Artificial Intelligence and Machine Learning

Explore the world of Artificial Intelligence and Machine Learning. Learn core AI concepts, build machine learning models, and gain hands-on experience applying ML techniques to real-world problems and modern business scenarios

Course Content

Introduction to Artificial Intelligence

What is AI?
History & Evolution of AI
AI vs ML vs DL
Narrow vs General AI
Real-world AI Applications

AI Problem-Solving & Search Techniques

Knowledge Representation & Logic

Probabilistic Reasoning

Introduction to ML

Python for ML

EDA & Data Preprocessing

Feature Engineering

Linear & Logistic Regression

KNN & SVM

Decision Trees & Random Forests

Model Evaluation

Unsupervised Learning

PCA & t-SNE

Association Rule Mining

Ensemble Methods

Cross Validation & Tuning

Introduction to NLP (AI Module)

Introduction to Computer Vision (AI Module)

Model Deployment Basics

Capstone Project – Part 1

Capstone Project – Part 2

Capstone Project – Part 3

AI & ML Interview Preparation

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Artificial Intelligence and Machine Learning

What Will You Learn?

Course Content

Introduction to Artificial Intelligence

What is AI?

History & Evolution of AI

AI vs ML vs DL

Narrow vs General AI

Real-world AI Applications

AI Problem-Solving & Search Techniques

State Space Search

Uninformed Search (BFS, DFS)

Informed Search (A*, Heuristics)

Optimization Problems

Knowledge Representation & Logic

Propositional Logic

Predicate Logic

Rule-based Systems

Knowledge Graphs & Ontologies

Probabilistic Reasoning

Bayesian Networks

Markov Models

Uncertainty Handling

Introduction to ML

Definition

Applications

Types of ML

Python for ML

NumPy

Pandas

Matplotlib

Scikit-learn

EDA & Data Preprocessing

Handling missing data

Outliers

Encoding

Feature scaling

Feature Engineering

Feature selection

Dimensionality reduction overview

Linear & Logistic Regression

Math intuition

Implementation

Evaluation metrics

KNN & SVM

Working of KNN

Hyperparameters

SVM theory

Decision Trees & Random Forests

Tree building

Pruning

Ensemble method

Model Evaluation

Confusion Matrix

ROC-AUC

Precision

Recall

Unsupervised Learning

K-Means

Hierarchical Clustering

PCA & t-SNE

Dimensionality reduction

Visualization

Association Rule Mining

Apriori algorithm

Eclat algorithm

Ensemble Methods

Bagging

Boosting

Stacking

Cross Validation & Tuning

Grid Search

Random Search

CV strategies

Introduction to NLP (AI Module)

Text preprocessing

Tokenization

Stemming & Lemmatization

Bag-of-Words & TF-IDF

Introduction to Computer Vision (AI Module)