Chelsea AI Ventures
Technical AI Training Developer Pathway 8 Weeks (Online, approx. 4-5 hours/week) or 3 Day Intensive (In-Person, London)

Technical AI Training: From Python to Deep Learning

A hands-on, coding-intensive course to build a rock-solid foundation in machine learning and deep learning

Target Audience

The Aspiring AI Developer

Core Value

Build a rock-solid foundation in machine learning and deep learning using Python, Scikit-learn, and TensorFlow

Key Differentiator

A comprehensive, hands-on introduction to the classic AI/ML stack

Learning Objectives

  • Use core Python data science libraries (NumPy, Pandas, Matplotlib) for data manipulation and visualization
  • Implement and evaluate various supervised and unsupervised machine learning models using Scikit-learn
  • Explain the architecture and training process of a basic neural network
  • Build, train, and debug a simple deep learning model for image classification using TensorFlow and Keras
  • Describe the foundational concepts of Computer Vision (CV) and Natural Language Processing (NLP)

Prerequisites

Intermediate proficiency in Python programming.

Course Structure

Weeks 1-2: Python for AI and Data Science

Master the essential toolkit. Deep dive into NumPy for numerical operations, Pandas for data manipulation, and Matplotlib/Seaborn for visualization.

Activities:

  • Build data preprocessing pipeline from scratch
  • Create comprehensive EDA notebook

Weeks 3-4: Machine Learning Fundamentals

Core ML paradigms. Week 3: Supervised Learning algorithms and train/test/validation. Week 4: Unsupervised Learning and evaluation metrics.

Activities:

  • Implement 5 different ML algorithms
  • Build model evaluation framework

Weeks 5-6: Deep Learning and Neural Networks

Week 5: Neural network building blocks, backpropagation, gradient descent. Week 6: Hands-on with TensorFlow and Keras.

Activities:

  • Build neural network from scratch (NumPy only)
  • Recreate with TensorFlow/Keras

Week 7: Introduction to Computer Vision

Apply deep learning to images. Convolutional Neural Networks (CNNs), convolutions, and pooling layers.

Activities:

  • Build CNN for image classification
  • Visualize learned features

Week 8: Introduction to Natural Language Processing

Apply deep learning to text. Classic NLP concepts and modern approaches using word embeddings.

Activities:

  • Implement text classification pipeline
  • Explore word embeddings

Topics Covered

Python data science stack (NumPy, Pandas, Matplotlib)
Data preprocessing and feature engineering
Supervised learning algorithms
Unsupervised learning techniques
Model evaluation and metrics
Neural network fundamentals
Backpropagation and optimization
TensorFlow and Keras
Convolutional Neural Networks
Computer Vision basics
Natural Language Processing fundamentals
Word embeddings and text processing

Capstone Project

Build an end-to-end image classification pipeline: load and preprocess data, build and train a CNN model, evaluate performance, and write a comprehensive report.

Why This Course Matters

The AI revolution isn’t just about using pre-built models—it’s about understanding the fundamental principles that make those models work. While prompt engineering and API calls have their place, truly competitive AI developers understand what’s happening under the hood.

This foundational knowledge is what separates developers who can only use AI from those who can build AI. It’s the difference between being limited by existing tools and being able to create new solutions when the tools don’t exist yet.

What Makes This Course Different

Unlike courses that rush to the latest frameworks, we ensure you understand the timeless fundamentals. You’ll build neural networks from scratch before using TensorFlow, implement algorithms by hand before calling Scikit-learn, and understand the math intuitively before applying it.

This isn’t about memorizing formulas—it’s about building deep intuition. When you encounter a new AI technique next year, you’ll have the foundation to understand and implement it quickly because you understand the underlying principles.

Course Philosophy

We believe in learning by doing. Every concept is paired with hands-on implementation in a live, interactive environment. By the end, you’ll have a portfolio of working AI projects and the confidence to tackle new challenges.

We also believe in demystifying AI. Yes, there’s math involved, but we explain it intuitively, focusing on why it matters for building models, not abstract theory. You’ll understand gradients because you’ll see how they update your model’s weights, not because you memorized calculus formulas.

Who Should Take This Course

This course is perfect if you:

  • Are a solid programmer ready to transition into AI
  • Want to understand ML/DL fundamentals, not just use them
  • Learn best through hands-on coding projects
  • Need a structured path through the overwhelming AI landscape
  • Want to build AI systems, not just integrate APIs
  • Are preparing for AI engineering roles

If you’re ready to invest 8 weeks in building unshakeable AI foundations, this course provides the comprehensive training you need.

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