How I'd Learn AI in 2024 (If I Could Start Over) | Machine Learning Roadmap

Summary

In this video, Asan Sharma shares his insights on becoming an AI engineer in 2024, highlighting the booming demand for AI professionals. He outlines a step-by-step learning path, starting with mastering math, Python, and data analysis libraries, then moving on to machine learning frameworks and models. Sharma emphasizes the importance of supervised, unsupervised, and reinforcement learning, and suggests practicing on platforms like Kaggle. He also delves into deep learning, neural networks, and generative AI, recommending courses and tutorials to build expertise. The video concludes with advice on leveraging large language models and creating custom AI applications.

Takeaways

• 🚀 AI is predicted to be the biggest trend of 2024 and beyond, with high demand for AI engineers.
• 📚 To become an AI engineer, start with a strong foundation in mathematics, focusing on calculus, linear algebra, and probability.
• 🐍 Learn Python, the most widely used programming language in AI, and understand its basics including data types, control structures, and OOP concepts.
• 📊 Master data analysis with Python using libraries like NumPy, Pandas, and Matplotlib for handling and visualizing data.
• 🛠️ Choose a machine learning framework such as PyTorch, PyTorch, or TensorFlow to create and train models.
• 📝 Understand the three types of machine learning: supervised, unsupervised, and reinforcement learning, and their respective applications.
• 🔍 Practice by solving problems on platforms like Kaggle using various datasets to apply your machine learning knowledge.
• 🧠 Dive into deep learning by learning about neural networks, backpropagation, and hyperparameters.
• 🖼️ Explore Convolutional Neural Networks (CNNs) for image classification and natural language processing (NLP) for text data.
• 🤖 Build generative AI applications by understanding large language models like GPT and learning prompt engineering.
• 📱 Stay updated with the latest AI trends and tools, such as GPT plugins and the GPT store, to create custom AI solutions.

Q & A

What is the prediction for AI in 2024?

AI is predicted to be the biggest trend of 2024, with a booming demand for AI engineers due to the launch of applications like chat GPT and other generative AI tools.

Who is the speaker in the video?

The speaker in the video is Asan Sharma, who started learning about machine learning and AI in 2019.

What is the basic definition of machine learning?

Machine learning is a process through which a system can recognize patterns and predict future outcomes.

What are the key mathematical concepts to understand for AI and machine learning?

The key mathematical concepts include calculus (differentiation and integration), linear algebra, and probability.

Why is Python important for AI engineers?

Python is the most used programming language in the AI field due to its simplicity and the vast number of libraries available for machine learning and data analysis.

What are the three libraries used for data analysis with Python?

The three libraries used for data analysis with Python are NumPy, pandas, and Matplotlib.

Which machine learning frameworks are recommended for beginners?

For beginners, PyTorch and scikit-learn are recommended as they are simpler to use compared to TensorFlow.

What are the three main types of machine learning models?

The three main types of machine learning models are supervised learning, unsupervised learning, and reinforcement learning.

What is the role of Kaggle in learning AI?

Kaggle provides a platform for learners to practice their AI skills by solving real-world problems using various datasets.

What is the significance of neural networks in deep learning?

Neural networks are the foundation of deep learning, consisting of layers of neurons that process inputs and generate outputs, allowing the model to learn complex patterns.

What is a CNN and how is it used in AI?

A Convolutional Neural Network (CNN) is a type of deep neural network used for image recognition and classification by processing pixel data and identifying patterns within images.