excendra logo
Get Started
  • COURSES DETAILS

AI / ML TRAINING.

Home 

Courses Detail

laptop

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

  • Fundamentals of Artificial Intelligence and Machine Learning
  • Data preprocessing and feature engineering
  • Supervised and unsupervised learning algorithms
  • Model training, evaluation, and optimization
  • Introduction to neural networks and deep learning
  • Real-world applications and case studies

Lessons of the Course

MODULE 1: FOUNDATIONS & CLASSICAL MACHINE LEARNING
 
✓
Python Essentials
  • Variables, loops, functions, and OOP basics
  • Library: Python 3.9+
  • Project: Build a CLI calculator with error handling
✓
Data Manipulation with NumPy & Pandas
  • Arrays vs DataFrames, filtering, grouping, merging
  • Libraries: NumPy, Pandas
  • Project: Clean and analyze a messy sales dataset
✓
Version Control with Git
  • Commits, branches, and GitHub workflows
  • Tools: Git, GitHub/GitLab
  • Project: Create a structured portfolio repository
✓
Visualization & Exploratory Data Analysis (EDA)
  • Matplotlib: Custom plots, subplots, styling
  • Seaborn: Statistical plots, heatmaps, pairplots
  • Libraries: Matplotlib, Seaborn
  • Project: Visualize COVID-19 trends using multiple chart types
  • Interactive: Plotly/Dash, correlation analysis, outlier detection
  • Project: Build an interactive housing data dashboard
  • EDA Case Study: End-to-end pipeline using Jupyter Notebook
  • Deliverable: Professional EDA report with insights
✓
Mathematics & Classical ML Algorithms
  • Learning Outcomes: Understand core math to implement and evaluate ML models using scikit-learn
  • Linear Algebra: Vectors, matrices, dot products using NumPy
  • Calculus: Derivatives, gradients, gradient descent visualization
  • Statistics & Probability: Distributions, Bayes' Theorem, hypothesis testing
  • Library: NumPy, SciPy Stats
  • Project: Implement linear regression from scratch and compare with scikit-learn
✓
Supervised & Unsupervised Learning
  • Linear & Logistic Regression, evaluation metrics (MSE, accuracy, precision/recall)
  • Decision Trees, Random Forest, SVM
  • K-Means, PCA, Hierarchical Clustering
  • Libraries: scikit-learn
  • Projects: Customer churn prediction, credit scoring, customer segmentation
  • Model Evaluation: Cross-validation, hyperparameter tuning (GridSearchCV)
  • Tools: scikit-learn, Yellowbrick
  • Project: Optimize a full ML pipeline end-to-end
✓
Capstone Project: Real Estate Price Prediction & Recommendation System
  • Predict house prices and recommend similar properties using clustering
  • Pipeline: Data cleaning → EDA → Feature engineering → Modeling → Evaluation
  • Deliverable: GitHub repository with code and a Streamlit app demo
MODULE 2: DEEP LEARNING & ADVANCED TOPICS
 
✓
Introduction to Neural Networks
  • Perceptrons, activation functions, forward and backward propagation
  • Library: TensorFlow/Keras
  • Project: Build a neural network from scratch using NumPy
✓
Building with Keras/TensorFlow
  • Sequential and Functional APIs, callbacks, early stopping
  • Libraries: TensorFlow 2.x, Keras
  • Project: MNIST digit classification (98%+ accuracy)
✓
Optimization & Regularization
  • Dropout, Batch Normalization, and optimizers
  • Visualization: TensorBoard for experiment tracking
  • Tools: TensorBoard, Weights & Biases (optional)
✓
Hyperparameter Tuning at Scale
  • Automated tuning using Keras Tuner
  • Library: Keras Tuner
  • Project: Optimize neural network architecture automatically
✓
Convolutional Neural Networks (CNNs)
  • Convolution layers, pooling, modern architectures (ResNet, EfficientNet)
  • Project: Cats vs Dogs classifier with data augmentation
✓
Transfer Learning
  • Fine-tuning pre-trained models (VGG16, ResNet50)
  • Libraries: TensorFlow Hub, tf.keras.applications
  • Project: Medical image classification with limited data
✓
Introduction to NLP & RNNs
  • Text preprocessing, word embeddings, simple RNNs
  • Libraries: TensorFlow, NLTK, spaCy basics
  • Project: Sentiment analysis on movie reviews
✓
Time Series & Sequence Models
  • LSTM and GRU for time series prediction
  • Project: Stock price prediction (simplified)
✓
Capstone: Intelligent Image Recognition System
  • Multi-class image classification using CNNs
  • Full deployment with FastAPI, Docker, and Hugging Face Spaces
  • Deliverable: Live web application with model inference
MODULE 3:CAPSTONE & SPECIALIZATION
 
✓
Capstone Project Sprint
  • Structure: Choose from curated project tracks or bring your own idea
  • Available Tracks:
    • Computer Vision: Object detection with YOLO
    • NLP: Text classification/chatbot using transformers
    • Time Series: Forecasting with LSTM/Prophet
    • Recommendation Systems: Collaborative filtering + deep learning
✓
Project Planning & Development
  • Problem definition, data collection, and literature review
  • MVP development and baseline model implementation
  • Mentorship: Daily standups, code reviews, troubleshooting sessions
✓
Advanced Implementation & Optimization
  • Advanced modeling and hyperparameter tuning
  • Deployment, documentation, and presentation preparation
  • Tools: Flexible stack based on student choice and mentor guidance
✓
Transformers & Hugging Face
  • BERT, GPT basics, and working with pre-trained models
  • Libraries: Transformers, Hugging Face Hub
  • Project: Fine-tune BERT for a custom task
✓
Advanced Visualization
  • Interactive dashboards using Streamlit and Gradio
  • Libraries: Streamlit, Gradio
  • Project: Build an ML web app in 2 hours
✓
MLOps Tools
  • DVC for data versioning and MLflow pipelines
  • Tools: DVC, MLflow
  • Project: Build a reproducible ML pipeline
✓
Production Considerations
  • Model quantization, ONNX format, and edge deployment
  • Libraries: ONNX Runtime, TensorFlow Lite
  • Demo: Deploy a model to mobile
✓
Ethics in AI
  • Bias detection, fairness metrics, and explainable AI (SHAP/LIME)
  • Libraries: SHAP, Fairlearn
  • Case Study: Analyze model bias in a hiring dataset
✓
Portfolio Development & Final Presentations
  • Portfolio polishing, GitHub READMEs, LinkedIn optimization
  • Final project presentations to an industry panel
  • Graduation, certifications, and next steps guidance
TOOLS & LIBRARIES SUMMARY
 
✓
Tools & Libraries Summary
CategoryTools & Libraries
ProgrammingPython 3.9+
ML/DLscikit-learn, TensorFlow 2.x/Keras, PyTorch (optional)
DataNumPy, Pandas, SQL basics
VisualizationMatplotlib, Seaborn, Plotly, Streamlit
DeploymentFastAPI, Docker, Hugging Face Spaces
Project ManagementGitHub, Notion/Trello, Slack/Discord
✓
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
✓
Differentiators of This Program
  • Zero to Deployment in 12 weeks
  • Industry Tools First – Teaching real-world tools
  • Project Portfolio – 8+ completed projects for GitHub
  • Deployment Focus – Every major model gets deployed

What Our Students Say

Man standing outdoors in a green mountainous landscape, wearing a blue sleeveless jacket and jeans.
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
intern
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.
Share On:

Instructor

teacher

Fred Adams

Senior Software & Enterprise Architect

This course includes:

  • 54 Hours of Learn-Anywhere Videos
  • Live & Recorded Access
  • Real-World Projects
  • Certified Learning Achievement

₹3599

₹8000
Register
Course image

AI / ML INTERNSHIP

Gain hands-on experience in AI/ML by building models, analyzing data, and creating real-world intelligent solutions.

Join Now