Python Programming
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Resources
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Why Python for DS?
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Python Installation and Setup - Pre-requisites
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Miniconda Installation
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Module, Script, Package and Library
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Virtual Environments
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Python, IPython Shell
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Introduction to Jupyter
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Keywords and Identifiers
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Variables and Data Types
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Python Standard IO
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Executing Python Script using Terminal
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Operators
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Strings
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Strings - Indexing, Formatting
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Control Flow - If else
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Control Flow - While loop, Break and Continue
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Control Flow - for loop
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Lists
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Lists - Indexing and Slicing
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Tuples
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Sets
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Dictionary
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Dictionary and List Comprehensions
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Introduction to Functions
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Positional and Keyword arguments
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*args, **kwargs
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Lambda Functions
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Higher Order Functions
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Namespaces, Scope
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Enclosing Scope and LEGB Rule
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Decorators
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Intro. to Object Oriented Programming
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Classes and Objects
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Methods
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Inheritance
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Exception Handling
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Reading and Writing .txt files
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Reading and Writing .csv files
SQL
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Resources
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Introduction to Databases and SQL
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Installing MySQL
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DDL - CREATE, ALTER, DROP
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SQL DataTypes
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Setting up the data
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Data Retrieval - SELECT, WHERE, COUNT, DISTINCT, LIKE
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Data Retrieval - ORDER BY, LIMIT, OFFSET
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Get Summary Info. using GROUP BY
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GROUP BY - HAVING Clause
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Creating additional Columns
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Intro. to Joins
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JOINS - INNER, LEFT, RIGHT
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JOINS - OUTER
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Sub Queries, Correlated Sub Queries
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Window Functions - RANK, ROW_NUM, NTILE, LEAD, LAG
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DML - INSERT, DELETE, UPDATE
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DCL - GRANT, REVOKE
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Python - Reading the data from MySQL Table
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Python - Writing data to MySQL Table
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Python - Inserting Multiple records at once to MySQL Table
Python for Data Science : NumPy
Python for Data Science : Pandas
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Resources
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Introduction to Pandas
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DataFrames
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Series
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DataFrames - Indexing
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DataFrame - loc and iloc Operators
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DataFrame - Comparsion and Filtering
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DataFrame - Insert, Concatenate and Delete
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DataFrame - Merging
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DataFrame - apply, applymap and map
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Groupby
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Sorting and Ranking
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Reading and Writing Data Files
Data Visualization : Matplotlib and Seaborn
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Resources
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Introduction to Data Visualization
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Understanding Matplotlib Object Hierarchy
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Adding Color, Line Style, Markers
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Labels, Ticks and Legends to Plots
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Line Plots
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Histogram
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Bar Plots
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Stacked Bar Chart & Grouped Bar Chart
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Scatter Plots
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Box and Violin Plots
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Seaborn - Distribution plot, Kernel Density Estimate (KDE)
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Seaborn - Relational Plot, Joint Plot
Probability and Statistics
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Resources
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Intro. to Statistics
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Types of Data, Sample and Population
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Estimates of Location
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Estimates of Location - Coding
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Estimates of Variability
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Coefficient of Variation
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Descriptive Statistics - Coding
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Intro. to Probability, Random Experiment & Random Variable
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Calculating Probability
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Conditional Probability
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Bayes Theorem
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Bayes Theorem Problem
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Discrete RV, Probability Mass Function (PMF)
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Bernoulli Distribution
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Bernoulli Distribution and PMF using Python
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Binomial Distribution
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Geometric, Hyper Geometric Distribution
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Continuous RV, Probability Density Function (PDF)
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Cumulative Distribution Function (CDF)
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Gaussian Distribution
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Standard Normal Distribution, Z Score
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Normal Distribution - Coding
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Normal approximation to Binomial
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Log Normal Distribution
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Law of Large Numbers
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Central Limit Theorem (CLT)
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Verifying CLT
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Intro. to Confidence Intervals
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Confidence Intervals : Margin of Error
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Confidence Intervals : t-Distribution
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Hypothesis Testing
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Z Test
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One sample and Two sample t-test
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t-test - Implementation
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Paired t-test
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Chi Square Test
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Chi Square Test - Implementation
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Covariance
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Pearson Correlation
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Spearman Rank Correlation
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Kendall's Tau Correlation
Exploratory Data Analysis
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Resources
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Machine Learning Life Cycle
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Predictive Modeling Steps
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EDA Steps
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Variable Types
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Variable Identification
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Categorical Encoding - Label, Ordinal Encoding
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Categorical Encoding - One Hot Encoding
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Categorical Encoding - Frequency Encoding
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Missing Value Identification
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Univariate Analysis - Descriptive Statistics
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Univariate Analysis - Data Profiling
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What are Outliers?
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Impact of Outliers - Why are they bad?
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Identifying Outliers - Box Plot approach
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Identifying Outliers - Z Score method
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Identifying Outliers - Modified Z Score method
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Outlier Treatment - Ways to handle Outliers
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Multivariate Outlier Identification
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Multivariate Outlier Identification - Implementation
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Need for Scaling
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Standardization and Normalization of data
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Intro. to Bivariate Analysis
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Continuous-Continuous
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Categorical-Categorical : Hypothesis Testing
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Categorical-Categorical : Visualizations
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Categorical-Continuous
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Quantile-Quantile Plot (QQ Plot)
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Kolmogorov Smirnov Test (KS Test)
Linear Algebra
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Resources
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Introduction to Linear Algebra
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Vector Operations
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Vector Dot Product
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Projection of a Vector
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Basis, Span and Linear Dependence
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System of Linear equations
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Solving System of Linear equations
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Types of Matrices
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Linear Transformations
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Eigen Vectors, Eigen Values
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Eigen Decomposition
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Deriving Eigen Vectors and Values using Python
K Nearest Neighbors
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Resources
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KNN - Intuition
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Failure cases of KNN
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Distance Measures - Euclidean, Manhattan, Minkowski
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Distance Measures - Hamming distance
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Cosine Similarity and Cosine Distance
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KNN Implementation
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KNN - Breaking a tie
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Decision Regions
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Overfitting versus Underfitting
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KNN - Choosing an Optimal value for K
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Need for Cross Validation
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Holdout Validation, Stratified Holdout Validation
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Stratified Holdout Validation - Data Partitioning
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K-Fold Cross Validation, LOOCV
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K-Fold Cross Validation Implementation
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KNN for Regression Problems
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Weighted KNN
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Curse of Dimensionality
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Bias Variance Tradeoff
Performance Measurement of Classification Models
Linear Regression
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Resources
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Intro. to Linear Regression
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Baseline model and SSE
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Least Squares Method
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Evaluation Metrics for Regression model
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Linear Regression Output Interpretation
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Multiple Linear Regression and Problems
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Assumptions of Linear Regression
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Linear Regression Implementation - Problem and Data Pre-processing
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Linear Regression Implementation - Linearity, Multi-Collinearity
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Linear Regression Implementation - Data Transformations
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Linear Regression Implementation - Creating model
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Linear Regression Implementation - Model Evaluation
Solving Optimization Problems
Constrained Optimization, Ridge and Lasso Regression
Logistic Regression
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Resources
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Line, Plane and Hyper Plane
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Distance of a point from Plane
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Logistic Regression - Geometric Intuition
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Sigmoid Function
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Mathematical Formulation of Objective Function
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Objective Function using MLE
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Regularization - Logistic Regression
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Parameters and Hyper-Parameters
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Grid Search CV and Random Search CV
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Why and when Feature Scaling is required
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Implementation - Data Pre-processing
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Implementation - Missing value Imputation
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Implementation - Categorical Feature Encoding
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Implementation - Model Building
Support Vector Machines (SVM)
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Resources
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SVM - Geometric Intuition
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Mathematical Formulation of Objective Function
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Hard Margin SVM
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SVM Soft Margin
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Hinge Loss
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SVM Dual Formulation
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Kernel Trick
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RBF Kernel
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SVM Kernels, Decision Boundaries
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SVM Decision Boundaries with change in hyper parameters
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SVM Implementation - Grid Search CV
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SVM Implementation - Random Search CV
Principal Component Analysis (PCA)
Decision Trees
Ensemble Models
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Resources
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Intro. to Ensemble Learning
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Basic Ensemble Implementation for Classification problem
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Basic Ensemble Implementation for Regression problem
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Why Ensemble models work well?
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Bias Variance Trade-off (Revisited)
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Generalization Error, Bias Variance Decomposition
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Bootstrap Aggregation (Bagging) Intuition
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Intro. to Random Forest
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Random Forest model - Hyper Parameter Tuning
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Random Forest Implementation
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Extremely Randomized Trees
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Boosting Intuition
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Pseudo Residual Loss
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Gradient Boosting
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Gradient Boosting - Regularization by Shrinkage
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GBM Implementation
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Intro. to XGBoost
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XGBoost Implementation
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XGBoost CV Implementation
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AdaBoost Math
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AdaBoost In-depth
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AdaBoost Implementation
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Model Stacking
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Model Stacking Implementation
Project : Microsoft Malware Detection
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Resources
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Problem Description
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Dataset Overview
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EDA - Data Pre-processing Part I
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EDA - Data Pre-processing Part II
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EDA - Data Profiling
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EDA - Hypothesis Testing
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Feature Engineering
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Feature Encoding Approaches
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Applying Feature Encoding
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Data Pre-processing for ML Model building
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Creating a Decision Tree Model
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Decision Tree - Model Evaluation
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Creating a Random Forest Model
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Creating XGBoost Model
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Model Performance Assessment - Cumulative Gains Chart
Project : Vesta Fraud Detection
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Resources
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Problem Description
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Data Overview
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EDA - Data Pre-processing
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EDA - Data Profiling
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EDA - Hypothesis Testing
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Feature Engineering
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Feature Encoding Approaches
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Categorical Feature Encoding
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Feature Selection Methods, Data Partitioning
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Creating Random Forest Model
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Cost Sensitive Learning using Class Weights
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Feature Selection - Recursive Feature Elimination
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Random Forest - Model Evaluation, AUC ROC Curve, Precision Recall Curve
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Creating XGBoost Model
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Finding Optimal Threshold, Model Evaluation
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Cumulative Gain Curve
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Creating Light GBM Model
Fundamentals of Natural Language Processing (NLP)
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Resources
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Intro. to NLP
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Project : Customer Review Classification, Data Overview
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Data Cleaning
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Text to Vector conversion
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Bag of Words (BoW)
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Terminologies
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Text Pre-processing : Stop Word Removal, Stemming, Lemmatization
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Text Pre-processing : Code Example
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Text Pre-processing : applying to review data
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Creating Bag of Words
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uni-gram, bi-gram and tri-gram
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Creating uni-gram, bi-gram and tri-gram's
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Term Frequency (TF) and Inverse Document Frequency (IDF)
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Why log in IDF?
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Creating TF-IDF matrix
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Model Building : Classification of Reviews
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Word Embeddings : Word2Vec
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Average Word2Vec, TFIDF Weighted Word2Vec
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Creating Word2Vec
Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
DBSCAN (Density based clustering)
Recommender Systems and Matrix Factorization
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Resources
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Recommender Systems
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Types of Recommender Systems
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User based Collaborative Filtering
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Challenges in User based Collaborative Filtering
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Item based Collaborative Filtering
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Matrix Factorization : SVD
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SVD, Eigen Decomposition Relevance
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Matrix Factorization for Collaborative Filtering
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Creating Recommender Systems : Non Personalized
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Creating Recommender Systems : User based Collaborative Filtering
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Creating Recommender Systems : Item based Collaborative Filtering
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Creating Recommender Systems : Matrix Factorization
Deep Learning : Fundamentals
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Resources
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Artificial Intelligence vs Machine Learning vs Deep Learning
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What is Deep Learning?
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Factors behind Deep Learning popularity
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Intro. to Google Colab
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Perceptron
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Multi Layer Perceptron
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Visualizing Neural Network
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Neural Network : Notation
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Training Single Neuron Model
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Training MLP
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Backward Propagation
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Epoch versus Iteration
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Activation Functions : Linear activation
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Activations Functions : Sigmoid, Tanh
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Activation Functions : Softmax
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Vanishing Gradient Problem
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Activation Functions : ReLU, Leaky ReLU
Deep Learning : High Performing Neural Nets
Project : Image Classification using Tensorflow and Keras
PyTorch
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Deep Learning with PyTorch - Introduction
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Overview of Tensors
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Tensor Operations
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Linear Regression Implementation from Scratch - using PyTorch
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Gradient Descent, Mini-batch and Stochastic Gradient Descent
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Dataset and DataLoader Classes
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MLP Regression Model Building
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MLP Regression Model Training
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Logistic Regression Implementation using PyTorch
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SoftMax Activation
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Cross Entropy Loss
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Image Classification - MLP - 1. Data PreProcessing
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Image Classification - MLP - 2. Model Building
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Image Classification - MLP - 3. Model Training
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Image Classification - 4. Model Training with GPU
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Image Classification - 5. Hyper Parameter Tuning
Deep Learning : Convolutional Neural Nets
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Resources
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Why CNN?
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Convolution : Filters
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Convolution : Edge Detection using Sobel Kernel
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Convolution over RGB Images
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Local Connectivity and Parameter Sharing
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CNN Architecture
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Pooling
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CNN Forward and Backward Propagation
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Creating CNN model
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Hyper parameter tuning
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Image augmentation
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Image augmentation implementation
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CNN Image augmentation using Keras
Deep Learning : LSTMs, RNNs
Building Agentic Workflows using AutoGen
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Intro. to AI Agents & AutoGen
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Multi-Agent Systems, Built-in Classes explained
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Creating a Multi-Agent System, Coding Excerise
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Using LLM APIs
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Run LLMs locally using Ollama
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Building a Multi-Agent Chat System - Sudoku Solver
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AutoGen Conversation Patterns - Sequential Chat Agent Workflow
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AutoGen Conversation Patterns - Nested Chat Agent Workflow
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AutoGen Function Calling - Register and Invoke Custom Functions in AI Agents
Agentic AI with LangChain
