AI & Data Science with Python

Course Overview

The Complete Path from
Python Beginner to AI Builder

This professional 24-class program takes you through every layer of the modern data and AI stack — starting from Python fundamentals and building all the way up to deploying real AI applications on the web. No prior experience required.

Each class combines concept teaching, live coding sessions, hands-on practice, and assignments — designed to build genuine, job-ready skills over six months of weekend learning.

What You Will Be Able to Do

Write Python programs confidently from scratch

Analyse and clean real-world datasets professionally

Create compelling data visualisations and dashboards

Build and evaluate Machine Learning models

Understand and apply core AI concepts including neural networks

Build NLP pipelines and Computer Vision classifiers

Deploy AI applications to the web with Streamlit

Present a portfolio-ready capstone AI project

AI & Data Science with Python

Professional 24-Class Certificate Program

Duration6 Months

Classes24 classes × 2h 20m

ScheduleWeekend sessions

FormatLive online

Projects3 major projects + capstone

CertificateOn completion

LevelBeginner → Intermediate

Enroll Now → Ask a Question

Reach us at giastusa.org@gmail.com — our team responds within 48 hours.

Full Curriculum

24 Classes Across 7 Phases

Click any phase to expand the full class list. Each class includes concept teaching, live coding, practice exercises, and an assignment.

Python Programming Fundamentals

Classes 1–4 • 4 sessions • ~9 hours • Build a rock-solid Python base from scratch

▼

CLASS 01

Introduction to AI, ML & Python

What is AI, ML, and Data Science. Real-world applications. Python installation, Jupyter Notebook & VS Code setup. Variables, input(), and your first program.

CLASS 02

Variables, Data Types & Operators

int, float, string, boolean. Type casting & type(). Arithmetic & logical operators, operator precedence. Student info and calculator programs.

CLASS 03

Conditional Statements & Loops

if / elif / else logic. for loops, while loops, break & continue. Grade calculator, prime number checker, FizzBuzz challenge.

CLASS 04

Functions, Lists & File Handling

Functions & parameters, lambda expressions. Lists, tuples, dictionaries. File handling and exception handling. Factorial function, file-based calculator.

NumPy & Pandas

Classes 5–8 • 4 sessions • ~9 hours • Master data manipulation with the most powerful Python libraries

▼

CLASS 05

NumPy Fundamentals

Arrays vs Lists, ndarray structure. Indexing, slicing, broadcasting. Matrix multiplication, transpose, vectorisation. Statistical operations on arrays.

CLASS 06

Pandas DataFrames

Series & DataFrame structures. Loading CSV files. Data inspection: head, tail, info, describe. Boolean indexing & filtering. Real student dataset analysis.

CLASS 07

Data Cleaning & Processing

Identifying missing values (NaN). dropna() vs fillna() strategies. Removing duplicates, fixing data types. Building a full preprocessing pipeline.

CLASS 08

Data Analysis Project

End-to-end pipeline: load a real student dataset, clean data, filter top performers, GroupBy analysis, sort & rank, export to CSV. Portfolio-ready Jupyter notebook.

🌟 Project Class

Data Visualisation

Classes 9–10 • 2 sessions • ~4.5 hours • Turn data into compelling visual stories

▼

CLASS 09

Matplotlib Visualisation

Figure, Axes, Subplot architecture. Line, bar, scatter, histogram, and pie charts. Customising titles, labels, colors, legends. Multi-subplot dashboard layout saved as high-res PNG.

CLASS 10

Seaborn Visualisation

Seaborn on top of Matplotlib. Heatmaps, pairplots, boxplots, violinplots. Built-in statistical summaries. Correlation visualisation. EDA on Iris dataset.

Statistics for Data Science

Classes 11–12 • 2 sessions • ~4.5 hours • Build the mathematical foundation for machine learning

▼

CLASS 11

Descriptive Statistics

Mean, median, mode — when to use each. Variance & standard deviation. Distribution concepts. Outlier detection. Z-score normalisation. Skewness analysis.

CLASS 12

Probability & Correlation

Probability basics (0 to 1). Normal distribution simulation. Pearson correlation analysis. Correlation ≠ causation. Binomial distribution. Correlation heatmap reports.

Machine Learning

Classes 13–18 • 6 sessions • ~14 hours • Build models that learn patterns from data

▼

CLASS 13

Introduction to Machine Learning

Supervised vs Unsupervised learning. Features & targets. The ML workflow. scikit-learn introduction. Iris dataset exploration.

CLASS 14

Data Preparation for ML

Train-test split strategy. Feature scaling: StandardScaler, MinMaxScaler. Encoding categorical data. One-hot encoding. Full data preparation pipeline.

CLASS 15

Linear Regression

Predicting continuous values. y = mx + b — the regression line. MSE and R² score. House price and salary prediction. Plotting and interpreting regression lines.

CLASS 16

Classification Algorithms

Logistic Regression for binary classification. KNN — the lazy learner. Decision Trees — tree-based splitting. Spam classifier, Iris flower classifier, Titanic dataset.

CLASS 17

Model Evaluation & Optimisation

Accuracy, Precision, Recall, F1-Score. Confusion matrix interpretation. Overfitting & Underfitting. Regularisation: L1 (Lasso), L2 (Ridge). Learning curves.

CLASS 18

Machine Learning Project

House price prediction system end-to-end: EDA, clean data, train Linear Regression + Decision Tree + KNN, evaluate with R² and cross-validation. Deployable prediction function.

🌟 Project Class

AI Concepts

Classes 19–21 • 3 sessions • ~7 hours • Step into neural networks, NLP, and computer vision

▼

CLASS 19

Neural Networks Basics

Artificial neurons & biological inspiration. Input, hidden, output layers. Activation functions: ReLU, Sigmoid, Softmax. Build a neural network with TensorFlow/Keras. Train on MNIST digit dataset.

CLASS 20

Natural Language Processing (NLP)

Tokenisation & stopword removal. TF-IDF for text representation. Text classification with ML. Sentiment analysis on movie reviews. Fake news classifier pipeline.

CLASS 21

Computer Vision Basics

Images as pixel arrays (H×W×C). Convolutional Neural Networks (CNN). Pooling, filters, and feature maps. Build CNN for CIFAR-10 classification. Visualise what CNN layers learn.

Final Capstone Project

Classes 22–24 • 3 sessions • ~7 hours • Design, build & present your AI portfolio project

▼

CLASS 22

Project Planning & Dataset Collection

Define your problem statement. Select a dataset (Kaggle / UCI / custom). Plan architecture & tech stack. EDA, feature selection, and engineering. Submit a 1-page project proposal.

🌟 Project Class

CLASS 23

Project Development & Deployment

Build full ML/AI pipeline. Hyperparameter tuning with GridSearchCV. Build ML web app with Streamlit. Save/load models with joblib. Deploy on Render or HuggingFace Spaces.

🌟 Project Class

CLASS 24

Final Presentation & Viva

5-minute live demo with live predictions. Walkthrough: problem → data → model → deployed app. Present results, accuracy, and insights. Technical viva Q&A. Certificate awarded.

🎯 Final Presentation

AI & Data Sciencewith Python

The Complete Path fromPython Beginner to AI Builder

24 Classes Across 7 Phases

The Full Modern AI Stack

AI & Data Science
with Python

The Complete Path from
Python Beginner to AI Builder