Prime: AI/ML Batch

LinkedIn Mentorship Session by Aman Sir

GSoC Mentorship Session

What will we Learn?

Schedule

Why did we choose Python?

Tools to Install

Using Visual Studio Code

Variables in Python

Data Types in Python

Keywords & Comments

Style Guide

Arithmetic Operators

Relational Operators

Assignment Operators

Logical Operators

Operator Precedence

Type Conversion & Casting

Taking User Input

Average of 2 Nums

Practice Examples (Conditionals)

Odd or Even

Nesting

Match case in Python

Loops using while

Practice Examples (Loops)

Multiplication Table of N

Break & Continue

Loops using for

Vowel Count

range( ) Function

Sum of N numbers

Functions in Python

Practice Examples (Functions)

Types of Functions

Lambda Functions

Factorial of N

Slicing in Strings

String Formatting - f-string & format( )

Lists in Python

List Methods

Using Loops with Lists

Tuples in Python

Tuple Methods

Dictionary in Python

Dictionary Methods

Sets in Python

Set Methods

Practice Problem (Part a & b)

Practice Problem (Part c)

Classes & Objects

Attributes & Methods

Constructor - init( ) Method

Types of Constructors

Attributes - class & instance

Instance Methods

Class Methods

Static Methods

Practice Problem

Encapsulation in OOPs

Inheritance in OOPs

Types of Inheritance

Abstraction

Polymorphism (Function Overriding)

Polymorphism (Duck Typing)

Operations on Files

Modes in File Operations

"with" Keyword

Delete Files

Practice Problem

Exception Handling

"finally" Keyword

List Comprehensions

Working with JSON Module

Conda Prompt

Installing Jupyter Notebook

Installing Jupyter Lab

Getting started

Set up JSON data

Amazon Store : Clean & Structure Data

Amazon Store : Meaningful Insights

Amazon Store : Recommendation Feature

Installation & Usage

Python List vs NumPy Array

Creating Arrays from Lists

Creating Arrays (using built-in methods)

Array Properties

Reshaping Arrays

Indexing on Arrays

Slicing Arrays

Copy v/s View in Slice

Common NumPy Data Types

Multi-dimensional Arrays & Axes

3D Arrays

Vectorization & Broadcasting

Vector Normalization

Math - Mean & Standard Deviation

Mathematical Functions (Aggregate)

Other Math functions

What is EDA?

Introduction to Pandas

Series in Pandas

Series Properties

DataFrame in Pandas

Pandas with csv & json data - give file

DataFrame Methods

Using Kaggle DataSet

Indexing & Selecting Data

Filtering Data

Filtering Data using Query

Data Cleaning (Handle Missing Values) - give file

Data Cleaning (Handle Duplicates)

Data Cleaning (Handle Data Types)

Data Cleaning (Handle Strings)

Data Transformation (other Methods)

Practice Task

Writing data to csv & json files

Group by & Aggregation

Melt & Pivot for Reshaping

Basic Visualization with Pandas

Merge & Join Data

Data Concatenation with Pandas

Multiple Sources for Data Science

Starting with SQL

SQL v/s NoSQL

What is SQL?

What is a Table?

(For Windows) Installation

(For Mac) Installation

Our First Database

Our First Table

Database Queries

CREATE Table

What are Constraints?

Key Constraints

Primary & Foreign Keys

INSERT into Table

SELECT Command

Where Clause

Operators in Where

Frequently Used Operators

Limit Clause

Order By Clause

Aggregate Functions

Group By Clause

Having Clause

General Order

UPDATE Table

DELETE Table

ALTER Table

TRUNCATE Table

Practice Set 1

Practice Set 2

Commit in Transactions

Rollbacks & Savepoints

JOINs in SQL (Inner Join)

Left Join & Right Join

Outer Join & Cross Join

Self Join

Practice Problem - Exclusive Joins

Sub-Queries in SQL

Views in SQL

Index in SQL

Composite Index

Stored Procedures

Call & Drop Procedures

Working with APIs

Homework Problem

Starting with Web Scraping

HTML Overview

Important Tags in HTML

Attributes in HTML

Web Scraping (requests Library)

Additional Techniques

Web Scraping (using BeautifulSoup)

BeautifulSoup Methods & Attributes

Storing Collected Data

HTML Elements & Tags

Hello World

Paragraph Element

Heading Elements

Practice Qs (Level1A)

Boilerplate Code

Lists in HTML

Attributes in HTML

Anchor Element

Image Element

Practice Qs

More HTML Tags

Comments in HTML

Is HTML Case Sensitive?

Practice Qs

Inline v/s Block

Div Element

Span Element

Hr Tag

Sup & Sub Tags

Practice Qs

Semantic Markup

Semantic Tags

Practice Qs

HTML Entities

Practice Qs

Emmets

Further Understanding HTML

Tables in HTML

Semantics in Tables

Colspan & Rowspan Attributes

Practice Qs

Forms in HTML

Input - Form Element

Placeholders & Labels

Button Element

Name Attribute

Practice Qs

Checkbox - Input Element

Radio - Input Element

Select - Input Element

Range - Input Element

Text Area

Practice Qs

Solution - Part 2

How to Plot data?

Introduction to Matplotlib

Important Plot Methods

Multiple datasets on Line Plot

Format Strings

Styling & Saving Plots

Common Plots & Charts

Bar Charts (Vertical)

Add Bar labels

Multiple datasets on Bar Chart

Bar Charts (Horizontal)

Scatter Plots

Customizations on Scatter Plots

Add Annotations

Multiple datasets on Scatter Plots

Pie Charts

Customizations on Pie Charts

Multiple datasets on Histogram

axvline in Histogram

Box Plots

Operations on Box Plot

Stack Plots

Subplots

Modern Matplotlib

Practice Task (Weekly Temperature)

Introduction to Seaborn

Creating Plots with Seaborn

Relational Plots in Seaborn

Categorical Plots in Seaborn

Distribution Plots in Seaborn

Relational Plots in Seaborn - Heatmaps

Best Practices for Data Visualization

Understanding Probability

Probability Examples

Solving Practice Problems

Types of Events

Complementary Rule

Addition Rule

Multiplication Rule

Solving Practice Problems

Conditional Probability

Law of Total Probability

Bayes' Theorem

Solving Practice Problem

Random Variables

Mean, Median & Mode

Variance & Standard Deviation

Probability Distributions & its Types

Binomial Distribution

Uniform Distribution

Normal Distribution

Central Limit Theorem

Straight Lines

Distance between 2 Points

Parallel & Perpendicular Lines

Distance between Parallel Lines

Practice Problem

Vectors

Vector Addition

Scalar Multiplication

Dot Product

Cross Product

Matrix

Operations on Matrices

Determinants

Eigen Vectors & Eigen Values

Functions

Composite Functions

Functions: Scalar Multiplication & Addition

Input: Scalar Multiplication & Addition

Differentiation

Differentiation Rules

Finding Minima / Maxima

Practice Problem

Types of ML (Supervised)

Types of ML (Unsupervised & RL)

Regression & Classification

Introduction to sklearn

Linear Regression (Algorithm 1)

What is Best Fit Line?

What is Cost Function?

Understanding Cost Function Curve

What is Gradient Descent?

Summary

Linear Regression (Code)

Regression Evaluation Metrics

Dummy Variable Trap

Other FE Techniques

Overfitting

Underfitting

How to fix Underfit & Overfit?

Is Model underfitting or overfitting?

Regularization (Lasso Regression)

Regularization (Ridge Regression)

Lasso Regression (Algorithm 2)

Using LassoCV

ElasticNet Overview

Logistic Regression (Algorithm 3)

Logistic Regression (Cost Fnx)

Logistic Regression (Code)

Using StandardScaler

Understanding Confusion Matrix

Classification Evaluation Metrics

Naive Bayes (Algorithm 4)

Naive Bayes (Example)

Types of Naive Bayes

Naive Bayes (Code)

kNN (Algorithm 5)

kNN (Code)

Limitations of kNN

What is Validation Data?

What is Cross Validation?

CV for Hyperparameter Tuning

Pipeline in sklearn

Linear Regression (with Gradient Descent)

LR with GD (Step1)

LR with GD (Step2)

LR with GD (Step3)

LR with GD (Step4)

Linear Regression (with OLS)

LR with Gradient Descent vs OLS

Logistic Regression (steps)

Logistic Regression Implementation

KNN Classifier (steps)

KNN Classifier Implementation

Handle Missing Data

Exploratory Data Analysis (EDA)

Feature Encoding

Correlation Heatmap

Feature Scaling

Model Training & Evaluation

Feature Engineering to Improve Model

Important Pointers

Decision Tree Classifier

Entropy (Impurity Metric)

Gini Impurity

Entropy v/s Gini Impurity

Information Gain

Pruning Decision Trees

Common Pruning Rules

Decision Tree Classifier (Code)

Pre-Pruning Implementation

Post-Pruning Implementation

Decision Tree Regressor

Variance Reduction

Decision Tree Regressor (Code)

SVM Classifier (Intuition)

Classification Hyperparameters

Kernel in SVM

Common Kernel Types

SVM Classifier (Code)

SVM Regressor (Intuition)

SVM Regressor (Code)

GridSearchCV for Hyperparameters

LinearSVR v/s SVR

Ensemble Learning

What is Bagging?

What is Boosting?

Random Forest

Out Of Bag (OOB)

Random Forest (Implementation)

Decision Tree v/s Random Forest

Bagging Classifier & Regressor

GB Classifier (Intuition)

GB Regressor (Code)

GB Classifier (Code)

AdaBoost (Intuition)

AdaBoost (Code)

Other Boosting Algorithms

XGBoost Code

Homogenous v/s Heterogenous Ensemble

Voting (Logic)

Voting (Code)

Stacking (Logic)

What is Blending?

Stacking (Code)

Deep learning - Neural Networks & terminologies

Perceptron

FNN, RNN & CNN architectures

Transformer

PyTorch

GenAI (LLMs, NLP)

RAG (Retrieval-Augmented Generation)

GAN (Generative Adversarial Network)

Agentic AI

OpenAI APIs

Flask (Development with AI)

Git & Github (Version Control)

Docker

Kubernetes

+ multiple Projects