Introduction to data manipulation and analysis.

Creating and manipulating arrays - NumPy

Introduction to Pandas for data manipulation

Data structures (Series, DataFrame)

Data cleaning and preprocessing techniques.

Introduction to data visualization and its importance

Matplotlib for 2D plotting - Line plots, scatter plots, bar plots, histograms and more.

Categorical plots, distribution plots, regression plots with Seaborn.

Plotly for interactive and web-based visualizations.

Creating interactive plots and dashboards

Introduction to machine learning and supervised learning.

Simple linear regression, multiple linear regression

Model evaluation metrics (mean squared error, R-squared)

Logistic regression for binary classification.

Gradient descent optimization algorithm

Naive Bayes for probabilistic classification

Linear SVM, kernel trick, nonlinear classification.

K-Nearest Neighbors (KNN) algorithm.

Decision Trees for classification and regression

Clustering algorithms overview

K-means Clustering for grouping data

Elbow method for cluster evaluation.

Hierarchical Clustering for creating tree-like clusters.

Introduction to artificial neural networks (ANN)

Perceptron and activation functions

Feedforward neural networks and forward propagation

Multi-layer networks with Keras

Activation functions, weight initialization, regularization

Saving and restoring a trained model for future use

Introduction to Convolutional Neural Networks (CNN)

CNN architectures: AlexNet, VGGNet, InceptionNet, ResNet

Convolutional layers, pooling layers, fully connected layers.

Introduction to Generative Adversarial Networks (GAN)

Training GANs for image generation and manipulation

Introduction to transfer learning

Using pre-trained models and transfer learning with Keras

Working with the ImageNet dataset

Building image classification projects using transfer learning

Fine-tuning pre-trained models