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 ----------------------
There are no units in this module.
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: Regularization Methods
There are no units in this module.
Module 12 Section 12: Part Recap
There are no units in this module.
Module 13 Section 13: ---------------------- Part 3: Classification ----------------------
There are no units in this module.
Module 14 Section 14: 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 15 Section 15: 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 16 Section 16: 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 17 Section 17: 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 18 Section 18: 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 19 Section 19: 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 20 Section 20: 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 21 Section 21: 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 22 Section 22: Part Recap
There are no units in this module.
Module 23 Section 23: ---------------------------- Part 4: Clustering ----------------------------
There are no units in this module.
Module 24 Section 24: 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 25 Section 25: 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 26 Section 26: Part Recap
There are no units in this module.
Module 27 Section 27: ---------------------------- Part 5: Association Rule Learning ----------------------------
There are no units in this module.
Module 28 Section 28: 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 29 Section 29: Eclat
Unit 1 Eclat Intuition
Unit 2 How to get the dataset
Unit 3 Eclat in R
Module 30 Section 30 - Part Recap
There are no units in this module.
Module 31 Section 31: ---------------------------- Part 6: Reinforcement Learning ----------------------------
There are no units in this module.
Module 32 Section 32: 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 33 Section 33: 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 34 Section 34: Part Recap
There are no units in this module.
Module 35 Section 35: ---------------------------- Part 7: Natural Language Processing ----------------------------
There are no units in this module.
Module 36 Section 36: Natural Language Processing
Unit 1 Natural Language Processing in Python - Step 1
Unit 2 Natural Language Processing in Python - Step 2
Unit 3 Natural Language Processing in Python - Step 3
Unit 4 Natural Language Processing in Python - Step 4
Unit 5 Natural Language Processing in Python - Step 5
Unit 6 Natural Language Processing in Python - Step 6
Unit 7 Natural Language Processing in Python - Step 7
Unit 8 Natural Language Processing in Python - Step 8
Unit 9 Natural Language Processing in Python - Step 9
Unit 10 Natural Language Processing in Python - Step 10
Unit 11 Natural Language Processing in R - Step 1
Unit 12 Natural Language Processing in R - Step 2
Unit 13 Natural Language Processing in R - Step 3
Unit 14 Natural Language Processing in R - Step 4
Unit 15 Natural Language Processing in R - Step 5
Unit 16 Natural Language Processing in R - Step 6
Unit 17 Natural Language Processing in R - Step 7
Unit 18 Natural Language Processing in R - Step 8
Unit 19 Natural Language Processing in R - Step 9
Unit 20 Natural Language Processing in R - Step 10
Module 37 Section 37: Part Recap
There are no units in this module.
Module 38 Section 38: ----------------------------Part 8 - Deep Learning ----------------------------
Unit 1 What is Deep Learning?
Module 39 Section 39: 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
Module 40 Section 40: Convolutional Neural Networks (CNN)
Unit 1 What are convolutional neural networks?
Unit 2 Step 1 - Convolution Operation
Unit 3 Step 1(b) - ReLU Layer
Unit 4 Step 2 - Pooling
Unit 5 Step 3 - Flattening
Unit 6 Step 4 - Full Connection
Unit 7 Summary
Unit 8 Softmax & Cross-Entropy
Unit 9 How to get the dataset
Unit 10 CNN in Python - Step 1
Unit 11 CNN in Python - Step 2
Unit 12 CNN in Python - Step 3
Unit 13 CNN in Python - Step 4
Unit 14 CNN in Python - Step 5
Unit 15 CNN in Python - Step 6
Unit 16 CNN in Python - Step 7
Unit 17 CNN in Python - Step 8
Unit 18 CNN in Python - Step 9
Unit 19 CNN in Python - Step 10
Module 41 Section 41: Part Recap
There are no units in this module.
Module 42 Section 42: ---------------------------- Part 9: Dimensionality Reduction ----------------------------
There are no units in this module.
Module 43 Section 43: 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 44 Section 44: Linear Discriminant Analysis (LDA)
Unit 1 How to get the dataset
Unit 2 LDA in Python
Unit 3 LDA in R
Module 45 Section 45: Kernel PCA
Unit 1 How to get the dataset
Unit 2 Kernel PCA in Python
Unit 3 Kernel PCA in R
Module 46 Section 46: Part Recap
There are no units in this module.
Module 47 Section 47: -------------------- Part 10 - Model Selection & Boosting --------------------
There are no units in this module.
Module 48 Section 48: 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 49 Section 49 - 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.

  • Rakesh

    Curious about the course

  • If possible, it would be nice if you can include XGBoost and Deep learning in this course, or make it a separate courses.

    Thank you.

  • modifyme

    Hello, I know it’s still early but there are no instructions or link to get the datasets

  • 曾思瑋

    Hi Kirill,

    May I access this course on Udemy?

    • Hi Tony,
      Sure you can, but you will need to pay in order to get the course on Udemy, as usual.

  • Isaac

    Hi Kirill,

    There are also no links to get the Excel files or materials mentioned in the course “DATA DRIVEN MARKETING.” As there is no discussion board for the course, I have to leave the message here.

    Best regards,

  • Support required:

    There are at least 2 videos did not match the title:

    – Machine Learning course > Section 2 > Categorical Data > Video is wrong
    – Multiple Linear Regression in Python – Step 1 – Video is wrong, it is showing R

    Moreover, there is no support/contact us page in this site. Maybe you would like to add one.

    Thank you.

    • Martin

      Hello Ellery,

      Sorry for the inconvenience. We have fixed these 2 lectures.
      For now, you can contact us at [email protected]

      Regards,

      SuperDataScience Support team

  • Goutham Nara

    i Kirill,
    In this course i’m unable to open the video in Module 6 >> Section 8(POLYNOMIAL REGRESSION IN R – STEP 2).
    Thanks

    • Martin

      Hello Goutham,
      Sorry for that! We just fixed this issue, you can check the Section again.
      Let us know if you have any doubt.

      SuperDataScience Support team