This module introduces learners to the foundations of machine learning, its real-world applications, and the tools needed to begin hands-on practice. Students explore what machine learning is, how machines learn, and where ML is applied across industries, setting the stage for practical TensorFlow projects.

This module equips learners with essential ML tools such as Anaconda, Jupyter Notebook, and Python libraries. Students learn to manage environments, leverage third-party packages, and perform numerical computations with NumPy for efficient machine learning pipelines.

This module focuses on preparing, analyzing, and visualizing data using Pandas, Matplotlib, and Seaborn. Learners handle complex datasets, manage missing values, and create insightful visualizations to uncover patterns, trends, and anomalies essential for ML readiness.

This module covers essential preprocessing techniques, data transformation, and classical ML algorithms. Students practice feature engineering, scaling, encoding, and regression modeling while leveraging Scikit-learn to prepare clean and structured datasets.

This module introduces deep learning with TensorFlow, covering computational graphs, operations, regression models, and neural networks. Students build and train models using activation functions, optimizers, and the MNIST dataset for hands-on image classification.