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AlphaML: Classical Machine Learning
Welcome!
Session 1 - Welcome: Structure, Doubts Form, Schedule, Gamification (29:12)
Session 2 - Platform Walkthrough (14:38)
Session 3 - Bonus: Using ChatGPT for Learning
Orientation [Live - 6th May] (45:16)
Doubts Form
Schedule
Gamification
Live Leaderboard
Milestone 1 (a) - Introduction to Python
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 - Quiz 1
Quiz 1 Solutions (10:37)
Day 5 - "Find and Replace", Strings, Functions, Modules, and Packages (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 - Quiz 2 (Coding Assignment) (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!
Day 1 - Git Theory and Desktop GUI (53:50)
Day 2 - Creating a stellar Github Profile ReadMe (57:13)
Day 2 - Quiz 3: Submit Your GitHub ReadMe Profiles
[Advanced/Optional] Git via Code/Command Line - Installation, Account Creation, Example Run-through
[Advanced/Optional] Github Cheat Sheet
Milestone 2 - Math for ML
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 - Basic Calculus Formulae (25:07)
Day 7 - Quiz 4
Supplementary Resources
Milestone 3(a) - Regression
Supplementary Resources
Day 1 - Linear, Multi-Linear, Non-linear Regression
Day 2 - Introduction to Sklearn
Day 3 - Let's code it up! (33:41)
Day 4 - Easy Documentation and Templates for ReadMe (for ML Code Submissions) (50:45)
Day 5 - Quiz 5
Milestone 3(b) - Practical Considerations
Day 1 - Regularisation (Ridge, Lasso, ElasticNet) - Theory + Implementation
Day 2 (a) - Bias and Variance of a Model, Generalisation (36:51)
Day 2 (b) - Quiz 6
Day 3 - Statistical Measures, Feature Selection, and Dimensionality Reduction (59:17)
Day 4 - Implementing Feature Selection and Dimensionality Reduction (85:25)
Day 5 - Quiz 7
Milestone 4 - Hackathon
Day 1 - Intro to Pandas (61:13)
Day 2 - Handling Missing Values + Categorical Values with Pandas (55:35)
Day 3 - Doubts + EDA by Kishori (115:21)
Day 4 - Matplotlib session by Kishori (58:57)
Day 5 - Hackathon and Kaggle Walkthrough (63:00)
Submit Your Hackathon Notebooks!
[May Cohort] Post-Hackathon Review (Stroke Prediction) (111:37)
[Feb Cohort] Post-Hackathon Review (Traffic Volume Prediction) (96:05)
Milestone 5(a) - Learning Algorithms
Day 1-3: All Notes
Day 1 - Building Gradient Descent (Theory) (62:56)
Day 2 - Implementing GD, Learning Rate, Versions, Convex Functions, and Minimas! (56:54)
Day 3 - GD: Adaptive Learning Rate, Smart Init, Feature Scaling (20:49)
Supplementary Links
Milestone 5(b) - Classification
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 - Quiz 8
Milestone 6 - Projects
30 June - Project Discussion Call (58:21)
Resources/Guidelines for the projects
Sample Slides for Demo Day
Demo Day Submissions
[Feb Cohort] Demo Day (89:01)
Milestone 7 - Unsupervised Algorithms
Day 1 - Eigen Decomposition, SVD, Trace, Pseudoinverse (52:32)
Day 2 - Generalised Covariance Matrix with Geometric Meaning (11:50)
Day 3 - PCA: Procedure and Failure Cases (31:07)
Milestone 8 - More Learning Paradigms
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)
Bonus - Intro to Neural Networks
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)
Crash Sessions
Milestone 1 - Python and Github (127:06)
Milestone 2 - Math (137:31)
Milestone 5, 7, 8 - DT, KNN, Ensemble, PCA, Gradient Descent (51:51)
Support Sessions
1.1 - 12 May - Weekly Checkup + Q&A (33:33)
1.2 - 3 Mar - Intro to Python, Standards + Q&A (79:24)
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
1.1 - Image & Text Retrieval by Kartik Dutta (Cisco Systems) (51:19)
1.2 - Summary of Kartik's Session - By Abbhinav (24:31)
2.1 - Intro to MLOps & Deployment for self-projects, by Zohair (92:54)
Bonus - Advanced Topics (LA, Advanced Prob & Naive Bayes)
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
SQL & PL/SQL Summary
10X Practitioner Bootcamp - Recordings and Giveaways
Session 1 (69:45)
Session 2 (101:30)
Bonus 1 - Math for ML
Bonus 2 - Top 10 Interview Topics
Bonus 3 - Top 3 Tools to save you 10+ hours
Bonus 4 - 3 Effective Methods of Prompting with ChatGPT
Day 1 - Introduction to Vectors, Scalars, Norm, Basis, and Linear Independence
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