● What is AI? History and evolution
● Types of AI: Narrow AI, General AI, Super AI
● Applications of AI in real life (Healthcare, Finance, Education, E-commerce)
● AI vs Machine Learning vs Deep Learning
● Career paths in AI/ML

● Python basics (variables, data types, operators)
● Control statements (if, loops)
● Functions and modules
● File handling
● Exception handling
● Introduction to Jupyter Notebook

● Linear Algebra basics (vectors, matrices)
● Statistics and Probability
● Mean, Median, Mode
● Variance and Standard Deviation
● Probability distributions
● Correlation and covariance

● Introduction to NumPy
● Data manipulation using Pandas
● Data cleaning and preprocessing
● Handling missing values
● Data visualization using Matplotlib & Seaborn
● Exploratory Data Analysis (EDA)

● What is Machine Learning?
● Types of ML: Supervised Learning 
●Unsupervised Learning
● Reinforcement Learning
● Machine Learning workflow
● Training vs Testing data

● Linear Regression 
● Multiple Linear Regression 
● Logistic Regression
● K-Nearest Neighbors (KNN)
● Decision Trees
● Random Forest
● Support Vector Machine (SVM)

● Clustering concepts
● K-Means Clustering
● Hierarchical Clustering
● DBSCAN
● Dimensionality Reduction: PCA (Principal Component Analysis)

● Performance metrics: Accuracy
Precision
Recall
F1-Score
● Confusion Matrix
● Bias-Variance tradeoff
● Cross-Validation
● Hyperparameter tuning

● Neural Networks basics
● Perceptron model
● Activation functions
● Artificial Neural Networks (ANN)
● Introduction to TensorFlow & Keras 

● Basics of NLP
● Text preprocessing
● Tokenization
● Stemming & Lemmatization
● Bag of Words & TF-IDF
● Sentiment Analysis

● Image processing concepts
● OpenCV introduction
● Image classification
● Face detection basics
● Object detection overview

● Ethical issues in AI
● Bias in Machine Learning
● Data privacy and security
● Explainable AI
● AI regulations and future trends

● Google Colab
● Jupyter Notebook
● Git & GitHub
● Kaggle datasets
● Model deployment basics

● Student Performance Prediction
● House Price Prediction
● Spam Email Detection
● Customer Segmentation
● Sentiment Analysis System

● Problem statement selection
● Dataset collection
● Model building & evaluation
● Result analysis
● Project documentation
● Final presentation