How to Start Your AI Learning Journey from Scratch in 2023
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Chapter 1: Introduction to Learning AI
If you're looking to delve into the world of AI, it's crucial to understand the technical aspects that underpin the AI tools we use daily. However, grasping these technical skills can be more complex than simply knowing how to operate ChatGPT. You'll need to familiarize yourself with coding, mathematics, machine learning, and additional skills.
Don't fret! You don't need to be an expert in these areas to embark on your AI journey. In this article, I'll outline a roadmap and resources that will help you learn AI from the ground up.
Before we proceed, remember that the skills listed below don’t have to be learned in a specific order. Feel free to explore each area and determine which ones you need to prioritize as you progress.
Section 1.1: Essential Python Libraries
Python is the primary programming language used in data science and machine learning (ML). While data science and ML are not identical to AI, they share many foundational elements.
Source: Corpnce
It's no wonder that Python is a vital tool for anyone interested in AI. You don't have to be a Python whiz to learn AI; it's more about grasping the basics and mastering some key libraries.
Your approach to learning Python for AI may vary depending on your existing coding knowledge. If you have experience with other programming languages, I recommend quickly reviewing Python fundamentals and then focusing on libraries like Numpy, Pandas, Matplotlib/Seaborn, and scikit-learn. Why? Because AI and ML applications heavily rely on data. By familiarizing yourself with these essential libraries, you'll be equipped to clean, manipulate, and visualize data, which is crucial for diving deeper into AI.
There are numerous free resources to learn Python and these key libraries. Personally, I recommend the freeCodeCamp YouTube channel. If you're new to coding, before diving into Python tutorials, consider using Brilliant for an interactive introduction to programming and data analysis. I find Brilliant to be an excellent platform for learning technical concepts on the go, especially if you're starting from scratch.
Section 1.2: Hands-On Machine Learning Projects
The most effective way to learn is through practical experience. Therefore, engaging in projects is essential for truly understanding AI.
The good news is that a variety of projects are available online, though many lack solutions, which can leave you feeling stuck. That's why I suggest exploring Kaggle projects.
Kaggle offers numerous machine learning challenges, complete with datasets (which saves you the hassle of data collection) and the ability to review others' code. Their solutions are presented in a notebook format, providing both code and a detailed explanation of the problem-solving process.
If you're learning AI independently, Kaggle will be a valuable resource for tackling projects. Additionally, Kaggle features a free 3-hour Intro to Machine Learning course that combines articles and notebooks. If you prefer video courses, I’ll point you to a free machine learning course later on.
Chapter 2: The Mathematical Foundations
Mathematics is a key skill for grasping the algorithms and concepts behind ML and AI. Fortunately, you don't need to become a math genius; a solid understanding of high-school-level math will suffice.
Since I mentioned high-school math, I want to clarify that you don’t need to overwhelm yourself with advanced topics initially. It's best to focus on the basics first and build up your math knowledge as needed.
Most of you likely possess the high-school math skills necessary for machine learning. However, if it's been a while since you've tackled math, here are some resources to help you refresh your knowledge:
- Khan Academy: Offers a variety of free video lectures covering calculus, statistics, probabilities, and more.
- Brilliant: Provides interactive lessons to help you learn essential high-school math concepts for machine learning.
I recommend Khan Academy for those needing a thorough review and Brilliant for those looking to refine their math skills.
Section 2.1: Free Machine Learning Specialization
To gain a deeper understanding of ML and AI, I suggest enrolling in the free Machine Learning Specialization on Coursera. This course series, led by Andrew NG—an esteemed figure in ML and AI—provides an excellent foundation.
It's a beginner-level program consisting of three courses: supervised machine learning, advanced learning algorithms, and unsupervised learning. Based on my experience, having a solid grasp of coding and math is beneficial before taking this course.
Throughout the specialization, you'll learn to build ML models using the key Python libraries mentioned earlier, apply supervised and unsupervised learning techniques, construct neural networks with TensorFlow, create recommender systems, and more! According to Coursera, if you dedicate about 10 hours a week to this course, you can complete it in two months. So, if you’re eager to explore ML and AI, start today—the sky's the limit!
As you immerse yourself in the ML and AI domains, you'll discover a continuous stream of new knowledge to enhance your skills. This could involve learning new libraries, effective data collection techniques, best practices for data cleaning, advanced mathematical concepts, or novel algorithms.
Embrace this learning journey as a sign of your progress. Don't hesitate to keep expanding your knowledge as you advance.
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