Course Description
Python Programming and Data Analysis Foundations
The course begins by helping students develop a strong foundation in Python programming. Key data manipulation and analysis libraries such as NumPy, Pandas, and Matplotlib are covered in depth. Students learn how to clean, organize, and visualize datasets — preparing them for more advanced concepts.
Statistics and Probability for Data Science
To effectively work with data, students are introduced to core statistical concepts and probability theories. This includes understanding distributions, hypothesis testing, and data interpretation — all essential for drawing accurate conclusions from data.
Machine Learning Algorithms and Applications
The curriculum dives into both supervised and unsupervised machine learning. Students explore and implement algorithms such as linear regression, logistic regression, decision trees, clustering, and support vector machines. Each topic is taught with hands-on practice and real-world case studies to reinforce learning.
Natural Language Processing and Time Series Analysis
Students are introduced to Natural Language Processing (NLP) to analyze text data and extract meaningful insights. Time series analysis is also included to help students master forecasting techniques for temporal data, such as sales or stock trends.
Deep Learning with TensorFlow and Keras
As part of the advanced modules, students get an introduction to deep learning. Using TensorFlow and Keras, they learn to build neural networks and apply deep learning concepts, adding powerful tools to their machine learning skillset.
Hands-On Projects and Industry Use Cases
Throughout the course, students work on real-time projects like customer segmentation, churn prediction, fraud detection, and recommendation systems. These hands-on experiences help students build a strong, job-ready portfolio.
Career Support and Freelance Readiness
The course provides end-to-end career support to help students break into the data science field. This includes resume building, interview preparation, career counseling, and freelance project guidance — empowering students to start earning independently and confidently.
Course Curriculum
Module 1 - Intoduction to Data Science, AI, ML, DL, and NLP|Roadmap
Topics Covered
- What is Data Science, AI, ML, DL,NLP?
- How they are different & their real-world applications
- Machine Learning Workflow: Data Collection - Preprocessing - Model Selection - Training - Evaluation
- Roadmap to become a Machine Learning Engineer
Tools:
- Python
- Jupyter Notebook/Google Colab
Sample Project:
- Creating a roadmap for a Data Science Career
Module 2 - Introduction to Python for Machine Learning
Topic Covered
- Python Basics(Data Types, Loops, Functions)
- Introduction to Google Colab, Jupyter Notebook, Pycharm, IDLE
- Installing Python Libraries: Numpy, Pandas, Matplotlib, Scikit-learn
Tools:
- Python
- Jupyter Notebook/Google Colab
Sample Project:
- Loading, analyzing and visualizing a dataset using Pandas & Matplotlib
Supervised Learning
Module 3 - Advertisement Sales Prediction(LOGISTIC REGRESSION)
Topics Covered:
- Introduction to Logistic Regression
- Feature Selection & Data Preprocessing
- Model Training & Performance Metrics
Tools:
- Scikit-Learn
- Pandas, Matplotlib
Sample Project:
- Predicting whether a customer will buy a product based on past data
Module 4 - Salary Estimation(K-NEAREST NEIGHBOR)
Topics Covered:
- How KNN works
- Choosing the Best K Value
Tools:
- Scikit-learn
Sample Project:
- Estimating salary based on experience
Module 5 - Character Recognition(SUPPORT VECTOR MACHINE)
Topics Covered:
- Understanding SVM
- Hyperplane & Kernel Tricks
Tools:
- Scikit-learn
- OpenCV
Module 6 - Titanic Survival Prediction(NAIVE BAYES)
Topic Covered:
- Bayes Theorem
- Probabilistic Classification
Tools:
- Scikit-learn
- Pandas
Sample Project
- Predicting survival chances on Titanic dataset
Module 7 - Leaf Detection(DECISION TREE)
Topic Covered
- How Decision Trees Work
- Feature Splitting
Sample Project
- Classifying different types of leaves
Module 8 - Handwritten Digit Recognition(RANDOM FOREST)
Sample Project
- Recognizing handwritten digits using MNIST dataset
Module 9 - Evaluating Classification Model Performance
Topic Covered
- Precision, Recall, F1-Score, Confusion Matrix
Sample Project
- Comparing different classification models
Module 10 - Classification Model Selection for Breast Cancer
Sample Project
- Finding the best classification model for breast cancer detection
Module 11 - House Price Prediction(LINEAR REGRESSION - Single Variable)
Sample Project
- Predicting house prices based on one feature(e.g., area in sqft)
Module 12 - Exam Mark Prediction(LINEAR REGRESSION - Multiple Variables)
Sample Project
- Predicting student marks based on study time & other factors
Module 13 - Predicting Previous Salary(POLYNOMIAL REGRESSION)
Sample Project
- Estimating previous salary of new employees
Module 14 - Stock Price Prediction(SUPPORT VECTOR REGRESSION)
Sample Project
- Predicting stock prices using Support Vector Regression
Module 15 - Height Prediction from Age(DECISION TREE REGRESSION)
Sample Project
- Estimating height based on age
Module 16 - Car Price Prediction(RANDOM FOREST)
Sample Project
- Predicting car prices based on features like mileage, year, brand
Module 17 - Heart Disease Prediction(NAIVE BAYES)
Sample Project
- Predicting heart disease using patient data
Module 18 - Evaluating Regression Model Performance
Topic Covered:
- Mean Squared Error, R-squared, MAE
Sample Project
- Comparing different regression models
Module 19 - Regression Model Selection for Engine Energy Prediction
Sample Project
- Finding the best regression model for energy prediction
Unsupervised Learning
Module 20 - Customer Pattern Identification(K-MEANS CLUSTERING)
Sample Project
- Clustering customers based on spending behaviour
Module 21 - Customer Spending Analysis(HIERARCHICAL CLUSTERING)
Sample Project
- Grouping customers based on spending habits
Module 22 - Leaf Types Data Visualization(PRINCIPAL COMPONENT ANALYSIS-PCA)
Sample Project
- Visualizing high-dimensional leaf data in 2D
Module 23 - Finding Similar Movies(SINGULAR VALUE DECOMPOSITION-SVD)
Sample Project
- Building a movie recommendation system
Module 24 - Market Basket Analysis(APRIORI ALGORITHM)
Sample Project
- Identifying frequently bought product combinations
Reinforcement Learning
Module 25 - Web Ads Click-through Rate Optimization(UPPER CONFIDENCE BOUND - UCB)
Sample Project
- Optimizing Online Ad Placement
Module 26 - AI Snake Game(REINFORCEMENT LEARNING)
Sample Project
- Creating a snake game that learns to play using RL
Advanced Topics
Module 27 - Sentiment Analysis(NLP)
Sample Project
- classifying positive & negative movie reviews
Module 28 - Breast Cancer Tumor Prediction (XGBOOST)
Sample Project
- Detecting cancerous tumors with high accuracy
Module 29 - Pima Indians Diabetes Classification
Sample Project
- Predicting diabetes using medical data
Module 30 - COVID-19 Detection using CNN
Sample Project
- Classifying COVID-19 X-ray images using Deep Learning
Our Progress
Consultation
