📘 AI & Machine Learning – Full Course Content

Module 1: Introduction to Artificial Intelligence

• What is AI? Types of AI

• Applications of AI in real world

• AI vs Machine Learning vs Deep Learning

• Basic terminology: features, labels, training, testing

Module 2: Python for AI & ML

• Python basics: variables, loops, functions

• Numpy & Pandas for data handling

• Matplotlib & Seaborn for visualization

• Working with Jupyter Notebook

• Practice exercises

Module 3: Data Preprocessing & Exploratory Data Analysis

• Handling missing data

• Encoding categorical data

• Feature scaling (Standardization, Normalization)

• Outlier detection

• Correlation analysis, distribution analysis

• Data cleaning case study

Module 4: Machine Learning Fundamentals

Supervised Learning

• Linear Regression

• Logistic Regression

• Decision Trees

• Random Forest

• K-Nearest Neighbors (KNN)

• Support Vector Machines (SVM)

Unsupervised Learning

• K-Means clustering

• Hierarchical clustering

• PCA (Dimensionality Reduction)

Evaluation Metrics

• Accuracy

• Precision

• Recall

• F1-score

• Confusion Matrix

• ROC–AUC

Module 5: Deep Learning Essentials

• Neural Networks explained

• Activation functions

• Backpropagation

• Introduction to TensorFlow / Keras

• Building a simple Neural Network

Module 6: Convolutional Neural Networks (CNN)

• Convolutions, pooling, filters

• Image classification

• Data augmentation

• Building CNN model

Module 7: Natural Language Processing (NLP)

• Text preprocessing (tokenization, stemming, stopwords)

• Bag-of-Words, TF-IDF

• Word embeddings

• Sentiment analysis project

Module 8: Introduction to Large Language Models (LLMs)

• What are LLMs (GPT, LLaMA, Mistral)?

• Tokenization, embeddings

• Prompt engineering basics

• LLM real-world applications