01.

Python Programming

Python Fundamentals: Variables, loops, conditionals, and functions.

Advanced Python: Modules, classes, OOP principles, and regular expressions.

Data Libraries: Introduction to NumPy, Pandas, and Matplotlib for data manipulation and visualization.

02.

Data Manipulation and Visualization

Pandas for data cleaning, wrangling, and extraction.

Matplotlib for creating compelling data visualizations.

03.

Machine Learning

Core ML Concepts: Supervised, unsupervised learning, and key algorithms like decision trees, SVM, and k-means clustering.

Regression Models: Linear regression, logistic regression, ridge, lasso, and polynomial regression.

Model Evaluation: Performance metrics including confusion matrix, ROC curve, and precision-recall.

04.

Deep Learning

Introduction to Artificial Neural Networks (ANNs) using Keras.

Building models using sequential and functional APIs.

Training deep networks, handling vanishing/exploding gradients, and regularization techniques

05.

Advanced Topics

Transfer Learning: Reusing pre-trained models for faster and more accurate results.

Hyperparameter Tuning: Optimizing learning rate, batch size, and number of neurons.