Foundation of AI and ML with Python and Excel

Sr No.

Topic

Description

Hours

Module I

Foundation and Basic Concepts

1

Mathematics and Statistics

Foundations of Maths & Statistics , Basic Concepts  Limits functions & Continuity

3

2

Computational Thinking

Concepts – Variables , Iterators , Loops, Nested Iterations , Binning & Sorting

3

3

Python for Data Science

Basics of Python Libraries: NumPy, Pandas, Matplotlib, Seaborn

3

Module II

Introduction to AI and ML basic concepts

4

Introduction to AI and ML

History and Evolution, Applications and Trends

2

5

Machine Learning

Machine Learning basics, Types of ML, Applications of ML, Data Mining Vs Machine Learning vs Big Data Analytics.

2

6

Future Trends in AI and ML

Generative AI , Latest trends in industry

2

Module III

Supervised Learning

7

Regression

Concepts of Linear, Polynomial regression

3

8

Classification

Logistic Regression, Decision Trees, SVMs

3

9

Data Mining Techniques

Introduction to data mining, Knowledge discovery- KDD process.
Data Preprocessing: Types of attributes, Data Cleaning - Missing values, Noisy data, data integration and transformations.

3

10

Data Preprocessing and Cleaning

Data Reduction - Data cube aggregation, dimensionality reduction, data compression, Numerosity reduction, discretization and concept hierarchy.

2

11

Model evaluation and Validation

Build model using appropriate Regression algorithms for real world problems

2

12

Model evaluation and Validation

Build model using appropriate Classification algorithms for real world problems

2

Capstone Project

Problem Identification, Data Collection and Preprocessing, Model Development and Evaluation Presentation and Reporting