Course Description

Course Overview:

This course is designed to take you from the fundamentals of machine learning to real-world applications, equipping you with hands-on skills to build, evaluate, and deploy ML models. You'll start with the basics of supervised and unsupervised learning, progress to advanced topics like deep learning and model deployment, and gain practical experience with Python, Scikit-Learn, and TensorFlow/Keras. By the end of the course, you'll be able to develop ML models for various applications, preprocess data effectively, and deploy your solutions in real-world scenarios using Flask and FastAPI.

Course Curriculum Chapter 1: Introduction to Machine Learning

📌 Lesson 1: What is Machine Learning? Types and Applications
📌 Lesson 2: Supervised vs. Unsupervised Learning
📌 Lesson 3: Setting Up Your ML Environment (Python, Scikit-Learn)

Chapter 2: Data Preprocessing & Feature Engineering

📌 Lesson 1: Handling Missing Data, Outliers & Data Scaling
📌 Lesson 2: Feature Selection and Dimensionality Reduction (PCA, LDA)

Chapter 3: Supervised Learning - Regression & Classification

📌 Lesson 1: Linear & Logistic Regression - Understanding & Implementation
📌 Lesson 2: Decision Trees, Random Forest, and Ensemble Methods
📌 Lesson 3: Model Evaluation Metrics (Accuracy, Precision, Recall, F1-score)

Chapter 4: Unsupervised Learning - Clustering & Anomaly Detection

📌 Lesson 1: K-Means, DBSCAN, and Hierarchical Clustering
📌 Lesson 2: Anomaly Detection & Outlier Detection Techniques

Chapter 5: Deep Learning & Neural Networks

📌 Lesson 1: Introduction to Neural Networks & Backpropagation
📌 Lesson 2: Implementing Deep Learning Models with TensorFlow/Keras

Chapter 6: Model Deployment & Real-World Applications

📌 Lesson 1: Deploying ML Models with Flask and FastAPI
📌 Lesson 2: Real-World Case Studies in Machine Learning

Why Should You Take This Course?

✔ Comprehensive Learning Path – Covers everything from ML basics to advanced deep learning and deployment.
✔ Hands-on Practical Experience – Includes Python implementation, Scikit-Learn, TensorFlow/Keras, and model deployment with Flask/FastAPI.
✔ Real-World Applications – Learn how ML is applied in industries like healthcare, finance, and automation.
✔ Industry-Relevant Skills – Gain expertise in feature engineering, model evaluation, and anomaly detection.
✔ Deploy Your Own ML Models – Go beyond theory and learn to deploy models in production-ready environments.
✔ Structured for All Levels – Whether you're a beginner or an intermediate learner, this course will enhance your ML knowledge step by step.