📚Course Overview
Master the fundamentals of machine learning and build intelligent applications
🎯 What You'll Master
ML Fundamentals
Learn core machine learning concepts and algorithms
Data Processing
Master data preprocessing and feature engineering techniques
Model Training
Train and evaluate machine learning models effectively
Real-world Applications
Build practical ML applications for various domains
Explore Our Syllabus
Learn more about our comprehensive curriculum
- Introduction to ML Concepts: Overview of Machine Learning (ML), including supervised, unsupervisedand reinforcement learning. Explanation of key terms like features, labelsand algorithms.
- Python Fundamentals for ML: Basics of Python: data types, loopsand conditionals. Introduction to key libraries: NumPy, Pandas, Matplotlib. Implementing simple algorithms with Scikit-Learn.
- Data Preprocessing: Exploratory Data Analysis (EDA) and data visualization. Techniques for handling missing data and encoding categorical data. Feature scaling and normalization.
- Supervised Learning - Regression: Linear and Polynomial Regression models. Using Scikit-Learn for implementation and performance evaluation (MSE, R-squared).
- Project - Predictive Analysis: Building a housing price prediction model using linear regression.