Syllabus
Explore the journey through AI fundamentals, from foundational concepts to hands-on projects. Each week builds essential skills, covering topics like search algorithms, machine learning, neural networks, and more, preparing you for a deep dive into the world of artificial intelligence.
Week 1: Python and Database Foundations and Setup
Week 1: Python and Database Foundations and Setup
Week 1 introduces the basics of artificial intelligence, covering key concepts like problem-solving and intelligent agents to set the foundation for AI learning.
Day 1: Python Foundation
🔥 Setting up a development environment🔥 Introduction to Python for Data Science🔥 Object-Oriented Programming in Python(over View matra)🔥 Best practices and advanced Python concepts🔥 Error Handling and Debugging🔥 Assessment and Feedback(Instructor Should design)Day 2: Data Management
🔥 Introduction to SQL🔥 Database Design🔥 Database Querying🔥 Practical
WEEK 2: Mathemematics and Statistics
WEEK 2: Mathemematics and Statistics
In Week 2, we build a strong foundation in the mathematical and statistical concepts essential for machine learning and AI.
Day 1: Mathematics for ML
🔥 Linear Algebra🔥 Vectors and Matrices🔥 Calculus🔥 Set Theory and Logic🔥 Graph TheoryDay 2: Statistics for ML
🔥 Introduction to Statistics with Python of AI🔥 Descriptive Statistics🔥 Inferential Statistics🔥 Probability Distributions🔥 Practical
WEEK 3: Data Preprocessing and Exploration
WEEK 3: Data Preprocessing and Exploration
Master essential skills in web scraping, advanced data preprocessing techniques, and data visualization to prepare and analyze data effectively for AI applications.
Day 1: Web Scraping
Day 2: Data Preprocessing Techniques
🔥 Advanced data Preprocessing with Python🔥 Handling Missing Values🔥 Feature Engineering🔥 PCA🔥 Outlier Detection and Treatment🔥 Normalisation and ScalingDay 3: Data Visualization
🔥 Understanding Data Visualtization🔥 Introduction to MatPlotLib🔥 Introduction to Seaborn🔥 Advanced-Data Visualization Techniques🔥 Storytelling with Data🔥 Visualization Tools: (Introduction to Tableau, PowerBI, etc.)
WEEK 4: Machine Learning
WEEK 4: Machine Learning
In Week 4, Dive into core machine learning concepts, exploring supervised learning techniques like regression and classification, and unsupervised learning methods such as clustering and dimensionality reduction.
Day 1: Supervised Learning: Regression and Classification Algorithms
🔥 Introduction to Regression🔥 Linear Regression🔥 Polynomial Regression🔥 Mean Absolute Error and Mean Squared ErrorDay 2: Unsupervised Learning: Clustering and Dimensionality Reduction
🔥 K-Means Clustering🔥 Hierarchical Clustering🔥 DBSCAN🔥 PCA
WEEK 5: Practical Applications
WEEK 5: Practical Applications
In Week 5, Apply your knowledge to real-world scenarios with an end-to-end machine learning project and team-based supervised project implementation.
Day 1: End to End Project
🔥 Machine Learning ProjectDay 2: Supervised Project
🔥 Team Based Project Implementation
