INTERMEDIATE LEVEL
🚀 Building Your AI Knowledge
For those who understand the basics and want to dive deeper into machine learning, neural networks, and practical AI development.
📚7 Steps
⏱️4-6 weeks
🎯Hands-on Focus
📋 Prerequisites
Before starting the intermediate level, make sure you have:
- ✓Completed the Basic Level or equivalent knowledge
- ✓Basic understanding of Python programming
- ✓Familiarity with AI concepts and terminology
1
Strengthening Your Python Skills for AI
Focus on Python libraries crucial for AI development and data manipulation.
What you'll learn:
- •NumPy for numerical operations
- •Pandas for data manipulation
- •Matplotlib/Seaborn for visualization
- •Jupyter notebooks for experimentation
⏱️ Estimated time: 4-6 hoursStart Step 1
2
Understanding Machine Learning Fundamentals
Learn core ML concepts including supervised and unsupervised learning.
What you'll learn:
- •Supervised Learning (Regression, Classification)
- •Unsupervised Learning (Clustering, Dimensionality Reduction)
- •Model training and evaluation
- •Overfitting and underfitting concepts
⏱️ Estimated time: 6-8 hoursStart Step 2
3
Introduction to Neural Networks and Deep Learning
Understand the basic structure of neural networks and what makes deep learning 'deep'.
What you'll learn:
- •Neural network architecture
- •Neurons, layers, and activation functions
- •Backpropagation and gradient descent
- •Introduction to deep learning frameworks
⏱️ Estimated time: 5-7 hoursStart Step 3
4
Your First Machine Learning Projects
Apply your knowledge through hands-on projects with real datasets.
What you'll learn:
- •House price prediction (Regression)
- •Handwritten digit classification (MNIST)
- •Basic sentiment analysis
- •Model evaluation and improvement
⏱️ Estimated time: 8-10 hoursStart Step 4
5
Exploring Key AI Frameworks and Tools
Get familiar with popular ML/DL frameworks and their use cases.
What you'll learn:
- •TensorFlow and Keras basics
- •PyTorch fundamentals
- •Scikit-learn for traditional ML
- •Choosing the right framework
⏱️ Estimated time: 6-8 hoursStart Step 5
6
Introduction to Large Language Models (LLMs)
Understand how LLMs work and learn about prompt engineering.
What you'll learn:
- •What are Large Language Models?
- •Transformer architecture basics
- •Prompt engineering techniques
- •Fine-tuning vs. prompt engineering
⏱️ Estimated time: 4-6 hoursStart Step 6
7
Introduction to AI Image Generation
Learn the basics of diffusion models and text-to-image generation.
What you'll learn:
- •How diffusion models work
- •Text-to-image generation process
- •Popular image generation models
- •Prompt engineering for images
⏱️ Estimated time: 3-5 hoursStart Step 7