AI/ML Training & Internship Program

AI/ML Training & Internship

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About The Course

Artificial Intelligence and Machine Learning focus on building systems that can learn from data, recognize patterns, and make intelligent decisions. This course introduces the core concepts of AI and ML, helping students understand how modern applications like recommendation systems, chatbots, and predictive analytics work. With a practical approach, learners gain hands-on experience in applying algorithms to real-world problems and developing data-driven solutions.

Key points

Lessons of the Course

MONTH 1: FOUNDATIONS & CLASSICAL MACHINE LEARNING

Week 1-2: Python & Data Foundation

Learning Outcomes: Write production-ready Python, manipulate data efficiently, create compelling visualizations, and use Git for collaboration.

Week 1: Python for AI & Git Fundamentals

Day 1-2: Python Essentials
- Variables, loops, functions, OOP basics
- Library: Python 3.9+
- Project: Build a CLI calculator with error handling
Day 3-5: Data Manipulation with NumPy & Pandas
- Arrays vs DataFrames, filtering, grouping, merging
- Libraries: NumPy, Pandas
- Project: Clean and analyze a messy sales dataset
Day 5: Version Control with Git
- Commits, branches, GitHub workflows
- Tool: Git, GitHub/GitLab
- Project: Create portfolio repository with proper structure

Week 2: Visualization & Exploratory Data Analysis (EDA)

Day 1-2: Static Visualization
- Matplotlib: Custom plots, subplots, styling
- Seaborn: Statistical plotting, heatmaps, pairplots
- Libraries: Matplotlib, Seaborn
- Project: Visualize COVID-19 trends with multiple chart types
Day 3-4: Interactive Visualization & EDA
- Plotly/Dash basics, correlation analysis, outlier detection
- Libraries: Plotly, Pandas Profiling
- Project: Interactive dashboard for housing data
Day 5: EDA Case Study
- Complete EDA pipeline on a real-world dataset
- Tool: Jupyter Notebook
- Deliverable: Professional EDA report with insights

Week 3-4: Mathematics & Classical ML Algorithms

Learning Outcomes: Understand just-enough math to implement algorithms, build and evaluate ML models using scikit-learn.

Week 3: Minimum Required Mathematics

Day 1: Linear Algebra for ML
- Vectors, matrices, dot products - implemented in NumPy
- Application: How recommendation systems use matrix operations
- Library: NumPy for all math implementations
Day 2: Calculus Essentials
- Derivatives, partial derivatives, gradients
- Visualization: Gradient descent with animation
- Tool: Custom Python visualization
Day 3: Statistics & Probability
- Distributions, Bayes' Theorem, hypothesis testing
- Application: Spam filter probability calculation
- Library: SciPy Stats
Day 4-5: Math in Practice
- Implement linear regression from scratch using only NumPy
- Compare with the scikit-learn implementation
- Project: Build a regression model without ML libraries

Week 4-5: Supervised & Unsupervised Learning

Day 1-3: Supervised Learning I
- Linear/Logistic Regression, Evaluation Metrics (MSE, Accuracy, Precision/Recall)
- Library: scikit-learn
- Project: Customer churn prediction
Day 4-5: Supervised Learning II
- Decision Trees, Random Forest, SVM
- Focus: When to use which algorithm
- Project: Credit scoring system
Day 6-7: Unsupervised Learning
- K-Means Clustering, PCA, Hierarchical Clustering
- Library: scikit-learn
- Project: Customer segmentation for marketing
Day 8-10: Model Evaluation & Selection
- Cross-validation, hyperparameter tuning (GridSearchCV)
- Bias-Variance tradeoff in practice
- Tool: scikit-learn, Yellowbrick for visualization
- Project: Optimize a model pipeline end-to-end

End of Month 1 Project: Real Estate Price Prediction & Recommendation System

Predict house prices (regression) + Recommend similar houses (clustering)
Full pipeline: Data cleaning → EDA → Feature engineering → Multiple models → Evaluation
Deliverable: GitHub repo with code + Streamlit app demo

MONTH 2: DEEP LEARNING & ADVANCED TOPICS

Week 6-8: Neural Networks & Deep Learning

Learning Outcomes: Build and train neural networks, understand CNN/RNN architectures, and use transfer learning.

Week 6-7: Neural Networks Fundamentals

Day 1-3: Introduction to Neural Networks
- Perceptrons, activation functions, forward/backward propagation
- Library: TensorFlow/Keras
- Project: Build NN from scratch using NumPy
Day 4-5: Building with Keras/TensorFlow
- Sequential/Functional API, callbacks, early stopping
- Library: TensorFlow 2.x, Keras
- Project: MNIST digit classification (98%+ accuracy)
Day 6-7: Optimization & Regularization
- Dropout, BatchNorm, different optimizers
- Visualization: TensorBoard for tracking experiments
- Tool: TensorBoard, Weights & Biases (optional)
Day 8-10: Hyperparameter Tuning at Scale
- Keras Tuner, automated hyperparameter optimization
- Library: Keras Tuner
- Project: Optimize network architecture automatically

Week 8: Advanced Neural Architectures

Day 1-2: Convolutional Neural Networks (CNNs)
- Conv layers, pooling, modern architectures (ResNet, EfficientNet)
- Project: Cats vs Dogs classifier with data augmentation
Day 3: Transfer Learning
- Fine-tuning pre-trained models (VGG16, ResNet50)
- Library: TensorFlow Hub, tf.keras.applications
- Project: Medical image classification with limited data
Day 4: Introduction to NLP & RNNs
- Text preprocessing, word embeddings, simple RNNs
- Library: TensorFlow, NLTK/spaCy basics
- Project: Sentiment analysis on movie reviews
Day 5: Time Series & Sequence Models
- LSTM, GRU for time series prediction
- Project: Stock price prediction (simplified)

End of Month 2 Project: Intelligent Image Recognition System

Multi-class image classification with CNNs
Full deployment: FastAPI + Docker + Hugging Face Spaces
Deliverable: Live web application with model inference

MONTH 3: CAPSTONE & SPECIALIZATION

Week 9-10: Capstone Project Sprint

Structure: Students choose from curated project tracks or bring their own idea.

Available Tracks:

Computer Vision: Object detection system with YOLO
NLP: Text classification/chatbot with transformers
Time Series: Forecasting system with LSTM/Prophet
Recommendation System: Collaborative filtering + deep learning

Week 9: Project Planning & Development

Days 1-2: Problem definition, data collection, literature review
Days 3-5: MVP development, baseline model implementation
Mentorship: Daily standups, code reviews, and troubleshooting sessions

Week 10: Advanced Implementation & Optimization

Days 1-3: Advanced modeling, hyperparameter optimization
Days 4-5: Deployment, documentation, presentation preparation
Tools: Student's choice from learned stack + mentor recommendations

Week 11-12: Advanced Topics & Industry Readiness

Week 11: Advanced Libraries & Frameworks

Day 1: Transformers & Hugging Face
- BERT, GPT basics, using pre-trained models
- Library: Transformers, Hugging Face Hub
- Project: Fine-tune BERT for a custom task
Day 2: Advanced Visualization
- Interactive dashboards with Streamlit/Gradio
- Library: Streamlit, Gradio
- Project: Build ML web app in 2 hours
Day 3: MLOps Tools
- DVC for data versioning, MLflow pipelines
- Tools: DVC, MLflow
- Project: Reproducible ML pipeline
Day 4: Production Considerations
- Model quantization, ONNX format, edge deployment
- Library: ONNX Runtime, TensorFlow Lite
- Demo: Deploy model to mobile
Day 5: Ethics in AI
- Bias detection, fairness metrics, explainable AI (SHAP/LIME)
- Library: SHAP, Fairlearn
- Case Study: Analyze model bias in hiring dataset

Week 12: Portfolio Development & Final Presentations

Days 1-2: Portfolio polishing, GitHub READMEs, LinkedIn optimization
Days 3-4: Final project presentations to industry panel
Day 5: Graduation, certifications, next steps guidance

TOOLS & LIBRARIES SUMMARY

Core Stack:

Programming: Python 3.9+
ML/DL: scikit-learn, TensorFlow 2.x/Keras, PyTorch (optional)
Data: NumPy, Pandas, SQL basics
Visualization: Matplotlib, Seaborn, Plotly, Streamlit
Deployment: FastAPI, Docker, Hugging Face Spaces

Project Management:

GitHub for code hosting
Notion/Trello for project tracking
Slack/Discord for communication

ASSESSMENT STRUCTURE

Weekly Projects (40%) - Hands-on implementation
Mid-term Project (25%) - End-to-end ML system
Capstone Project (35%) - Industry-level solution with documentation

What Our Students Say

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Learning MERN Stack at Excendra was transformative. Practical projects, clear explanations, and industry-focused mentorship helped me develop strong full-stack skills and confidence to build professional applications.

Mehran Muzaffar Software Developer COMTECH GROUP

I completed Full Stack Development training at Excendra. Practical projects and clear teaching built my skills and confidence. With their support, I secured a job soon after the course and recommend Excendra for tech career.

Hurain Javaid Software Engineer Sun-Net Inc.