearest Neighbor
Module 14 Section 14: Support Vector Machine (SVM)  
Unit 1 SVM Intuition
Unit 2 How to get the dataset
Unit 3 SVM in Python
Unit 4 SVM in R
Module 15 Section 15: Kernel SVM  
Unit 1 Kernel SVM Intuition
Unit 2 Mapping to a higher dimension
Unit 3 The Kernel Trick
Unit 4 Types of Kernel Functions
Unit 5 How to get the dataset
Unit 6 Kernel SVM in Python
Unit 7 Kernel SVM in R
Module 16 Section 16: Naive Bayes  
Unit 1 Bayes Theorem
Unit 2 Naive Bayes Intuition
Unit 3 Naive Bayes Intuition (Challenge Reveal)
Unit 4 Naive Bayes Intuition (Extras)
Unit 5 How to get the dataset
Unit 6 Naive Bayes in Python
Unit 7 Naive Bayes in R
Module 17 Section 17: Decision Tree Classification  
Unit 1 Decision Tree Classification Intuition
Unit 2 How to get the dataset
Unit 3 Decision Tree Classification in Python
Unit 4 Decision Tree Classification in R
Module 18 Section 18: Random Forest Classification  
Unit 1 Random Forest Classification Intuition
Unit 2 How to get the dataset
Unit 3 Random Forest Classification in Python
Unit 4 Random Forest Classification in R
Module 19 Section 19: Evaluating Classification Models Performance  
Unit 1 False Positives & False Negatives
Unit 2 Confusion Matrix
Unit 3 Accuracy Paradox
Unit 4 CAP Curve
Unit 5 CAP Curve Analysis
Module 20 Section 20: ---------------------------- Part 4: Clustering ----------------------------  
Unit 1 Welcome to Part 4 - Clustering
Module 21 Section 21: K-Means Clustering  
Unit 1 K-Means Clustering Intuition
Unit 2 K-Means Random Initialization Trap
Unit 3 K-Means Selecting The Number Of Clusters
Unit 4 K-Means Clustering in Python
Unit 5 How to get the dataset
Unit 6 K-Means Clustering in R
Unit 7 Quiz 6 - K-Means Clustering
Module 22 Section 22: Hierarchical Clustering  
Unit 1 Hierarchical Clustering Intuition
Unit 2 Hierarchical Clustering How Dendrograms Work
Unit 3 Hierarchical Clustering Using Dendrograms
Unit 4 How to get the dataset
Unit 5 HC in Python - Step 1
Unit 6 HC in Python - Step 2
Unit 7 HC in Python - Step 3
Unit 8 HC in Python - Step 4
Unit 9 HC in Python - Step 5
Unit 10 HC in R - Step 1
Unit 11 HC in R - Step 2
Unit 12 HC in R - Step 3
Unit 13 HC in R - Step 4
Unit 14 HC in R - Step 5
Unit 15 Quiz 7 - Hierarchical Clustering
Module 23 Section 23: ---------------------------- Part 5: Association Rule Learning ----------------------------  
Unit 1 Welcome to Part 5 - Association Rule Learning
Module 24 Section 24: Apriori  
Unit 1 Apriori Intuition
Unit 2 How to get the dataset
Unit 3 Apriori in R - Step 1
Unit 4 Apriori in R - Step 2
Unit 5 Apriori in R - Step 3
Unit 6 Apriori in Python - Step 1
Unit 7 Apriori in Python - Step 2
Unit 8 Apriori in Python - Step 3
Module 25 Section 25: Eclat  
Unit 1 Eclat Intuition
Unit 2 How to get the dataset
Unit 3 Eclat in R
Module 26 Section 26: ---------------------------- Part 6: Reinforcement Learning ----------------------------  
Unit 1 Welcome to Part 6 - Reinforcement Learning
Module 27 Section 27: Upper Confidence Bound (UCB)  
Unit 1 The Multi-Armed Bandit Problem
Unit 2 Upper Confidence Bound (UCB) Intuition
Unit 3 How to get the dataset
Unit 4 Upper Confidence Bound in Python - Step 1
Unit 5 Upper Confidence Bound in Python - Step 2
Unit 6 Upper Confidence Bound in Python - Step 3
Unit 7 Upper Confidence Bound in Python - Step 4
Unit 8 Upper Confidence Bound in R - Step 1
Unit 9 Upper Confidence Bound in R - Step 2
Unit 10 Upper Confidence Bound in R - Step 3
Unit 11 Upper Confidence Bound in R - Step 4
Module 28 Section 28: Thompson Sampling  
Unit 1 Thompson Sampling Intuition
Unit 2 Algorithm Comparison: UCB vs Thompson Sampling
Unit 3 How to get the dataset
Unit 4 Thompson Sampling in Python - Step 1
Unit 5 Thompson Sampling in Python - Step 2
Unit 6 Thompson Sampling in R - Step 1
Unit 7 Thompson Sampling in R - Step 2
Module 29 Section 29: ---------------------------- Part 7: Natural Language Processing ----------------------------  
Unit 1 Welcome to Part 7 - Natural Language Processing
Unit 2 How to get the dataset
Unit 3 Natural Language Processing in Python - Step 1
Unit 4 Natural Language Processing in Python - Step 2
Unit 5 Natural Language Processing in Python - Step 3
Unit 6 Natural Language Processing in Python - Step 4
Unit 7 Natural Language Processing in Python - Step 5
Unit 8 Natural Language Processing in Python - Step 6
Unit 9 Natural Language Processing in Python - Step 7
Unit 10 Natural Language Processing in Python - Step 8
Unit 11 Natural Language Processing in Python - Step 9
Unit 12 Natural Language Processing in Python - Step 10
Unit 13 Homework Challenge
Unit 14 Natural Language Processing in R - Step 1
Unit 15 Natural Language Processing in R - Step 2
Unit 16 Natural Language Processing in R - Step 3
Unit 17 Natural Language Processing in R - Step 4
Unit 18 Natural Language Processing in R - Step 5
Unit 19 Natural Language Processing in R - Step 6
Unit 20 Natural Language Processing in R - Step 7
Unit 21 Natural Language Processing in R - Step 8
Unit 22 Natural Language Processing in R - Step 9
Unit 23 Natural Language Processing in R - Step 10
Module 30 Section 30: ----------------------------Part 8 - Deep Learning ----------------------------  
Unit 1 Welcome to Part 8 - Deep Learning
Unit 2 What is Deep Learning?
Module 31 Section 31: Artificial Neural Networks (ANN)  
Unit 1 Plan of attack
Unit 2 The Neuron
Unit 3 The Activation Function
Unit 4 How do Neural Networks work?
Unit 5 How do Neural Networks learn?
Unit 6 Gradient Descent
Unit 7 Stochastic Gradient Descent
Unit 8 Back Propagation
Unit 9 How to get the dataset
Unit 10 Business Problem Description
Unit 11 ANN in Python - Step 1 - Installing Theano, Tensorflow and Keras
Unit 12 ANN in Python - Step 2
Unit 13 ANN in Python - Step 3
Unit 14 ANN in Python - Step 4
Unit 15 ANN in Python - Step 5
Unit 16 ANN in Python - Step 6
Unit 17 ANN in Python - Step 7
Unit 18 ANN in Python - Step 8
Unit 19 ANN in Python - Step 9
Unit 20 ANN in Python - Step 10
Unit 21 ANN in R - Step 1
Unit 22 ann in R 2
Unit 23 ANN in R - Step 3
Unit 24 ANN in R - Step 4 (Last step)
Module 32 Section 32: Convolutional Neural Networks (CNN)  
Unit 1 Plan of attack
Unit 2 What are convolutional neural networks?
Unit 3 Step 1 - Convolution Operation
Unit 4 Step 1(b) - ReLU Layer
Unit 5 Step 2 - Pooling
Unit 6 Step 3 - Flattening
Unit 7 Step 4 - Full Connection
Unit 8 Summary
Unit 9 Softmax & Cross-Entropy
Unit 10 How to get the dataset
Unit 11 CNN in Python - Step 1
Unit 12 CNN in Python - Step 2
Unit 13 CNN in Python - Step 3
Unit 14 CNN in Python - Step 4
Unit 15 CNN in Python - Step 5
Unit 16 CNN in Python - Step 6
Unit 17 CNN in Python - Step 7
Unit 18 CNN in Python - Step 8
Unit 19 CNN in Python - Step 9
Unit 20 CNN in Python - Step 10
Module 33 Section 33: ---------------------------- Part 9: Dimensionality Reduction ----------------------------  
Unit 1 Welcome to Part 9 - Dimensionality Reduction
Module 34 Section 34: Principal Component Analysis (PCA)  
Unit 1 How to get the dataset
Unit 2 PCA in Python - Step 1
Unit 3 PCA in Python - Step 2
Unit 4 PCA in Python - Step 3
Unit 5 PCA in R - Step 1
Unit 6 PCA in R - Step 2
Unit 7 PCA in R - Step 3
Module 35 Section 35: Linear Discriminant Analysis (LDA)  
Unit 1 How to get the dataset
Unit 2 LDA in Python
Unit 3 LDA in R
Module 36 Section 36: Kernel PCA  
Unit 1 How to get the dataset
Unit 2 Kernel PCA in Python
Unit 3 Kernel PCA in R
Module 37 Section 37: -------------------- Part 10 - Model Selection & Boosting --------------------  
Unit 1 Welcome to Part 10 - Model Selection & Boosting
Module 38 Section 38: Model Selection  
Unit 1 How to get the dataset
Unit 2 k-Fold Cross Validation in Python
Unit 3 k-Fold Cross Validation in R
Unit 4 Grid Search in Python - Step 1
Unit 5 Grid Search in Python - Step 2
Unit 6 Grid Search in R
Module 39 Section 39 - XGBoost  
Unit 1 How to get the dataset
Unit 2 XGBoost in Python - Step 1
Unit 3 XGBoost in Python - Step 2
Unit 4 XGBoost in R
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Machine Learning

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Course Description

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 structured in a fun and exciting way, but at the same time we dive deep into Machine Learning. In this course you will learn the following algorithms:

  • Linear Regression
  • Multiple Linear Regression
  • K-Means Clustering
  • Hierarchical Clustering
  • K-Nearest Neighbour
  • Decision Trees
  • Random Forest

Moreover, the course is packed with practical exercises which are based on live 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 R and Python code templates which you can download and use on your own projects.