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

MODULE 1:FOUNDATIONS & CLASSICAL MACHINE LEARNING

Python & Data Foundation (Weeks 1-2)

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Learning Outcomes

Write production-ready Python
Manipulate data efficiently
Create compelling visualizations
Use Git for collaboration

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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

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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

MODULE 2: Mathematics & Classical ML Algorithms

Machine Learning Foundations (Weeks 3-5)

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Learning Outcomes

Understand just-enough math to implement algorithms
Build and evaluate ML models using scikit-learn

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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

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Weeks 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

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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

MODULE 3: DEEP LEARNING & ADVANCED TOPICS

Week 6-8: Neural Networks & Deep Learning

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Learning Outcomes

Build and train neural networks
Understand CNN and RNN architectures
Apply transfer learning techniques

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Week 6-7: Neural Network Fundamentals

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Day 1-3: Introduction to Neural Networks

Perceptrons, activation functions
Forward and backward propagation
Library: TensorFlow / Keras
Project: Build neural network from scratch using NumPy

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Day 4-5: Building with Keras/TensorFlow

Sequential & Functional API
Callbacks and early stopping
Library: TensorFlow 2.x, Keras
Project: MNIST digit classification (98%+ accuracy)

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Day 6-7: Optimization & Regularization

Dropout, Batch Normalization
Different optimizers (Adam, SGD, RMSprop)
Visualization: TensorBoard experiment tracking
Tools: TensorBoard, Weights & Biases (optional)

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Day 8-10: Hyperparameter Tuning at Scale

Keras Tuner for automated optimization
Architecture search techniques
Project: Automatically optimize neural network structure

Week 8: Advanced Neural Architectures

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Day 1-2: Convolutional Neural Networks (CNNs)

Convolution layers and pooling
Modern architectures: ResNet, EfficientNet
Project: Cats vs Dogs classifier with data augmentation

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Day 3: Transfer Learning

Fine-tuning pre-trained models (VGG16, ResNet50)
Libraries: TensorFlow Hub, tf.keras.applications
Project: Medical image classification with limited data

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Day 4: Introduction to NLP & RNNs

Text preprocessing and word embeddings
Simple RNN architectures
Libraries: TensorFlow, NLTK / spaCy basics
Project: Movie review sentiment analysis

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Day 5: Time Series & Sequence Models

LSTM and GRU networks
Sequence modeling for forecasting
Project: Simplified stock price prediction

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End of Month 2 Project

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

MODULE 4: CAPSTONE & SPECIALIZATION

Week 9-10: Capstone Project Sprint

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Structure

Students choose from curated project tracks or bring their own idea
Full mentor-guided execution

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Available Tracks

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

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Week 9: Project Planning & Development

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

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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

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Week 11: Advanced Libraries & Frameworks

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Day 1: Transformers & Hugging Face

BERT, GPT fundamentals & pre-trained models
Library: Transformers, Hugging Face Hub
Project: Fine-tune BERT for a custom NLP task

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Day 2: Advanced Visualization

Interactive dashboards with Streamlit / Gradio
Project: Build ML web app in under 2 hours

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Day 3: MLOps Tools

DVC for data versioning
MLflow pipelines for experiment tracking
Project: Create reproducible ML workflow

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Day 4: Production Considerations

Model quantization & ONNX format
Edge deployment basics
Libraries: ONNX Runtime, TensorFlow Lite
Demo: Deploy model to mobile

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Day 5: Ethics in AI

Bias detection & fairness metrics
Explainable AI (SHAP, LIME)
Library: SHAP, Fairlearn
Case Study: Bias analysis in hiring dataset

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Week 12: Portfolio & 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-step guidance

TOOLS & LIBRARIES SUMMARY

Core Stack & Tools

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Core Stack

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

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Project Management Tools

GitHub: Version control & code hosting
Notion / Trello: Task planning & milestone tracking
Slack / Discord: Team communication & mentorship

Assessment Structure

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Grading Breakdown

Weekly Projects (40%): Hands-on implementation and practical exercises
Mid-term Project (25%): Complete end-to-end machine learning system
Capstone Project (35%): Industry-level solution with full documentation and deployment

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Evaluation Criteria

Code quality and documentation
Model performance and optimization
Problem-solving approach
Deployment readiness
Presentation and communication skills

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.