AIML Bootcamp

What you'll Build

Multiple End-to-End Machine Learning Systems
From data preprocessing and EDA to supervised and unsupervised ML models.

Deep Learning & Neural Network Models
ANN and CNN models with optimization, regularization, and performance tuning.

Advanced Computer Vision Applications
Image processing, classification, object detection, and segmentation systems.

One Major Industry-Grade AI/ML Project
Complete with dataset, trained model, evaluation, thesis, and final presentation.

Week 1: Python Foundations for AI

Python essentials, data types, control flow, functions & OOP
Modular coding, error handling, reusable helpers
NumPy arrays, broadcasting, vectorization & matrix operations

Week 2: Data Handling & ML Foundations

Pandas for data wrangling, preprocessing & feature preparation
Data visualization using Matplotlib & Seaborn
ML pipeline fundamentals, metrics, train–test split

Week 3–4: Machine Learning Foundations

Supervised learning: Decision Trees, Random Forest, Gradient Boosting
Evaluation metrics: ROC, AUC, Precision, Recall, F1
Unsupervised learning: PCA, dimensionality reduction & clustering
Feature engineering, scaling & model improvement techniques

Week 5: Neural Networks Fundamentals

Perceptron & ANN architecture
Forward & backward propagation
Loss functions, optimizers & regularization techniques

Week 6: Deep Learning Architecture Mastery

Multi-layer neural networks & weight initialization
Batch normalization, dropout & early stopping
Model saving, callbacks & CNN fundamentals

Week 7: Computer Vision Foundations

Digital images, pixels & color spaces
Filters, convolutions & edge detection
Image transformations & histogram analysis using OpenCV

Week 8: Transfer Learning & Optimization

Pretrained models & feature extractors
Fine-tuning MobileNet, ResNet & EfficientNet
Model compression & quantization techniques

Week 9: Object Detection Mastery

Object detection theory (R-CNN → YOLO evolution)
YOLO implementation & detection pipelines
Detection metrics including mAP & evaluation

Week 10: Segmentation & Advanced Computer Vision

Semantic & instance segmentation
U-Net & Mask R-CNN architectures
Advanced CV experimentation & analysis

Week 11–12: Major Project, Thesis & Presentation

Major project planning, dataset preparation & execution
Model training, testing & evaluation
Final project demo, thesis submission & viva

Learning Outcomes

Strong End-to-End AI/ML Expertise

Deep understanding of ML, DL, and Computer Vision systems with practical execution.

Extensive Project Portfolio

8 mini-projects plus one major AI/ML capstone project ready for GitHub and interviews.

Hands-On Mastery of Industry Tools

Experience with Python, NumPy, Pandas, Scikit-learn, TensorFlow/Keras & OpenCV.

Production-Level Model Development Skills

Ability to train, evaluate, optimize, debug, and compare AI models confidently.

Career & Professional Readiness

Resume building, interview preparation, communication skills, and portfolio positioning.

Certified 3-Month Training + Internship Completion

Successful completion of a 90-Days AIML Training + Internship Program with certification.

AI & ML Industrial Training Program