Artificial Intelligence & Machine Learning
FAB-252610
Module Overview
Artificial Intelligence (AI) and Machine Learning (ML) focus on building systems that learn from data and make intelligent decisions. This course provides a strong foundation in Python-based machine learning along with essential mathematics and statistics required to understand how algorithms work. Students will learn data analysis using NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn, while also covering core statistical concepts such as mean, variance, probability, distributions, correlation, and regression. The course emphasizes both theoretical understanding and hands-on implementation, with Linear Regression as a core applied learning model.
Curriculum
Course Overview
This FabStudio AI & Machine Learning course combines practical implementation with strong mathematical and statistical foundations. Students will learn how machine learning models work internally and apply them using Python and industry-standard libraries.
Technologies Covered: Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn
Core Concepts: Statistics, Probability, Linear Algebra Basics, Linear Regression
Goal: Understand ML theory and build a complete Linear Regression model from scratch and using Scikit-learn.
Learning Objectives
- Understand AI and Machine Learning fundamentals.
- Learn core statistical concepts used in ML.
- Apply probability concepts in data analysis.
- Understand basic linear algebra (vectors, matrices).
- Perform data preprocessing using Pandas and NumPy.
- Visualize data using Matplotlib and Seaborn.
- Implement and evaluate Linear Regression models.
Course Content
Module 1: Introduction to AI & ML
- What is Artificial Intelligence?
- What is Machine Learning?
- Types of Machine Learning
- Real-world applications
Module 2: Mathematics for Machine Learning
- Basics of Linear Algebra (Vectors & Matrices)
- Functions and Graphs
- Concept of Gradient
- Cost Function intuition
Module 3: Statistics & Probability
- Mean, Median, Mode
- Variance and Standard Deviation
- Probability basics
- Normal Distribution
- Correlation and Covariance
Module 4: Python for Data Science
- NumPy arrays and operations
- Data handling with Pandas
- Data cleaning and preprocessing
Module 5: Data Visualization
- Matplotlib plotting
- Seaborn statistical visualization
- Understanding trends and patterns
Module 6: Machine Learning – Linear Regression
- Supervised learning basics
- Linear Regression theory
- Cost function and gradient descent intuition
- Implementation using Scikit-learn
- Model evaluation (MSE, R² score)
Final Project
- Work on a real dataset
- Perform statistical analysis
- Build and evaluate a Linear Regression model
- Present findings and insights