Machine Learning and AI Engineering Bootcamp ( 10 weeks )
Prerequisite: Basic Mathematics, Basic Programming Knowledge, Working Computer, Good Internet
The Machine Learning and AI Engineering Bootcamp is an intensive hands-on program designed to equip learners with practical skills in data analysis, machine learning, and AI system development. Participants begin with Python programming and data handling, progress into exploratory data analysis and SQL, and advance to supervised and unsupervised machine learning techniques. The bootcamp also introduces model evaluation, optimization, and deployment fundamentals. By the end of the program, students will be capable of building, evaluating, and deploying real-world AI solutions across industries.
For Students Who Are Unable To Join The Live Sessions Due To Conflicting Schedules, The Recording Of Any Live Class You Miss Will Be Sent To You 3-5 Hours After The Class Ends
Why you should join this Bootcamp
live-classes
Participate in interactive instructor-led live sessions focused on practical AI and machine learning implementation.
hands-on-projects
Build real-world machine learning systems using real datasets.
python-foundation
Develop strong Python programming skills tailored for AI engineering.
data-preprocessing
Master data cleaning, feature engineering, and transformation techniques.
machine-learning-models
Implement and evaluate supervised and unsupervised learning algorithms.
model-optimization
Learn cross-validation, hyperparameter tuning, and performance improvement techniques.
deployment-basics
Understand how to package and deploy machine learning models.
certificate
Earn a certificate upon successful completion of the Machine Learning and AI Engineering Bootcamp.
Curiculum
Python Programming Foundations
IDE setup and environment configuration,Data types, variables, and control flow,Functions and data structures,Git and version control fundamentals
Numerical Computing with NumPy
NumPy arrays and vectorization,Mathematical operations,Performance optimization with arrays
Data Handling with Pandas
DataFrames and Series,Reading CSV and JSON files,Handling missing data,Aggregation and grouping
Exploratory Data Analysis (EDA)
Statistical summaries,Data visualization with Matplotlib,Advanced visualization with Seaborn,Generating EDA reports
SQL for Machine Learning
Relational database concepts,SELECT, filtering, sorting,GROUP BY and aggregates,SQL joins for multi-table analysis
Machine Learning Fundamentals
Supervised vs unsupervised learning,Train-test split and cross-validation,Overfitting and underfitting,Bias-variance tradeoff
Supervised Learning Algorithms
Linear regression,Logistic regression,Decision trees and random forests,K-Nearest Neighbors,Model evaluation metrics
Unsupervised Learning
K-Means clustering,Hierarchical clustering,Principal Component Analysis (PCA),Feature scaling techniques
Model Evaluation and Optimization
Confusion matrix and ROC-AUC,Cross-validation,Hyperparameter tuning,Model comparison strategies
AI Engineering and Deployment
Saving and loading trained models,Building simple prediction APIs,Introduction to model deployment,Ethics and responsible AI
Capstone Project
Problem definition and dataset selection,Data preprocessing and EDA,Model training and evaluation,Deployment-ready solution,Final presentation and review
Students Hands-on Projects
House Price Prediction System
Build a regression model to predict house prices based on features such as location, size, and number of rooms.
Customer Churn Prediction
Develop a classification model to predict whether a customer will leave a service using historical usage data.
Credit Risk Assessment Model
Create a machine learning model to classify loan applicants as low or high risk.
Sales Forecasting Model
Use historical sales data to build a predictive model for future revenue forecasting.
Fraud Detection System
Build a classification model to detect fraudulent transactions in financial datasets.
Customer Segmentation with Clustering
Apply K-Means clustering to segment customers into different behavioral groups.
Recommendation System Prototype
Develop a simple recommendation engine using collaborative or content-based filtering techniques.
End-to-End ML Deployment Project
Train a machine learning model and deploy it as a simple API for real-time predictions.