active 3 days, 20 hours ago
  • arvind posted an update in the group Group logo of Machine LearningMachine Learning 1 month, 3 weeks ago

    Part 1 – Machine Learning For Beginners – Basics

    Part 2 – MI environment

    Part 3 – Python Decision Tree (Theory)

    Part 4 – Python Decision Tree (Coding)

    Part 5 – Python Decision Tree (Graphiviz)

    Part 6 – Knn(Friend Recommender)

    Part 7- 5-Fold Cross Validation

    • I would suggest you should know some basic statistics, and probability theory up to a working understanding of Bayes’ Theorem. It is possible to get by without these if you really want to avoid the maths, but your knowledge of the fundamentals will be shallow and you may struggle. Nothing too fancy.

      If you want to have a full understanding of neural networks or some of the more convoluted statistical techniques, then I suggest you will need to be comfortable with linear algebra, differential calculus, and some more advanced statistics.

      My personal recommendation is this: maths looks really scary, but often even the more alien looking formulae describe relatively simple concepts, especially for someone who knows logical constructs via programming. Don’t be afraid to look for more “human friendly” tutorials on some of the trickier concepts – you might be surprised.

      There’s also nothing wrong with starting (or even staying) with statistical learning like decision trees. Most real-world data science can be completed better with well-crafted statistical techniques than with neural networks

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