Machine Learning with Python

Duration: Three Months

Format: Hands-on Practice Sessions + Assignments + Capstone Project

Machine Learning (ML) is revolutionizing how industries make data-driven decisions. This course offers a balanced blend of theory and hands-on practice, helping participants understand core ML concepts and algorithms. You’ll learn how to prepare data, train models, and evaluate their performance using real-world scenarios. With Python as the primary programming language, the course guides you through building and deploying ML models using popular libraries like scikit-learn. By the end, you’ll be equipped to tackle practical challenges and create intelligent solutions that adapt and improve over time.

Course Curriculum

1. Introduction to Machine Learning
   1. Applications of Machine Learning
   2. Types of Machine Learning
   3. Machine Learning Process
   4. Python Libraries for Machine Learning
2. Data Pre-processing
   1. Handling missing values
   2. Feature Scaling with Normalization
   3. Feature Scaling with Standardization
   4. Encoding Categorical Variables
3. Supervised Learning – Regression
   1. Correlation and Regression
   2. Simple Linear Regression
   3. Multiple Linear Regression
   4. Model Evaluation
4. Supervised Learning – Classification
   1. Logistic Regression
   2. Decision Trees
   3. Random Forest
   4. Model Evaluation
5. Unsupervised Learning & Deep Learning
   1. K-Means Clustering
   2. Deep Learning Basics
   3. Types of Artificial Neural Networks
   4. Multilayer Perceptron for Classification

 Ideal For: students and professionals aspiring to pursue career in AI field