Data Science with AI

Module 1: Preparatory Sessions – Python – 2 Weeks (8 Hours)
Build a strong Python foundation with data structures, visualization, and numerical computing for data science.

Python Basics
OOP
NumPy
Pandas
Data Visualization

Module 2: Linear Algebra and Advanced Statistics – 1 Week (4 Hours)
Apply probability, statistics, and linear algebra concepts for ML model building and data analysis.

Module 3: Data Wrangling with SQL – 2 Week (8 Hours)
Efficiently extract, clean, and prepare structured datasets using SQL for analytics and ML workflows.

Module 4: Machine Learning – 3 Weeks (12 Hours)
Understand ML fundamentals and implement basic models for classification, regression, and clustering tasks.

Module 5: Supervised Learning – 4 Weeks (16 Hours)
Gain expertise in supervised ML algorithms and apply them to real-world case studies and forecasting.

Module 6: Unsupervised Learning – 2 Weeks (8 Hours)
Discover hidden patterns, clusters, and reduce dimensionality for better insights and model efficiency.

Module 7: Deep Learning Using TensorFlow – 2 Weeks (8 Hours)
Build and train deep learning models for computer vision and NLP tasks.

Module 8: Artificial Intelligence (AI) – 2 Weeks (8 Hours)
Work with modern AI architectures (Transformers & LLMs) and build real-world AI applications.

Final Project: Applied Machine Learning & AI – 2 Weeks (8 Hours)
Deliver a full-cycle ML/AI project — from data to deployment — showcasing business insights and technical skills.

Data Science with AI

Course Content

Module 1: Preparatory Sessions – Python – 2 Weeks (8 Hours) Build a strong Python foundation with data structures, visualization, and numerical computing for data science.

Python Basics

OOP

NumPy

Pandas

Data Visualization

Module 2: Linear Algebra and Advanced Statistics – 1 Week (4 Hours) Apply probability, statistics, and linear algebra concepts for ML model building and data analysis.

Descriptive Statistics

Probability

Inferential Statistics

Linear Algebra

Module 3: Data Wrangling with SQL – 2 Week (8 Hours) Efficiently extract, clean, and prepare structured datasets using SQL for analytics and ML workflows.

SQL Basics

Advanced SQL

UDFs

Optimization & Performance

Module 4: Machine Learning – 3 Weeks (12 Hours) Understand ML fundamentals and implement basic models for classification, regression, and clustering tasks.

Introduction to Machine Learning

Regression, Classification, Clustering

Bias-Variance Tradeoff, Cross-validation

Feature Engineering, Evaluation Metrics

Mini-Projects

Module 5: Supervised Learning – 4 Weeks (16 Hours) Gain expertise in supervised ML algorithms and apply them to real-world case studies and forecasting.

Linear Regression, Logistic Regression

Decision Trees, Random Forests

Ensemble Methods (XGBoost, LightGBM)

SVM, KNN

Time Series Forecasting (ARIMA, SARIMA, Prophet, ML-based)

Performance Metrics

Case Study Project

Module 6: Unsupervised Learning – 2 Weeks (8 Hours) Discover hidden patterns, clusters, and reduce dimensionality for better insights and model efficiency.

K-means

PCA

LDA

Dimensionality Reduction Applications

Module 7: Deep Learning Using TensorFlow – 2 Weeks (8 Hours) Build and train deep learning models for computer vision and NLP tasks.

Neural Networks Basics

TensorFlow/Keras Workflow

Deep Learning Projects (Image/Text Classification)

Module 8: Artificial Intelligence (AI) – 2 Weeks (8 Hours) Work with modern AI architectures (Transformers & LLMs) and build real-world AI applications.

LSTM

Transformers

BERT, GPT, LLMs

Hugging Face, OpenAI APIs

Hands-on Projects

Final Project: Applied Machine Learning & AI – 2 Weeks (8 Hours) Deliver a full-cycle ML/AI project — from data to deployment — showcasing business insights and technical skills.

End-to-end project integrating supervised, unsupervised & deep learning methods

Data collection, cleaning, preprocessing

Feature engineering & selection

Model building (classification, regression, clustering, forecasting)

Model evaluation & comparison (performance metrics, cross-validation)

Deployment (Flask/FastAPI/Streamlit)

Business insights & storytelling with data

Student Ratings & Reviews

LINKS

SUPPORT

GALLERY

Module 1: Preparatory Sessions – Python – 2 Weeks (8 Hours)
Build a strong Python foundation with data structures, visualization, and numerical computing for data science.

Module 2: Linear Algebra and Advanced Statistics – 1 Week (4 Hours)
Apply probability, statistics, and linear algebra concepts for ML model building and data analysis.

Module 3: Data Wrangling with SQL – 2 Week (8 Hours)
Efficiently extract, clean, and prepare structured datasets using SQL for analytics and ML workflows.

Module 4: Machine Learning – 3 Weeks (12 Hours)
Understand ML fundamentals and implement basic models for classification, regression, and clustering tasks.

Module 5: Supervised Learning – 4 Weeks (16 Hours)
Gain expertise in supervised ML algorithms and apply them to real-world case studies and forecasting.

Module 6: Unsupervised Learning – 2 Weeks (8 Hours)
Discover hidden patterns, clusters, and reduce dimensionality for better insights and model efficiency.

Module 7: Deep Learning Using TensorFlow – 2 Weeks (8 Hours)
Build and train deep learning models for computer vision and NLP tasks.

Module 8: Artificial Intelligence (AI) – 2 Weeks (8 Hours)
Work with modern AI architectures (Transformers & LLMs) and build real-world AI applications.

Final Project: Applied Machine Learning & AI – 2 Weeks (8 Hours)
Deliver a full-cycle ML/AI project — from data to deployment — showcasing business insights and technical skills.