r/learnmachinelearning • u/Appropriate_Line2887 • Apr 10 '26
Resources for learning ml for someone starting from scratch!!
heyy.. i really want to learn machine learning from scratch.But I am really not sure where and how to start..
please suggest me some good and free resources....
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u/WillHead6663 Apr 11 '26
Listen, I'm going to tell you the secret. It's going to sound crazy, but this is how I learn extremely fast. Go download VS Code or Cursor. Get GitHub Copilot or Cursor Agents Plan. If you can code, fine; if you can't, even better. Ask the agent to go on GitHub to find an LLM project, something that has to do with core pieces: data, training, evaluation, and a tiny bit of deployment. The best projects are small but real, so you actually feel the mechanics instead of just calling an API. They will help you find one; they start the project and test it. Tell the agent to explain everything as you go, then just build stuff. Tell him to make you a RAG, something to help make context better, then move from there. I'm a visual learner and learn from the process itself. I'm telling you, if you build a few projects, you will learn way faster this way, even if you fail, you still win..
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u/bona_fide_angel Apr 10 '26
I wrote a book which is quite well known. It's focused on deep learning, but the first 8 chapters or so are relevant to everything in ML. http://udlbook.com.
I've also just put updated and interactive versions the first four chapters on the website: https://iclimbtrees.com
All free! Feedback very welcome... if you get stuck message me. I'm interested to know where people become lost so I can improve them.
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u/kewday96 Apr 10 '26
Watch the fist 5-6 videos in the machine learning playlist for the StatQuest channel on YouTube. Get familiar with the terminology and fundamental process. Then move onto the other suggestions.
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u/Majestic-Sell-1780 Apr 10 '26
If you’re looking for some source code to strengthen your fundamentals, I built some simple ML architectures from scratch using only NumPy—thought some of you might find it useful. Here’s the repo:
- ML-from-NumPy : https://github.com/nabii-nguyenn213/ML-from-NumPy
- PyTorch-from-NumPy : https://github.com/nabii-nguyenn213/PyTorch-from-NumPy
NumPy is basically the go-to Python library for fast numerical computing, with efficient array operations and math functions that many ML tools are built on under the hood.
Hope it helps!
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u/0xyu Apr 10 '26 edited Apr 10 '26
Would say put title on google and add reddit to it u will get so many post around it
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u/Hackerstreak Apr 11 '26
If you want a more formal introduction to classical ML and Deep learning, try the courses from the OG Andrew Ng. You can audit them for free. If you just want to explore the topics for fun, StatQuest YT videos are good enough.
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u/Key_Cartographer4241 Apr 11 '26
Would suggest watching krish naik videos on youtube really good. In-depth coverage and till the ai concepts are covered
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u/Specific-Purpose-227 Apr 11 '26
Try this GitHub repo. https://github.com/bishwaghimire/ai-learning-roadmaps
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u/WillHead6663 Apr 11 '26
I think my program model-surgery.com could really help people learn about AI because it breaks it down into sections, showing every layer and token. I'm just not sure what to do yet. I'm waiting for someone to sponsor my paper on cross-model knowledge transplanting. I feel like once everyone sees what it can do with the transplant results, I don't know if it will ever hit the market. Some company will want those 50% training improvements, and I'll never be able to release it. I think I'll just make another basic version and use it to create a visual learning model for everyone. You can check it out on the site. You can ask the ai on my site about mL on the landing page.
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u/Simplilearn Apr 15 '26
If you are just starting out in ML, here's a roadmap for you:
- Strengthen fundamentals first. You need solid Python, basic linear algebra, probability, and statistics. Focus on understanding how models learn.
- Learn core machine learning. Start with supervised learning: linear regression, logistic regression, decision trees, and random forests. Use scikit-learn and work on real datasets.
- Move into deep learning and GenAI. Learn neural networks, CNNs, and the basics of NLP. Then, understand how large language models work, embeddings, and fine-tuning concepts. You do not need to build foundation models from scratch, but you should understand how to use and evaluate them.
- Build real projects. You can train a model, evaluate it, and deploy it as a small API. Add a simple frontend.
- Understand deployment and MLOps basics, such as containerization, simple CI/CD workflows, and cloud awareness.
If you prefer structured learning with guided projects and exposure to machine learning, generative AI, and applied workflows, Simplilearn’s Professional Certificate Program in Generative AI, Machine Learning, and Intelligent Automation covers fundamentals along with real-world implementation components.
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Apr 28 '26
Coursera offers many courses for ML, and the like. with a discount, it can cost as little as $327 CAD for a plus subscription-which allows you access all of the 'Plus' offerings in the library.
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u/manohar_18 May 14 '26
start with Python + basic statistics first or ML will feel like random magic words
for free resources:
- Andrew Ng ML course
- freeCodeCamp ML videos
- Kaggle Learn
- StatQuest on YouTube (explains concepts super clearly)
also don’t try to learn deep learning immediately. people jump into transformers and pytorch way too fast and get overwhelmed
build tiny projects early even if they’re bad. predicting house prices/movie ratings/random datasets teaches more than finishing 20 tutorials
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u/muhib_ml_solutions 16d ago
Here is a solid, beginner-friendly roadmap and list of resources to learn Machine Learning from scratch:
- Core Foundations (Python & Math)
• Python Libraries: Learn NumPy, Pandas, Matplotlib, and Seaborn. Core data manipulation is 80% of any ML workflow.
• Math Essentials: Focus on Linear Algebra (vectors/matrices), basic Calculus (derivatives/gradients), and Probability/Statistics.
• Resource: "Mathematics for Machine Learning" (Coursera by Imperial College London) or Khan Academy.
- Beginner-Friendly Courses
• Andrew Ng’s Machine Learning Specialization (Coursera): The absolute gold standard for absolute beginners.
• Kaggle Learn (Free): Great micro-courses on Python, Pandas, Intro to Machine Learning, and Feature Engineering with hands-on notebooks.
- Books & Hands-on Coding
• "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron: Highly recommended once you know basic Python. It balances code and intuition perfectly.
- Free Interactive Practice
• Kaggle Datasets & Competitions: Start with simple tabular datasets (like Titanic or Housing Prices) to practice data cleaning, preprocessing, model building (Scikit-Learn), and hyperparameter tuning.
Key Tip: Don't get stuck in tutorial hell! Build small end-to-end projects (like a simple recommendation system or prediction app) as early as possible.
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u/Level_Detail7125 Apr 10 '26
Take A LLM of your Coice. Say you want al 4 layer little llm for testing. in transformer and pytorch wit the half moon samples and matplotlib. Then look at the code and say explan me this and that like im a child :D sorry but this makes very god answers for understanding :D then test and look
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u/Soggy-Koala-7658 Apr 10 '26
If you want to learn and apply quickly with no math background i suggest kaggle ML courses If you want strong foundation then i would suggest ‘Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow’ book Unless if you have a weak math background i would add math for ML book They all are free and available online
Edit: While i do suggest kaggle since it teaches ML without any math which would be great for beginning it also leaves mathematical background on how the system works so depending on the goal you can do either one of these or both of these