Intro to Python for Machine Learning + Understanding AI vs ML vs Deep Learning
Lesson 1: Machine Learning
I’ve officially started the Python Machine Learning from Beginner to Pro course on Udemy. This post covers the foundation of what I’ll be learning, beginning with an introduction to machine learning and a breakdown of AI, ML, and Deep Learning.
Introduction to Python for Machine Learning
Before we jump into writing code or building models, it’s important to understand the basics of machine learning, how Python fits into the ML workflow, and the different types of learning techniques we’ll encounter along the way.
Python is the main language used in this course because it’s simple to write, widely used in data science, and supported by powerful libraries that make ML much easier to implement. We’ll explore these libraries in detail as we go.
In the first few sessions, we’ll focus on theory — setting the stage before jumping into practical work. This includes:
Installing Python for ML
Understanding key ideas
Getting familiar with ML concepts before coding
AI vs ML vs Deep Learning: What’s the Difference?
As we begin, it's essential to know the difference between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).
Artificial Intelligence (AI):
AI is a broad field focused on building machines or software that mimic human intelligence. These systems can:
Reason
Solve problems
Understand language
Make decisions
Interpret environments
Scope of AI includes:
Machine Learning
Robotics
Natural Language Processing (NLP)
Expert Systems
Examples: Chatbots, self-driving cars, and personalized recommendations
Machine Learning (ML):
ML is a subset of AI. It’s about teaching machines to learn from data and make predictions or decisions without being explicitly programmed.
Main types of ML:
Supervised Learning – Models learn from labeled data
Unsupervised Learning – Models detect patterns in unlabeled data
Reinforcement Learning – Models learn through trial and error with feedback from the environment
Examples: Email spam filters, facial recognition, and sales forecasting
Deep Learning (DL):
DL is a specialized part of ML that uses deep neural networks (many layers of processing nodes) to learn from vast amounts of data.
Why it matters:
Deep learning can process complex data like images, audio, and language — often outperforming traditional ML techniques.
Examples:
Virtual assistants like Siri or Alexa
Face detection systems
Real-time language translation
Summary:
AI is the big idea: making systems intelligent
ML is how many AI systems learn from data
DL is an advanced form of ML that handles large, complex data using neural networks
Next, I’ll dive into installing Python and setting up the tools we’ll need for machine learning. This journey is just beginning, and I’ll be documenting it step by step.
Stay tuned.