Machine Learning (Python)

Tools:

• TensorFlow: A powerful open-source library for deep learning.


• Scikit-learn: A versatile ML library for regression, classification, and clustering.


• Keras: A high-level API simplifying neural network building.


• Google Colab: A cloud-based Jupyter Notebook with free GPU access.

Materials:

• Pre-built datasets: From Kaggle, UCI, and Scikit-learn.


• Hands-on projects: Regression, classification, and anomaly detection.


• ML-focused e-books: Covering theory and real-world applications.

Course Overview

Machine Learning is a vital skill today. This course provides a structured approach to ML, covering data preprocessing, model building, evaluation, and deployment.

Module 1: Python for Machine Learning

• Installing Python, Jupyter Notebook, and Google Colab.


• Python syntax, loops, functions, and data structures.


• Data manipulation with Pandas and NumPy.


• Visualization using Matplotlib and Seaborn.

Module 2: Introduction to Machine Learning

• Supervised vs. unsupervised learning.


• Data preprocessing: Handling missing values and feature scaling.


• Regression and classification fundamentals.


• Overfitting and underfitting in models.

Module 3: Data Handling and Visualization

• Advanced Pandas techniques.


• Feature engineering and data cleaning.


• Exploratory Data Analysis (EDA).


• Correlation and causation.

Module 4: Core Machine Learning Algorithms

• Decision Trees and pruning.


• K-Nearest Neighbors (KNN) and distance metrics.


• K-Means clustering for pattern recognition.


• Support Vector Machines (SVMs) and hyperplanes.

Module 5: Real-World Applications and Deployment

• Deploying ML models with Flask or Django.


• Creating APIs for ML services.


• Cloud deployment with AWS or Google Cloud.


• Developing a capstone project.

Assessment & Certification

Students will complete coding assignments, quizzes, and a capstone project. Upon completion, they will receive a certificate.

Who Should Enroll?

• Beginners with Python knowledge.


• Software developers interested in ML.


• Students and professionals seeking a structured ML learning path.

Expected Outcomes

• Understand ML concepts and build models.


• Preprocess and clean datasets.


• Train, evaluate, and deploy ML models.

This structured syllabus ensures a deep understanding of both theoretical and practical ML concepts.