AI ML with Python and Deep Learning

Artificial Intelligence (AI)

COURSE OVERVIEW

This eight-day intensive program provides a comprehensive roadmap for mastering the modern AI stack. Participants will move from the mathematical and programming foundations of Machine Learning (ML) to the architectural complexities of Deep Learning (DL). By leveraging Python’s powerful ecosystem—including Scikit-Learn, TensorFlow, and Keras—students will learn how to transform raw data into intelligent, predictive models. The course culminates in an end-to-end project where participants build, optimize, and prepare a real-world AI solution for deployment.

COURSE OBJECTIVES

By the end of this 8-day program, participants will be able to:

Construct Predictive Models: Build and evaluate robust supervised learning algorithms for regression and classification tasks.
Discover Hidden Patterns: Apply unsupervised learning techniques like clustering and dimensionality reduction to unlabelled datasets.
Architect Neural Networks: Design, train, and fine-tune deep learning models using backpropagation and various activation functions.
Leverage Modern Frameworks: Master industry-standard libraries including NumPy, Pandas, TensorFlow, and Keras.
Process Specialized Data: Implement Convolutional Neural Networks (CNNs) for images and Recurrent Neural Networks (RNNs) for sequential data.
Engineer End-to-End Pipelines: Execute a complete ML lifecycle from data preprocessing and feature engineering to model deployment concepts.

Duration: 8 Days / 64 Hours

Delivery Method: Classroom-based, Virtual Instructor Led Training

COURSE OUTLINE

Week 1: Foundations & Classical Machine Learning

Day 1: Introduction to AI and Machine Learning

The AI Landscape: Distinguishing between Narrow AI, General AI, and the role of Machine Learning within the ecosystem.
Learning Paradigms: Comparative study of Supervised, Unsupervised, and Reinforcement Learning.
Python Environment Architecture: Configuring Virtual Environments, Jupyter Notebooks, and essential AI libraries.
Data Handling Basics: Introduction to data structures and loading external datasets (CSV, JSON, SQL).

Day 2: Data Science Essentials with Python

Numerical & Tabular Processing: Mastering NumPy for vectorization and Pandas for data frame manipulation.
The Preprocessing Pipeline: Handling missing values, encoding categorical variables, and feature scaling.
Feature Engineering: Deriving new insights through transformation and selection techniques.
Exploratory Data Analysis (EDA): Visualizing data distributions and correlations using Matplotlib and Seaborn.

Day 3: Supervised Learning & Evaluation

Regression Analysis: Implementing Linear and Polynomial models to predict continuous outcomes.
Classification Strategies: Deep dive into Logistic Regression, Decision Trees, and Random Forests.
The Metrics of Success: Understanding Confusion Matrices, Accuracy, Precision-Recall, and F1-Score.
Hands-on Labs: Developing a predictive model to solve a binary classification business challenge.

Day 4: Unsupervised Learning & Complexity Reduction

Clustering Techniques: Grouping data with K-Means and Hierarchical clustering.
Dimensionality Reduction: Using PCA (Principal Component Analysis) to simplify datasets without losing variance.
Association Rule Learning: Discovering relationships in large-scale transactional data.
Practical Exercises: Segmenting customer data into actionable behavioral groups.

Week 2: Deep Learning & Production Integration

Day 5: Deep Learning Fundamentals

Biological to Artificial: The structure of a Neural Network and the role of the Perceptron.
Activation Functions: Implementing ReLU, Sigmoid, and Tanh to introduce non-linearity.
Backpropagation & Optimization: How models learn through Gradient Descent and error correction.
TensorFlow & Keras Intro: Building your first "Sequential" model in a high-level API environment.

Day 6: Advanced Neural Architectures

Computer Vision with CNNs: Convolutional layers, pooling, and spatial hierarchy for image recognition.
Sequence Modeling with RNNs: Understanding "Memory" in models using LSTMs and GRUs for time-series or text.
Transfer Learning: Utilizing pre-trained models (VGG, ResNet) to accelerate specialized development.
Model Optimization: Preventing overfitting with Dropout, Regularization, and Early Stopping.

Day 7: AI Project Development & Pipelines

End-to-End ML Pipelines: Orchestrating the flow from raw data ingestion to prediction.
Model Deployment Concepts: Introduction to Model Serving, APIs (FastAPI/Flask), and Containerization (Docker).
Performance Tuning: Hyperparameter optimization and cross-validation for maximum reliability.
Practical Implementation: Building a robust validation strategy for production-grade AI.

Day 8: Capstone Project & Evaluation

AI Solution Implementation: Final development time for a self-selected or assigned technical problem.
Project Presentations: Demonstrating the technical architecture and results to peers.
Assessments & Feedback: Comprehensive review of the course objectives and individual performance evaluations.
Roadmap Ahead: Certification paths and emerging trends in Deep Learning.

REGISTER NOW