History and evolution of AI

Applications and impact of AI in various industries

Definitions: AI, machine learning, deep learning, Types of AI

Installing Python and Jupyter Notebook

Python Basics, Variables, data types, and control structures

Functions and modules

Lists, tuples, dictionaries, and sets

Using Pandas for data manipulation, Basic data cleaning techniques

Linear Algebra Basics, Calculus Basics

Derivatives and integrals, Partial derivatives and gradients

Solving linear algebra problems using NumPy

Visualizing functions and derivatives using Matplotlib

Basic probability concepts, Probability distributions

Descriptive statistics, Inferential statistics

Understanding data distributions

Hypothesis testing and confidence intervals

Machine Learning Basics (Supervised vs. unsupervised learning, Key concepts)

Regression Algorithms(Linear regression, Polynomial regression)

Classification Algorithms (Logistic regression, k-Nearest Neighbors (k-NN))

Decision Trees and Random Forests (Building and understanding decision trees, Ensemble methods and Random Forests)

Support Vector Machines (SVM)

Model Evaluation (Metrics, Cross-validation techniques)

Clustering Algorithms, K-means clustering

Hierarchical clustering, Dimensionality Reduction

Principal Component Analysis (PCA)

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Feature Engineering, Creating new features

Feature scaling and encoding categorical variables

Cross-validation techniques

Hyperparameter tuning (Grid Search, Random Search)

Neural Networks Basics, Introduction to neural networks

Activation functions and loss functions

Introduction to TensorFlow and Keras

Setting up a deep learning project

CNN Architecture

Convolutional and pooling layers

Building and training a CNN model

Image classification, Object detection

Text preprocessing (tokenization, stemming, lemmatization)

Bag of Words and TF-IDF, Advanced NLP Techniques

Word embeddings (Word2Vec, GloVe)

Recurrent Neural Networks (RNNs) and LSTMs

Time Series Analysis, Introduction to time series data

Forecasting with ARIMA and Prophet

Case studies of AI applications

Ethical considerations in AI

Capstone Project, Data collection, preprocessing, modeling, and evaluation

Advanced AI Topics, Reinforcement learning basics

Introduction to Generative Adversarial Networks (GANs)

Project Presentation