The Ultimate Roadmap to Learn AI Without a Degree

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Hello everyone!

We all know that AI is going to dominate the market by 2030, and the best part? You don't need a degree to break into this exciting field. All you need is a laptop and a solid learning roadmap.

I’m Sreeraj, and while I’ve completed my BTech in Artificial Intelligence from an IIT, I can tell you this – you only need 6-8 months of consistent self-education to get a strong foothold in AI. So, if I had to start learning AI from scratch in 2020 without a degree, here’s the exact roadmap I would follow.

Understanding AI: The Basics

First, let's break down what AI really is. At its core, Artificial Intelligence is a program or model that can recognize patterns in data and use those patterns to predict future outcomes. This might sound simple, but the process of training AI models is actually quite complex. The key to understanding AI lies in Machine Learning – a field focused on how these models learn from the data.

Now, there’s one thing that holds true for 95% of AI – it's driven by mathematics. If you don’t enjoy math, you’ll likely struggle. But don't worry, the math isn't as intimidating as it seems if you approach it step-by-step. You'll need to understand three core areas:

1. Linear Algebra – Vital for working with large datasets and performing matrix operations.

2. Calculus – Helps in understanding how to optimize models, a critical aspect when working with AI.

3. Probability – This is necessary because AI models don’t give definite answers but probabilities (e.g., 99% sure it's correct).

Once you understand these areas, you're on your way! I highly recommend checking out resources like 3Blue1Brown for an intuitive understanding of AI math. After that, head over to Khan Academy or Brilliant.org for more structured courses.

Python: The Language of AI

When it comes to AI and Machine Learning, Python is the language of choice. It's not just a language; it's a powerhouse for machine learning due to its libraries and vast community support.

Start by learning the basics of Python – variables, loops, control flow, and functions. You don't need to master the entire language. Focus on the fundamentals so you can use Python to handle the heavy mathematical computations required in AI.

To learn Python for free, I recommend Sentdex on YouTube, which teaches Python through projects. Alternatively, you can check out freeCodeCamp or various beginner-friendly Python tutorials online.

Data Handling: The Backbone of AI

As you dive deeper, you’ll need to learn how to handle and process large datasets – often millions of entries. This is where Python modules like Pandas, Numpy, and Matplotlib come into play.

• Pandas is for structuring and processing datasets.

• Numpy handles complex mathematical operations on these datasets.

• Matplotlib is used to visualize data through graphs and plots.

These tools will become second nature as you progress through your AI journey.

Understanding Machine Learning Frameworks

After mastering the basics, you’ll need to familiarize yourself with machine learning frameworks. These frameworks simplify the process of implementing machine learning models and algorithms.

The three most popular frameworks are:

1. PyTorch: Beginner-friendly and great for learning the fundamentals of machine learning and deep learning.

2. Scikit-learn: Another beginner-friendly framework, perfect for classical machine learning models.

3. TensorFlow: Powerful but complex, and best appreciated once you have a good grasp of the basics.

I recommend starting with PyTorch since it allows for more hands-on learning and gives you a deeper understanding of how models work.

Mastering Machine Learning and Deep Learning

Now that you have the tools and libraries in place, it’s time to learn Machine Learning Algorithms and Deep Learning concepts. If you're looking for the best free resources, I highly recommend the Andrew Ng’s Machine Learning Course on Coursera – it’s considered the gold standard for AI learners.

For deep learning, there’s another excellent course by Andrew Ng and others, as well as a Neural Networks from Scratch series by Andrej Karpathy on YouTube that dives deep into NLP (Natural Language Processing).

Kaggle: Hands-On Learning

After completing the courses, you’ll need to practice and build projects. The best place to do this is Kaggle – an online platform where you can find datasets, take part in competitions, and practice your skills.

Spend 3-4 months here, building your portfolio with real-world AI projects. It’s one of the most valuable platforms for honing your skills and preparing for job opportunities.

Building Your AI Career

Once you’ve completed the courses and gained hands-on experience, you’re ready to start applying for jobs. AI and machine learning are in-demand skills, and your Kaggle projects and certifications will be your ticket to landing your first job or internship.

To sum up, you don’t need a degree to become an AI expert. With the right mindset, consistent effort, and the roadmap I’ve outlined above, you can self-educate and build a solid foundation in AI within a year. Keep pushing, stay curious, and soon you'll be creating cutting-edge AI models.

Feel free to reach out if you need guidance along the way. You’ve got this!

Best,
Sreeraj