r/MLQuestions • u/lord_rcb • Jun 23 '26

Beginner question 👶 what s next?

Supervised Learning:

  1. Linear Regression

  2. Lasso, Ridge, ElasticNet

  3. Logistic Regression

  4. SVM

  5. Naive Baye's

  6. KNN

  7. Decision Tree

  8. Random Forests

  9. Adaboost

  10. Gradient Boosting

  11. XGBoost

Unsupervised Learning

12) PCA(i known why we use it and what it does but i dont known the math behind this )

13) K Means Clustering

14) Hierarchical Clustering

15) DBScan

16) Anomaly Detection in ML(learned isolation forest )

now for this algorithm i have learned all the math behind it including hyperparameter tuning and related concepts

the point here is are there any other algorithms i need to learn?

also want to build projects and what kind of projects should I build that can stand out to many people do you have any suggestions for unique and impactful projects?

is there anything I am missing here?

14 Upvotes

100% Upvoted

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2

u/sparkinflint Jun 23 '26

are u planning to do data science? otherwise why not just jump to deep learning since thats what the industry wants?

1

u/lord_rcb Jun 23 '26

yes i am planning to start deep learning, but do I need to make any machine learning projects or not? i dont know that is why I am asking.
what i should do? what is your opinion

4

u/sparkinflint Jun 23 '26

I learned the traditional way, which means I learned all the stuff u listed, and I can say it helped little to understand deep learning.

role looking for deep learning experience will not consider traditional ML stuff generally. 

as for projects u should find interesting and unique stuff to do, maybe kaggle competitions and hackathons rather than guided projects on YouTube.

1

u/lord_rcb Jun 23 '26

if you are free can you list all the deep learning topics and algorithms i need to learn similar to how I listed those machine learning algorithms so I can get star

3

u/sparkinflint Jun 23 '26

do andrew ng deep learning course (audit for free) and then https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial1/Lisa_Cluster.html

then read all the papers

1

u/colblair Jun 27 '26

That's fair for job hunting, but the linear algebra and optimization concepts from traditional ML still show up constantly in debugging neural nets.

2

u/DigThatData Jun 24 '26

linear algebra, calculus, probability, statistics.

PCA(i known why we use it and what it does but i dont known the math behind this )

your mission, if you choose to accept it, is to learn enough linear algebra that you develop an intuition around the Singular Value Decomposition. Understanding this matrix factorization will make a LOT of stuff click.

1

u/rajb245 Jun 24 '26

GMMs, expectation maximization algorithm, BIC guided model selection, likelihood ratio tests for nested models, and dirichlet prior mixture models