Learn the foundations of Machine Learning in this experiential 2-month long workshop!
Curriculum
Welcome!
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Gamification
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Milestone 1 (a) - Introduction to Python
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- [Bonus] QnA Lab: Interview Questions
- Day 1 - Intro to Programming Languages and How to Use this series (48:31)
- Day 2 - Variables, Assignment Statement, Comments, Data Types, If-else, Indentation (48:13)
- Day 3 - Typecasting and Looping (51:10)
- Day 4: Experiential Unit 1 - Let's revise!
- Quiz 1 Solutions (10:37)
- Day 5: Experiential Unit 2 - "Find and Replace" and more! (59:25)
- Day 6 - Data Structures: Lists and Strings (48:35)
- Day 7 - (a) Data Structures: Dict, Tuples, and Sets, (b) Operators and ShortHand Notation (44:46)
- Day 8: Experiential Unit 3 - Let's build! (Quiz 2) (8:06)
- Quiz 2 Solutions (21:47)
- [Optional & Advanced] - Classes, Objects, and Inheritance (47:37)
- [Supplementary] Summary (71:37)
Milestone 1 (b) - Position Yourself with Github!
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Milestone 2 - Math for ML
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- Day 1 - Introduction to Vectors, Scalars, Norm, Basis, and Linear Independence
- Day 2 - Matrix as a Linear Transformation, Operations on Matrices (56:11)
- Day 3 - Solving Ax=b and its geometric interpretation (59:08)
- Day 4 - Norms, Special Matrices, Vector Algebra (26:05)
- Day 5 - Determinants
- Day 6 - Intro to Probability
- Day 7 (a) - Basic Calculus Formulae (25:07)
- Day 7(b): Experiential Unit 4 - Are you sure you've picked up these key concepts?
- Supplementary Resources
Milestone 3(a) - Regression
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Milestone 3(b) - Practical Considerations
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- Day 1 - Regularisation (Ridge, Lasso, ElasticNet) - Theory + Implementation
- Day 2 (a) - Bias and Variance of a Model, Generalisation (36:51)
- Day 2 (b): Experiential Unit 6 - Have you noted these finer points?
- Day 3 - Statistical Measures, Feature Selection, and Dimensionality Reduction (59:17)
- Day 4 - Implementing Feature Selection and Dimensionality Reduction (85:25)
- Day 5: Experiential Unit 7 - Select those features! (Quiz 7)
Milestone 4 - Hackathon
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Milestone 5(a) - Learning Algorithms
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Milestone 5(b) - Classification
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- Day 1 - Intro to Classification [Recap: Bootcamp Day 1] (69:44)
- Day 2 - Linear Discriminant Functions and SVM (43:53)
- Day 3 - Constrained Optimization (27:40)
- Day 4 - SVM: Derivation, Multi-class, Regression (51:34)
- Day 5 - SVM Kernel Trick (27:32)
- Day 6 - KNN: Classification, Regression, Outliers, Curse of Dimensionality (48:54)
- Day 7 - Imbalanced Classes (36:59)
- Day 8: Experiential Unit 9 - Let's put the sessions to test!
Milestone 6 - Projects
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Milestone 7 - Unsupervised Algorithms
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Milestone 8 - More Learning Paradigms
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- Day 1 - Expectation Measure and Information Theory (79:00)
- Day 2 - Intro to Decision Trees & Tree Construction (IG, Entropy, Gini Impurity) (43:08)
- Day 3 (a) - Practical Considerations (25:32)
- Day 3 (b) - Intro to Ensemble Learning (26:39)
- Day 4 - Voting, Random Classifier, Bagging, and Sampling Techniques (52:28)
- Day 5 (a) - Random Forest and Out-of-bag testing (15:36)
- Day 5 (b) - Intro to Boosting (17:45)
- Day 5 (c) - Adaboost (32:27)
Cases
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Crash Sessions
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Bonus - Intro to Neural Networks
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- Intro to Tensorflow (31:34)
- Project 1 - Episode 1: Intro to Image Segmentation (Data Understanding) (52:52)
- Project 1 - Episode 2: Data Pre-processing on Image Segmentation (30:56)
- Project 1 - Episode 3: Building a U-net, training, and evaluation! (63:23)
- Project 2 - Episode 1: Classification on Cifar 10 (41:44)
- Project 2 - Episode 2: Classification on Cifar 10 (40:38)
Support Sessions
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- 1.1 - 12 May - Weekly Checkup + Q&A (33:33)
- 1.2 - 3 Mar - Intro to Python, Standards + Q&A (79:24)
- 1.3 - 30 Mar 2025 - Python, Logical Thinking, Coding Standards (59:13)
- 2.1 - 17 Mar - Math Q&A (35:23)
- 2.2 - 19 Nov - Span of Vectors and Motivation for Math in ML (32:06)
- 3.1 - 3 June - Regression + Intros (48:15)
- 4.1 - 31 Mar - Minima, Overfitting, Establishing Baselines, and Practicality (91:39)
- 4.2 - 9 June - Hackathon Prerequisites (30:12)
Guest Talks
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Bonus - Advanced Topics (LA, Advanced Prob & Naive Bayes)
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- 1 - More Methods on Dealing with Missing Values (33:50)
- 2.1 - Introduction to Sets and Probability (42:00)
- 2.2 - Conditional Probability and Bayes Rule (35:38)
- 2.3 - PDF, PMF, Special Distributions, and more! (57:49)
- 2.4 - Naive Bayes Classifier (61:55)
- 3.1 - LU Factorisation and Gaussian Elimination
- 3.2 - Supplementary Material for Gaussian Elimination
- 3.3 - Solving Old Quiz + Elimination on Rectangular Matrix
- 3.4 - Gaussian Elimination Notes
Bonus - SQL Flash Cards
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10X Practitioner Bootcamp - Recordings and Giveaways
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