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23 Commits
Part 1 - Data Preprocessing
Part 1 - Data Preprocessing
 
 
Part 10 - Model Selection & Boosting
Part 10 - Model Selection & Boosting
 
 
Part 2 - Regression
Part 2 - Regression
 
 
Part 3 - Classification
Part 3 - Classification
 
 
Part 4 - Clustering
Part 4 - Clustering
 
 
Part 5 - Association Rule Learning/Section 28 - Apriori
Part 5 - Association Rule Learning/Section 28 - Apriori
 
 
Part 6 - Reinforcement Learning
Part 6 - Reinforcement Learning
 
 
Part 7 - Natural Language Processing/Section 36 - Natural Language Processing
Part 7 - Natural Language Processing/Section 36 - Natural Language Processing
 
 
Part 8 - Deep Learning
Part 8 - Deep Learning
 
 
Part 9 - Dimensionality Reduction
Part 9 - Dimensionality Reduction
 
 
README.md
README.md
 
 

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Machine Learning A-Z Udemy

Content

PART 1 - Data Preprocessing

Section 1 (Welcome)

  1. Applications of Machine Learning
  2. Why Machine Learning is the Future
  3. Important notes, tips & tricks for this course
  4. This PDF resource will help you a lot
  5. Updates on Udemy Reviews
  6. Installing Python and Anaconda (Mac, Linux & Windows)
  7. Update: Recommended Anaconda Version
  8. Installing R and R Studio (Mac, Linux & Windows)
  9. BONUS: Meet your instructors

Section 2 (Data Preprocessing)

  1. Welcome to Part 1 - Data Preprocessing
  2. Get the dataset
  3. Importing the Libraries
  4. Importing the Dataset
  5. For Python learners, summary of Object-oriented programming: classes & objects
  6. Missing Data
  7. Categorical Data
  8. WARNING - Update
  9. Splitting the Dataset into the Training set and Test set
  10. Feature Scaling
  11. And here is our Data Preprocessing Template! Quiz 1: Data Preprocessing

PART 2 - Regression

Section 3/4 (Simple Linear Regression)

  1. How to get the dataset
  2. Dataset + Business Problem Description
  3. Simple Linear Regression Intuition - Step 1
  4. Simple Linear Regression Intuition - Step 2
  5. Simple Linear Regression in Python - Step 1
  6. Simple Linear Regression in Python - Step 2
  7. Simple Linear Regression in Python - Step 3
  8. Simple Linear Regression in Python - Step 4
  9. Simple Linear Regression in R - Step 1
  10. Simple Linear Regression in R - Step 2
  11. Simple Linear Regression in R - Step 3
  12. Simple Linear Regression in R - Step 4 Quiz 2: Simple Linear Regression

Section 5 (Multiple Linear Regression)

  1. How to get the dataset
  2. Dataset + Business Problem Description
  3. Multiple Linear Regression Intuition - Step 1
  4. Multiple Linear Regression Intuition - Step 2
  5. Multiple Linear Regression Intuition - Step 3
  6. Multiple Linear Regression Intuition - Step 4
  7. Prerequisites: What is the P-Value?
  8. Multiple Linear Regression Intuition - Step 5
  9. Multiple Linear Regression in Python - Step 1
  10. Multiple Linear Regression in Python - Step 2
  11. Multiple Linear Regression in Python - Step 3
  12. Multiple Linear Regression in Python - Backward Elimination - Preparation
  13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !
  14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution
  15. Multiple Linear Regression in Python - Automatic Backward Elimination
  16. Multiple Linear Regression in R - Step 1
  17. Multiple Linear Regression in R - Step 2
  18. Multiple Linear Regression in R - Step 3
  19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
  20. Multiple Linear Regression in R - Backward Elimination - Homework Solution
  21. Multiple Linear Regression in R - Automatic Backward Elimination Quiz 3: Multiple Linear Regression

Section 6 (Polynomial Regression)

  1. Polynomial Regression Intuition
  2. How to get the dataset
  3. Polynomial Regression in Python - Step 1
  4. Polynomial Regression in Python - Step 2
  5. Polynomial Regression in Python - Step 3
  6. Polynomial Regression in Python - Step 4
  7. Python Regression Template
  8. Polynomial Regression in R - Step 1
  9. Polynomial Regression in R - Step 2
  10. Polynomial Regression in R - Step 3
  11. Polynomial Regression in R - Step 4
  12. R Regression Template

Section 7 (Support Vector Regression)

  1. How to get the dataset
  2. SVR Intuition
  3. SVR in Python
  4. SVR in R

Section 8 (Decision Tree Regression)

  1. Decision Tree Regression Intuition
  2. How to get the dataset
  3. Decision Tree Regression in Python
  4. Decision Tree Regression in R

Section 9 (Random Forest Regression)

  1. Random Forest Regression Intuition
  2. How to get the dataset
  3. Random Forest Regression in Python
  4. Random Forest Regression in R

Section 10 (Evaluating Regression Models Performance)

  1. R-Squared Intuition
  2. Adjusted R-Squared Intuition
  3. Evaluating Regression Models Performance - Homework's Final Part
  4. Interpreting Linear Regression Coefficients
  5. Conclusion of Part 2 - Regression

PART 3 - Classification

Section 11 (Logistic Regression)

  1. Logistic Regression Intuition
  2. How to get the dataset
  3. Logistic Regression in Python - Step 1
  4. Logistic Regression in Python - Step 2
  5. Logistic Regression in Python - Step 3
  6. Logistic Regression in Python - Step 4
  7. Logistic Regression in Python - Step 5
  8. Python Classification Template
  9. Logistic Regression in R - Step 1
  10. Logistic Regression in R - Step 2
  11. Logistic Regression in R - Step 3
  12. Logistic Regression in R - Step 4
  13. Logistic Regression in R - Step 5
  14. R Classification Template Quiz 4: Logistic Regression

Section 12 (K-Nearest-Neighbor(K-NN))

  1. K-Nearest Neighbor Intuition
  2. How to get the dataset
  3. K-NN in Python
  4. K-NN in R Quiz 5: K-Nearest Neighbor

Section 13 (Support Vector Machine)

  1. SVM Intuition
  2. How to get the dataset
  3. SVM in Python
  4. SVM in R

Section 13 (Kerenl-SVM)

  1. SVM Intuition
  2. How to get the dataset
  3. SVM in Python
  4. SVM in R

Section 14 (Naive Bayes)

  1. Bayes Theorem
  2. Naive Bayes Intuition
  3. Naive Bayes Intuition (Challenge Reveal)
  4. Naive Bayes Intuition (Extras)
  5. How to get the dataset
  6. Naive Bayes in Python
  7. Naive Bayes in R

Section 15 (Decision Tree Classification)

  1. Decision Tree Classification Intuition
  2. How to get the dataset
  3. Decision Tree Classification in Python
  4. Decision Tree Classification in R

Section 16 (Random Forest Classification)

  1. Random Forest Classification Intuition
  2. How to get the dataset
  3. Random Forest Classification in Python
  4. Random Forest Classification in R

Section 17 (Evaluating Classification Models Performance)

  1. False Positives & False Negatives
  2. Confusion Matrix
  3. Accuracy Paradox
  4. CAP Curve
  5. CAP Curve Analysis
  6. Conclusion of Part 3 - Classification

PART 4 - Clustering

Section 18 (K-Means Clustering)

  1. K-Means Clustering Intuition
  2. K-Means Random Initialization Trap
  3. K-Means Selecting The Number Of Clusters
  4. How to get the dataset
  5. K-Means Clustering in Python
  6. K-Means Clustering in R Quiz 6: K-Means Clustering

Section 19 (Hierarchical Clustering)

  1. Hierarchical Clustering Intuition
  2. Hierarchical Clustering How Dendrograms Work
  3. Hierarchical Clustering Using Dendrograms
  4. How to get the dataset
  5. HC in Python - Step 1
  6. HC in Python - Step 2
  7. HC in Python - Step 3
  8. HC in Python - Step 4
  9. HC in Python - Step 5
  10. HC in R - Step 1
  11. HC in R - Step 2
  12. HC in R - Step 3
  13. HC in R - Step 4
  14. HC in R - Step 5 Quiz 7: Hierarchical Clustering
  15. Conclusion of Part 4 - Clustering

PART 5 - Assosiation Rule Learning [CURRENT]

Section 20 (Apriori)

  1. Apriori Intuition
  2. How to get the dataset
  3. Apriori in R - Step 1
  4. Apriori in R - Step 2
  5. Apriori in R - Step 3
  6. Apriori in Python - Step 1
  7. Apriori in Python - Step 2
  8. Apriori in Python - Step 3

Section 21 (Eclat)

  1. Eclat Intuition
  2. How to get the dataset
  3. Eclat in R

PART 6 - Reinforsment Learning

Section 22 (Upper Confidence Bound (UCB))

  1. The Multi-Armed Bandit Problem
  2. Upper Confidence Bound (UCB) Intuition
  3. How to get the dataset
  4. Upper Confidence Bound in Python - Step 1
  5. Upper Confidence Bound in Python - Step 2
  6. Upper Confidence Bound in Python - Step 3
  7. Upper Confidence Bound in Python - Step 4
  8. Upper Confidence Bound in R - Step 1
  9. Upper Confidence Bound in R - Step 2
  10. Upper Confidence Bound in R - Step 3
  11. Upper Confidence Bound in R - Step 4

Section 23 (Thompson Sampling)

  1. Thompson Sampling Intuition
  2. Algorithm Comparison: UCB vs Thompson Sampling
  3. How to get the dataset
  4. Thompson Sampling in Python - Step 1
  5. Thompson Sampling in Python - Step 2
  6. Thompson Sampling in R - Step 1
  7. Thompson Sampling in R - Step 2

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