How I’d learn ML in 2024 (if I could start over)
Machine learning is an ever-evolving field that requires dedication and continuous learning. If I were to start over with learning ML in 2024, here are the steps I would take:
1. Understand the Basics
First and foremost, I would make sure to have a solid foundation in mathematics and statistics. Understanding concepts like linear algebra, calculus, and probability theory is essential for grasping the algorithms and models used in ML.
2. Learn the Programming Languages
Python and R are the most commonly used languages in ML. I would focus on mastering Python as it has a wealth of libraries such as TensorFlow and Scikit-learn that are essential for building and training ML models.
3. Take Online Courses
Platforms like Coursera, Udemy, and edX offer a wide range of ML courses taught by industry experts. I would enroll in these courses to gain a deeper understanding of ML concepts and techniques.
4. Practice, Practice, Practice
Hands-on experience is crucial in learning ML. I would work on real-world projects, participate in Kaggle competitions, and collaborate with other ML enthusiasts to hone my skills and gain practical experience.
5. Stay Updated
ML is a fast-paced field with new advancements and technologies emerging constantly. I would stay updated on the latest trends, research papers, and technologies in ML through blogs, forums, and conferences.
6. Build a Portfolio
To showcase my knowledge and skills in ML, I would create a portfolio showcasing the projects I have worked on and the models I have built. This would not only demonstrate my expertise but also make me more attractive to potential employers.
By following these steps, I would be able to navigate the ever-changing landscape of ML and stay ahead of the curve in 2024.
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lmao you dont need Math. Source: Data Scientist with 2.5 YOE
Thank you for this!
Thank you for this! I'm starting my neuroscience PhD soon, and I want to implement ML to aid my projects. Your video provides a super helpful framework going forward 🙂
The coursera courses are not for free like mentioned in the video..
Starting from my point, I think dont rely on GPT too heavily, though it really helps when you're stuck in some problems. Sometimes you have to create your own constrcution of neural networks based on all the basics from python, torch, numpy etc. And your own construction is way more straightforward than the GPT gives. (My experience learning DL for 2 months😜)
Can u suggest courses for ML tech stack
Haha, I pretty much stumbled into this exact process and sequence of courses! For me besides Andrew and Andrew, ChatGPT was also a game changer.
the courses you have mentioned over the coursea is no longer free.
Do you need a degree to work in any A.I. field? Every time I see anything about A.I. or Machine Learning Engineering…. I see people always say you need a Master's or PhD.
Im having a hard time choosing whether i should learn ML or blockchain development?
For those viewers with short attention spans. give these prompts in ChatGPT4 with a video summarizer plugin: "Please tell me what this YouTube video is about: https://youtu.be/gUmagAluXpk?si=wjPwJ3AQXsLrGqnP" and "I'm new to machine learning and ChatGPT. How can i apply this information in my life?"
As an MSc Computational Physics student and a beginner in learning machine learning who has done some research on how to teach myself ML, a lot of what you said is consistent with my own conclusions and how I would approach the self-learning process. Great video! Subscribed.
Is machine learning course on coursera is free?
Thank you! Very helpful!
It will help you.
1. Basics of python
2. Learn numpy, pandas, matplotlib
3. Beginner course:
* Supervised Machine Learning: Regression and Classification
* Advanced Learning Algorithms
* Unsupervised Learning, Recommenders, Reinforcement Learning
By- Andrew Ng
4. Neural network
* Neural Networks: Zero to Hero
5. Deep learining specialisation
* Neural Networks and Deep Learning
* Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
* Structuring Machine Learning Projects
* Convolutional Neural Networks
* Sequence Models
Finally a good video on how to actually get started thanks a lot man
🎯 Key Takeaways for quick navigation:
00:00 🚀 Introduction and Learning Path Overview
– Learning ML in 2024 requires a laptop and a step-by-step approach.
– Six key steps outlined for effective machine learning learning.
– Emphasizes not strictly following a particular order for the steps.
01:23 🐍 Learning Basics of Python for ML
– Starting with learning the basics of Python is crucial for ML.
– Recommendation to actively code along with tutorials for a hands-on experience.
– Avoid going too in-depth initially to maintain a fun learning experience.
02:30 🧮 Fundamental Maths for ML
– Emphasis on the need to learn fundamental maths: calculus, linear algebra, and probability theory.
– Resources suggested for learning high school or entry-level college maths.
– Highlighting that complex maths is not required, focusing on understanding basics is sufficient.
03:54 📊 ML Developer Stack: Tools and Libraries
– Introduction to the ML developer stack, including Jupyter notebooks, pandas, numpy, and matplotlip.
– Purpose of libraries explained, such as numpy for matrix operations and pandas for tabular data.
– Practical improvement of Python and ML skills through familiarity with these tools.
05:32 🎓 Learning ML and Deep Learning Concepts
– Recommendation of Andrew Ng's machine learning specialization as a gold standard.
– Introduction to classical ML concepts and their importance in interviews.
– Suggestion to follow up with Andre Kathi's neural network series for practical implementation of NLP models.
06:54 🧠 Advanced ML Courses and Hugging Face
– Continuing with more advanced and practical courses, such as the Deep Learning Specialization.
– Inclusion of Hugging Face library in the course for understanding modern NLP techniques.
– Highlighting the flexibility to directly take Hugging Face's NLP course for in-depth knowledge.
06:12 🛠️ Working on Real ML Projects
– Emphasis on hands-on learning through working on Kaggle challenges.
– Caution to start with simpler challenges to avoid frustration.
– Encouragement to move on to more complex challenges with prize money as skills improve.
06:54 📄 Reimplementing Papers for Practical Experience
– Recommends reimplementing a paper and recreating results for a significant learning experience.
– Highlights the value of such projects in standing out on ML applications.
– Acknowledges other simpler ways to stand out during the learning process, teased for further exploration in another video.
Made with HARPA AI
I was scared that I didn't know enough math after taking linear algebra, but that covers most. I totally agree you can always go back and learn the holes in your knowledge in stead of spending months filling in potential holes.
I wish you had posted this 6 months ago but anyways I can use this video for revision roadmap thanks mate keep it up👏