Supervised ML Algorithms

What you'll be able to do

• What is supervised learning and different algorithms under it?
• In depth understanding of KNN, Naive Bayes, SVM, Decision Tree etc.
• When to use which type of algorithm
• How a problem can be solved using various algorithms
• How to tune the hyperparameters
• Evaluate the model using various evaluation metrices
• Implement the algorithms in a project and solve it end to end

1. 01SECTION 1: K Nearest Neighbors13 lessons · 1 h 17 min

   1. K Nearest Neighbours IntuitionPreview5:40
   2. When can kNN not workPreview3:53
   3. Download Resources1:00
   4. Distance MeasuresPreview11:01
   5. Cosine SimilarityPreview9:50
   6. KNN for Regression Problems4:33
   7. Weighted kNN5:11
   8. Voronoi Diagram3:37
   9. Measuring Effectiveness8:50
   10. Overfitting vs Underfitting4:24
   11. K Fold Cross Validation9:21
   12. How to spot underfitting and overfitting areas graphically8:57
   13. Course Review1:00
2. 02SECTION 2: KD Tree and LSH8 lessons · 1 h 20 min

   1. Binary Search Tree (BST)11:34
   2. Constructing the Tree - Part 18:03
   3. Constructing the Tree - Part 26:43
   4. How to navigate the KD Tree14:30
   5. Drawbacks4:53
   6. Introduction to Hashing10:54
   7. LSH - Part 110:41
   8. LSH - Part 212:26
3. 03SECTION 3: Decision Trees13 lessons · 1 h 28 min

   1. Introduction to Decision Trees14:01
   2. Example of reading a Decision Tree5:57
   3. Entropy Part 1 - Understanding the Formula3:46
   4. Entropy Part 2 - Example calculation from dataset6:23
   5. Entropy Part 3 - Role in Building Decision Trees2:54
   6. Information Gain9:13
   7. Gini Impurity8:22
   8. Constructing the Decision tree9:58
   9. How to split numeric features5:34
   10. Dealing with categorical features with many possible values6:35
   11. How to avoid overfitting and Hyperparameters - Part 15:45
   12. How to avoid overfitting and Hyperparameters - Part 23:03
   13. Decision Trees for Regression Problems6:03
4. 04SECTION 4: Naive Bayes15 lessons · 2 h 14 min

   1. What is conditional probability8:08
   2. Basic ideas8:08
   3. Bayes Theorem Proof4:13
   4. Bayes Theorem Math Workout-Part 13:52
   5. Bayes Theorem Math Workout-Part 24:31
   6. Naive Bayes Algorithm18:54
   7. Naive Bayes Calculations Example11:44
   8. Naive bayes for Text Classification Problems13:20
   9. Laplace Smoothing14:06
   10. How to overcome the problem of small numbers5:52
   11. Bias Variance Tradeoff9:52
   12. Model Interpretability7:31
   13. How Imbalanced data impacts Naive Bayes Models8:57
   14. Impact of Outliers6:40
   15. How Naive Bayes handles numeric features7:46
5. 05SECTION 5: Support Vector Machines10 lessons · 1 h 10 min

   1. SVM Intuition6:02
   2. Alternate interpretation3:29
   3. SVM Part 1 - The Objective4:56
   4. SVM Part 2 - Equation of Hyperplane from Basic Geometry5:44
   5. SVM Part 3 - Why use -1 and +1 instead of 1 and 06:39
   6. SVM Part 4 - Understanding the objective formulation4:31
   7. SVM Part 5 - Soft margin classifier and slack variables12:30
   8. SVM Part 6 - Kernels and Mapping Function12:58
   9. SVM Part 7 - Primal vs Dual Form7:59
   10. SVM Part 8 - Support Vector Regression5:21
6. 06Hands on ML4 lessons · 1 h 15 min

   1. Scikit-Learn Documentation Overview4:58
   2. Scikit-Learn Hands On38:50
   3. Scikit-Learn Pipelines19:22
   4. Building a Custom Machine Learning Regressor with Scikit-Learn11:44

What you need

• Basics of Python
• Foundational knowledge of Data Science
• High school maths

Who this course is for

• Data Science Aspirants
• Data Science Professionals
• Software/Data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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