Course Overview
This comprehensive course provides a solid foundation in Machine Learning (ML) and Artificial Intelligence (AI). We cover statistical modeling, essential ML algorithms, Python libraries like TensorFlow and PyTorch, and the practical skills needed to analyze data, train models, and solve real-world problems.
The training is highly project-based, culminating in a Capstone AI project where you will deploy a functional model.
Detailed Curriculum
Module 1: Foundations & Python for Data Science
- Python Fundamentals (Numpy, Pandas)
- Introduction to Linear Algebra and Calculus for ML
- Data Visualization (Matplotlib, Seaborn)
Module 2: Classical Machine Learning Algorithms
- Linear and Logistic Regression
- Decision Trees and Random Forests
- K-Means Clustering and Dimensionality Reduction (PCA)
Module 3: Deep Learning and Neural Networks
- Introduction to Neural Networks and Backpropagation
- Convolutional Neural Networks (CNNs) for Image Processing
- Recurrent Neural Networks (RNNs) for Sequence Data
- Frameworks: TensorFlow/Keras and PyTorch
Module 4: Model Deployment and Ethics
- Model Evaluation, Bias, and Fairness
- Introduction to MLOps (Deployment using Flask/Streamlit)
- Final Capstone Project & Presentation