Introduction to Artificial Intelligence & Industry Applications

Overview of Artificial Intelligence and Machine Learning

History and evolution of AI

Types of AI – Narrow AI, General AI, Super AI

Real-world applications of AI across industries

AI in business, healthcare, finance, manufacturing & marketing

Career paths and roles in AI & Machine Learning

Python Programming for AI & Machine Learning

Introduction to Python and setup

Python syntax, variables, and data types

Conditional statements and loops

Functions and modules

Object-Oriented Programming concepts

Working with files and exceptions

Popular Python libraries for AI & ML

Mathematics & Statistics for Machine Learning

Linear algebra fundamentals

Matrices, vectors, and matrix operations

Probability theory basics

Descriptive and inferential statistics

Mean, median, variance, and standard deviation

Correlation and covariance

Probability distributions

Data Analysis & Data Preprocessing

Understanding data types and data sources

Data collection techniques

Handling missing values

Data cleaning and transformation

Feature scaling and normalization

Encoding categorical data

Exploratory Data Analysis (EDA)

Data visualization using Python

Machine Learning Fundamentals

What is Machine Learning?

Types of Machine Learning – Supervised, Unsupervised, Reinforcement

Training and testing datasets

Model evaluation techniques

Overfitting and underfitting

Bias-variance tradeoff

Cross-validation methods

Supervised Learning Algorithms

Linear Regression

Multiple Linear Regression

Logistic Regression

K-Nearest Neighbors (KNN)

Decision Trees

Random Forest

Support Vector Machines (SVM)

Model performance metrics – Accuracy, Precision, Recall, F1-score

Unsupervised Learning Algorithms

Introduction to clustering techniques

K-Means Clustering

Hierarchical Clustering

DBSCAN

Dimensionality Reduction

Principal Component Analysis (PCA)

Association Rule Learning

Deep Learning Fundamentals

Introduction to Neural Networks

Biological vs Artificial Neural Networks

Perceptron model

Activation functions

Loss functions

Gradient Descent and Backpropagation

Model optimization techniques

Artificial Neural Networks (ANN)

Architecture of ANN

Forward and backward propagation

Training neural networks

Hyperparameter tuning

Regularization techniques

ANN use cases and applications

Convolutional Neural Networks (CNN)

Introduction to CNN

Image processing basics

Convolution and pooling layers

Image classification models

Object detection concepts

Real-world computer vision applications

Recurrent Neural Networks (RNN)

Sequence data understanding

Introduction to RNN

Long Short-Term Memory (LSTM)

Gated Recurrent Units (GRU)

Time series forecasting

Text and speech-based applications

Natural Language Processing (NLP)

Introduction to NLP

Text preprocessing techniques

Tokenization and stemming

Bag of Words and TF-IDF

Sentiment analysis

Text classification

Chatbots and language models

Machine Learning Model Deployment

Model saving and loading

Introduction to APIs

Deploying ML models using Flask / FastAPI

Model monitoring and maintenance

Real-time vs batch predictions

AI Tools & Frameworks

NumPy

Pandas

Matplotlib & Seaborn

Scikit-learn

TensorFlow

Keras

PyTorch (Introduction)

Ethics, Bias & Responsible AI

Ethical considerations in AI

Bias in machine learning models

Fairness and transparency

Data privacy and security

Responsible AI practices

Industry Projects & Case Studies

End-to-end Machine Learning project

AI-based prediction system

Image or text-based AI application

Real-world datasets and problem statements

Model evaluation and optimization

Project presentation and documentation

Career Preparation & Interview Readiness

Resume building for AI & ML roles

Portfolio development

Interview questions and problem-solving

Industry best practices

Career guidance and roadmap