/Machine-Learning-Algorithms

In this repository, I have curated all the materials I used to study Machine learning Algorithms.

Primary LanguageJupyter Notebook

RESOURCES TO LEARN MACHINE LEARNING ALGORITHMS

In this repository, I have curated all the materials one can use to study SQL, Data Analysis, and Machine learning Algorithms.

Table of Contents

  1. YouTubers/Playlists to Follow
  2. Probability and Statistics
  3. Python
  4. SQL
  5. Machine Learning
  6. Classical Machine Learning Algorithms
  7. Case Studies

YOUTUBERS/PLAYLISTS TO FOLLOW:

1. PROBABILITY AND STATISTICS:

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2. PYTHON:

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3. SQL:

PRACTICE:

INTERVIEW QUESTIONS:

LEARNING:

GENERAL RESOURCES:

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4. MACHINE LEARNING:

INTERVIEW:

LEARNING:

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5. CLASSICAL MACHINE LEARNING ALGORITHMS

A. Linear Regression

B. Logistic Regression

C. Tree-Based/Ensemble Algorithms

D. K-Nearest-Neighbors

E. Support Vector Machines

F. Naive Bayes

G. Feature Selection

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6. CASE STUDIES:

The best way to approach such a question is to have a framework -

  1. Ask questions to narrow down the problem area
  2. Suggest and use feedback to decide on business metrics relevant to the problem
  3. Decide the best ML formulation (classification/forecasting/recommendation)
  4. Decide on model metrics that can tie to business metrics.
  5. Suggest which models you would experiment with
  6. Explain how you would productionalize.
  7. Explain how you would A/B test the final model

LEARNING:

RESOURCES:

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Machine learning Roadmap