AI and ML Course

AI, ML & Python Introduction + Setup

• What is Artificial Intelligence and Machine Learning

• Real-world applications and use cases

• Why Python for AI and ML

• Setting up development environment

• Installing Anaconda Distribution

• Introduction to Jupyter Notebook

• Assignment: Install Anaconda and setup environment

Variables & Data Types

• What are variables and why they matter

• Python data types (int, float, string, boolean)

• Creating and assigning variables

• Type checking and type conversion

• Best practices for naming variables

• Assignment: Write simple Python programs with variables

Input, Output & Type Conversion

• Taking user input using input() function

• Displaying output using print() function

• Type conversion and casting

• Formatting output

• Building interactive programs

• Assignment: Create an interactive program

Conditional Statements

• Understanding if-else statements

• Comparison operators (==, !=, >, <, >=, <=)

• Logical operators (and, or, not)

• elif statements for multiple conditions

• Nested if statements

• Assignment: Build a decision-making program

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While Loops + Practice

• While loop syntax and usage

• Loop control (break, continue, pass)

• Infinite loops and prevention

• Practice with both loop types

• Assignment: Build programs using while loops

Lists

• What are lists and why they matter

• Creating and accessing lists

• List methods (append, extend, insert, remove, pop)

• Slicing and indexing

• List comprehension basics

• Assignment: Build programs using lists

Tuples & Sets

• What are tuples and their characteristics

• Creating and accessing tuples

• Tuple methods and immutability

• What are sets and their characteristics

• Set operations (union, intersection, difference)

• Assignment: Write programs using tuples and sets

Dictionaries

• What are dictionaries and why they matter

• Creating and accessing dictionaries

• Dictionary methods (keys, values, items, get)

• Adding and removing items

• Nested dictionaries

• Assignment: Build programs using dictionaries

 Strings

• String basics and methods

• String slicing and indexing

• String formatting (f-strings, format)

• String methods (upper, lower, split, join, replace)

• Regular expressions basics

• Assignment: Write string manipulation programs

File Handling

• Reading and writing files

• Open, read, write, close operations

• Working with CSV files

• File modes (r, w, a, r+, w+, a+)

• Exception handling basics

• Assignment: Build file handling programs

Mini Project - Python Fundamentals

• Apply all Python fundamentals learned

• Build a complete small project

• Project planning and execution

• Code review and feedback

• Assignment: Complete and submit mini project

Intro to Data & NumPy

• Understanding numerical data

• What is NumPy and why it matters

• Installing and importing NumPy

• NumPy arrays vs Python lists

• Creating NumPy arrays

• Assignment: Create and explore NumPy arrays

 NumPy Arrays & Operations

• Array attributes and properties

• Array operations (addition, subtraction, multiplication)

• Universal functions (ufuncs)

• Broadcasting in NumPy

• Reshaping and transposing arrays

• Assignment: Perform array calculations

Pandas Series

• What is Pandas and why it matters

• Pandas Series creation

• Accessing Series data

• Series operations and methods

• Assignment: Work with Pandas Series

Pandas DataFrames

• Creating DataFrames from various sources

• Accessing DataFrame data

• DataFrame attributes and methods

• Viewing and exploring datasets

• Assignment: Explore tabular datasets

Row & Column Selection

• Selecting columns (single and multiple)

• Selecting rows (loc, iloc)

• Conditional selection

• Adding and removing columns

• Assignment: Filter datasets effectively

Data Cleaning

• Handling missing data (NaN values)

• Replacing and imputing missing values

• Dropping rows and columns

• Handling duplicate data

• Assignment: Clean a real-world dataset

 Data Transformation

• Creating new features from existing data

• Applying functions to data

• Grouping and aggregation

• Merging and joining DataFrames

• Pivot tables

• Assignment: Create new insights from data

Matplotlib Visualization

• What is Matplotlib and why it matters

• Creating line plots, bar charts, scatter plots

• Customizing plots (titles, labels, legends)

• Subplots and multi-plot layouts

• Assignment: Plot basic charts

Seaborn Visualization

• What is Seaborn and why it matters

• Statistical plots (box plots, violin plots, swarm plots)

• Heatmaps and pair plots

• Distribution plots and histograms

• Color palettes in Seaborn

• Assignment: Visualize data patterns

Basic Statistics

• Statistical summary (mean, median, mode)

• Standard deviation and variance

• Correlation and covariance

• Understanding distributions

• Assignment: Summarize data statistically

Exploratory Data Analysis (EDA)

• What is EDA and why it matters

• Data profiling and overview

• Univariate, bivariate, multivariate analysis

• Finding trends and outliers

• Drawing insights from data

• Assignment: Conduct EDA on a dataset

Mini Project - Exploratory Data Analysis

• Apply all data analysis skills learned

• Analyze a real-world dataset

• Create visualizations and insights

• Present findings

• Assignment: Complete and submit EDA project

Machine Learning Introduction

• What is Machine Learning

• Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

• Machine Learning workflow (data collection, preprocessing, training, evaluation)

• Real-world applications

• Assignment: Research ML applications

Train-Test Split & Overfitting

• Train-test split concept and importance

• Splitting datasets (80-20 rule)

• Understanding overfitting and underfitting

• Bias-variance tradeoff

• Assignment: Split datasets correctly

Scikit-learn Basics

• What is Scikit-learn and why it matters

• Installing and importing Scikit-learn

• Understanding the Scikit-learn API

• Building a first ML model

• Assignment: Build first ML model

Linear Regression (Concept)

• Understanding regression

• Simple linear regression

• Multiple linear regression

• Cost function and optimization

• Assignment: Understand regression concepts

Linear Regression (Implementation)

• Implementing Linear Regression in Scikit-learn

• Training the regression model

• Making predictions

• Visualizing regression results

• Assignment: Train a regression model

 Regression Evaluation

• Mean Absolute Error (MAE)

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

• R-squared score

• Adjusted R-squared

• Assignment: Measure model error

Logistic Regression

• Understanding classification

• What is Logistic Regression

• Logistic function (sigmoid)

• Binary classification

• Assignment: Build a logistic regression model

Classification Metrics

• Accuracy score

• Confusion matrix

• Precision and Recall

• F1-score

• ROC curve and AUC

• Assignment: Evaluate classification models

KNN Algorithm

• What is K-Nearest Neighbors

• How KNN works

• Distance metrics (Euclidean, Manhattan)

• Choosing the right K value

• Assignment: Implement KNN algorithm

Feature Scaling

• Why feature scaling matters

• Standardization (Z-score normalization)

• Min-Max scaling (Normalization)

• When to use which method

• Assignment: Apply feature scaling to datasets

Model Comparison

• Comparing different models

• Pros and cons of each algorithm

• Selecting the best model

• Model optimization

• Assignment: Compare multiple models

Mini Project - Classification

• Apply all ML skills learned

• Build an end-to-end classification project

• Train, evaluate, and optimize models

• Present findings

• Assignment: Complete and submit classification project

 Decision Trees & Random Forests

• What are Decision Trees

• How Decision Trees work

• Understanding Gini impurity and entropy

• Tree pruning and depth control

• Assignment: Build a decision tree classifier

Random Forests

• What is Ensemble Learning

• How Random Forests work

• Bootstrapping and aggregation

• Advantages over Decision Trees

• Assignment: Build a random forest model

Unsupervised Learning Intro

• What is Unsupervised Learning

• Applications of Unsupervised Learning

• Clustering vs Association

• Difference from Supervised Learning

• Assignment: Research clustering applications

K-Means Clustering

• What is K-Means Clustering

• How K-Means works

• Cluster assignment and centroid update

• Implementing K-Means in Scikit-learn

• Assignment: Apply K-Means to a dataset

Choosing K (Elbow Method)

• The Elbow Method

• Inertia and WCSS

• Silhouette score

• Choosing optimal K value

• Assignment: Find optimal K for a dataset

Categorical Encoding

• Handling categorical data

• Label Encoding

• One-Hot Encoding

• Ordinal Encoding

• Assignment: Encode categorical data

 Feature Importance

• Understanding feature importance

• Feature selection techniques

• Feature extraction

• Dimensionality reduction

• Assignment: Select important features

Cross-Validation (Basic)

• What is Cross-Validation

• K-Fold Cross-Validation

• Stratified K-Fold

• Leave-One-Out Cross-Validation

• Assignment: Apply cross-validation

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ML Pipeline Overview

• End-to-end understanding of ML projects

• Data collection to deployment lifecycle

• Challenges in ML projects

• Assignment: Understand ML pipeline

Problem Framing

• Understanding business problems

• Framing as ML problems

• Defining success metrics

• Project planning

• Assignment: Frame a real-world problem

Mini Project - Clustering

• Apply unsupervised learning skills

• Build a clustering project

• Analyze and interpret clusters

• Present findings

• Assignment: Complete and submit clustering project

Neural Network Basics

• What are Neural Networks

• Inspiration from human brain

• Structure: Input, hidden, output layers

• Neurons and connections

• Activation functions

• Assignment: Understand NN intuition

Network Architecture

• Designing simple networks

• Choosing number of layers

• Choosing number of neurons

• Hyperparameters overview

• Assignment: Design a simple network

TensorFlow & Keras Intro

• What is TensorFlow and why it matters

• What is Keras and why it matters

• Installing TensorFlow and Keras

• TensorFlow ecosystem overview

• Assignment: Install and explore TensorFlow

Training Concepts

• How networks learn

• Forward propagation

• Backpropagation and gradient descent

• Epochs and batch sizes

• Learning rate optimization

• Assignment: Understand training concepts

 Overfitting in Neural Networks

• Understanding overfitting in NN

• Regularization techniques

• Dropout layers

• Early stopping

• Assignment: Improve generalization

MNIST Dataset

• Introduction to MNIST dataset

• Understanding image data

• Loading and exploring MNIST

• Preparing data for NN

• Assignment: Explore MNIST dataset

Build Image Classifier

• Building a Neural Network for classification

• Training the model

• Evaluating performance

• Visualizing results

• Assignment: Train an image classifier

Model Evaluation

• Assessing NN performance

• Accuracy, loss, and metrics

• Confusion matrix for classification

• Visualizing predictions

• Assignment: Evaluate NN performance

Model Improvement

• Hyperparameter tuning

• Adding dropout and regularization

• Increasing network depth

• Improving accuracy

• Assignment: Improve NN performance

Introduction to NLP

• What is Natural Language Processing

• Text as data

• Common NLP tasks (classification, sentiment, translation)

• Text preprocessing

• Assignment: Explore NLP concepts

Simple Text Classification

• Building a text classifier

• Tokenization and vectorization

• TF-IDF for text representation

• Training and evaluation

• Assignment: Build a text classification model

Final Project Planning

• Define project scope

• Choose a real-world problem

• Set milestones and timeline

• Data collection planning

• Assignment: Submit project proposal

End-to-End ML Project

• Understanding complete ML workflow

• Setting up project structure

• Version control with GitHub

• Collaboration and team work

• Assignment: Setup project repository

• Problem & Data Collection

  • Define project problem

  • Identify data sources

  • Data collection strategies

  • Data documentation

  • Assignment: Collect project data

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EDA for Final Project

• Exploratory Data Analysis

• Understanding project data

• Visualizing key insights

• Data quality assessment

• Assignment: Complete EDA for project

Data Preprocessing

• Data cleaning

• Feature engineering

• Data transformation

• Train-test split

• Assignment: Preprocess project data

Baseline Model

• Building initial model

• Evaluating baseline performance

• Understanding baseline results

• Setting improvement targets

• Assignment: Build baseline model

Model Improvement

• Hyperparameter tuning

• Model optimization

• Comparing model versions

• Final model selection

• Assignment: Optimize project model

Deployment Concepts

• Understanding ML deployment

• Deployment options

• Challenges in deployment

• Model monitoring

• Assignment: Explore deployment options

Streamlit Basics

• What is Streamlit

• Installing and setting up Streamlit

• Creating a simple app

• Adding UI elements

• Assignment: Build a simple Streamlit app

Model Integration

• Connecting ML model with UI

• Creating interactive predictions

• Making app functional

• Testing the application

• Assignment: Integrate model with app

Testing & Debugging

• Testing the application

• Debugging common issues

• Performance optimization

• Final refinements

• Assignment: Test and debug application

Report Writing & Communication

• Writing project report

• Visualizing results

• Presentation preparation

• Communicating findings

• Assignment: Prepare project report

Final Presentation

• Showcase final project

• Present to peer group

• Demonstrate application

• Receive feedback

• Course completion and certification