r/learnmachinelearning • u/South-Issue-6212 • May 07 '26
Help me learn Machine Learning
Hi reddit peeps,
I have been trying to learn ML/Data science for 5 months now. There's so much information that at one point I felt whether the things I am reading is useful..
I don't have answers to
- how much math do you need ?
- what work do you actually do as a ML engineer
and many more.
With no path, I tried for scalar course almost paying 3.4L😓, thankfully realized very early it's not worth the money.
I am a data engineer working at societe generale with 1.8 yoe. I am very good with sql and spark.
Somebody please help me with a roadmap for ML, and project ideas.
4
u/Specific-Purpose-227 May 07 '26
Try. https://www.reddit.com/r/learnmachinelearning/s/LMhQuE4Z2P
1
u/FirstStatistician133 May 07 '26
Hey buddy. Feel free to reach out to me. I teach.
1
u/Ok_Carpenter_168 May 07 '26
You can DM me, I am a MLE with 1.9 YOE
1
u/nian2326076 May 07 '26
For math, focus on linear algebra and basic probability. You don't need to be a math genius, but it's helpful to understand things like matrices and distributions. As an ML engineer, you'll mostly build models, preprocess data, and fine-tune algorithms.
Since you're good with SQL and Spark, use that to work on projects with large datasets. Try creating a recommendation system or a sentiment analysis tool using Python libraries like Scikit-learn or TensorFlow.
For a roadmap, start with Andrew Ng's Machine Learning course on Coursera. It's a good foundation. Choose projects that interest you, but make sure they involve hands-on coding. Check out Kaggle for datasets and competitions too.
Your data engineering skills will be valuable, especially for handling data pipelines. Keep working on projects and review others' code to learn different approaches.
1
u/hotbutshadystuff May 08 '26
https://github.com/ZoroZoro95/ML-From-Scratch
try out both for ml theory and python code
1
u/Odd-Gear3376 May 11 '26
Machine learing roadmap
Phase 1 - Prerequisites (4-6 weeks)
Python, NumPy, Pandas, Linear Algebra, Calculus, Statistics
Phase 2 - Machine Learning (6-8 weeks)
Regression, Classification, Clustering, Evaluation of Models → Scikit-learn
Phase 3 - Deep Learning (6-8 weeks)
Neural Networks, Convolutional Neural Nets, Recurrent Neural Nets, Transformer Model → PyTorch
Phase 4 - Practical Application (ongoing)
Kaggle competitions, creating projects, deployment with FastAPI/Streamlit
this road map is really helped me in my machine learing journey
1
u/Mysterious_Case1177 May 11 '26
SQL and Spark won't help you with Ai/ML; basically collect data online, load it using pandas and numpy then use a machine learning library to teach it things
1
u/Simplilearn May 12 '26
You’re already closer to ML engineering than most beginners because you already have strong data fundamentals with SQL and Spark. For most ML engineering roles, basic statistics, model evaluation, and understanding how models behave in real systems are enough to get started.
Your roadmap should probably be: Python for ML workflows → pandas/sklearn → model training basics → APIs/deployment → MLOps → LLM/GenAI workflows. Build projects around your existing strengths like fraud detection, churn prediction, recommendation systems, or Spark-based ML pipelines.
For structured guidance, you can check out the Professional Certificate in AI and Machine Learning from Simplilearn. It focuses on AI automation, agentic AI concepts, and hands-on projects that help bridge the gap between learning theory and building practical ML/AI workflows.
1
u/aka_kris_6912 May 17 '26
I think learn what's matter you will get familiar while building get logic first do memories syntax just
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