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Notes: Classical Machine Learning
Welcome
Welcome Note
[Bonus] QnA Lab: Interview Questions
Python
Intro to Programming Languages
Implementation: Variables, Assignment Statement, Comments, Data Types, If-else, Indentation
Implementation: Typecasting and Looping
Implementation: "Find and Replace", Strings, Functions, Modules, and Packages
Implementation: Data Structures - Lists and Strings
Implementation: Data Structures - Dict, Tuples, and Sets, (b) Operators and ShortHand Notation
Implementation: Classes, Objects, and Inheritance
Library: Pandas
Library: Matplotlib
Basics of Exploratory Data Analysis (EDA)
Library: Sklearn
Github
Git Theory and Desktop GUI (32:08)
Creating a stellar Github Profile ReadMe
Easy Documentation and Templates for ReadMe (for ML Code Submissions)
Github Cheat Sheet
Mathematics for ML
Introduction to Vectors, Scalars, Norm, Basis, and Linear Independence
Matrix as a Linear Transformation, Operations on Matrices
Solving Ax=b and its geometric interpretation
Gaussian Elimination
Norms, Special Matrices, Vector Algebra
Determinants
Eigen Decomposition, SVD, Trace, Pseudoinverse
Generalised Covariance Matrix with Geometric Meaning
Intro to Probability
Basic Calculus Formulae
Constrained Optimization
Supplementary Resources
Introduction to Unsupervised Algorithms
PCA: Procedure and Failure Cases
Gradient Descent
Theory: GD Variants, Learning Rate, Convex Functions, Smart Init, Feature Scaling, etc.
Implemention: GD, LearImplemention: GD, Learning Rate, Versions, Convex Functions, and Minimas!ning Rate, Versions, Convex Functions, and Minim
Supplementary Links
Supervised: Regression
Linear, Multi-Linear, Non-linear Regression
Implementation: Linear Regression
Supplementary Resources
Practical Considerations
Theory + Implementation: Regularisation (Ridge, Lasso, ElasticNet)
Bias and Variance of a Model, Generalisation
Statistical Measures, Feature Selection, and Dimensionality Reduction
Implemention: Feature Selection and Dimensionality Reduction
Implementation: Handling Missing Values + Categorical Values with Pandas
Theory: More Methods on Dealing with Missing Values
Supervised: Classification
Linear Discriminant Functions and SVM
SVM: Derivation, Multi-class, Regression
SVM Kernel Trick
KNN: Classification, Regression, Outliers, Curse of Dimensionality
Implementation: Imbalanced Classes
Supervised: Advanced Classical Algorithms
Expectation Measure and Information Theory
Intro to Decision Trees & Tree Construction (IG, Entropy, Gini Impurity)
Practical Considerations
Intro to Ensemble Learning
Voting, Random Classifier, Bagging, and Sampling Techniques
Random Forest and Out-of-bag testing
Intro to Boosting
Adaboost
Retrieval
Summary of Guest Session - By Abbhinav
Projects
Resources/Guidelines for the projects
MLOps Basics
Intro to MLOps & Deployment for self-projects
Detailed MLOps Lifecycle (121:16)
Theory: GD Variants, Learning Rate, Convex Functions, Smart Init, Feature Scaling, etc.
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