Rakshit Kr. Singh

Machine Learning Starter Guide

2024-3-30

Machine learning is a way of teaching computers to learn without being explicitly programmed. Computers can learn from data and identify patterns, which allows them to make predictions and decisions that would be difficult or impossible for humans to make.

“People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.” — Pedro Domingos

A general pipeline for Machine Learning is:

  1. python
  2. Numpy
  3. Pandas and Matplotlib
  4. Sklearn
  5. Tensorflow / PyTorch — implementation know-how
  6. Mathematics

Python

Python is a general-purpose, high-level programming language that is easy to learn and use. It is often used for web development, data science, and machine learning. Python has a large and active community, and there are many resources available to help you learn the language.

Some of the benefits of learning Python:

It is easy to learn and use.
It is powerful and versatile.
It has a large and active community.
It is free and open-source.

Resources:

  1. 100 Days of Python Programming — CampusX
  2. Python in 100 Seconds — FireShip
  3. Official Python Site
  4. Free Python Programming Book (goalkicker.com)

Numpy

Nearly every scientist working in Python draws on the power of NumPy.

NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant.

Resources:

  1. NumPy Website
  2. Python NumPy Tutorial for Beginners — FreeCodeCamp
  3. NumPy Learn

Pandas

Pandas is a fast, powerful, flexible, and easy-to-use open-source data analysis and manipulation tool, built on top of the Python programming language.

Resources:

  1. pandas — Python Data Analysis Library (pydata.org)
  2. Pandas & Python for Data Analysis by Example — Full Course for Beginners — FreeCodeCamp

Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.

Resources:

  1. MatplotLib Official Site
  2. Quick start guide — Matplotlib 3.7.2 documentation
  3. Matplotlib Full Course — Data Science Fundamentals — NeuralNine

SkLearn

Scikit-learn is a free and open-source machine learning library for the Python programming language. It provides a range of supervised and unsupervised learning algorithms for tasks such as classification, regression, and clustering. The library is built on top of NumPy, SciPy, and matplotlib. Scikit-learn is widely used in academia and industry alike for developing machine learning models.

Resources:

  1. 100 Days of Machine Learning — CampusX
  2. scikit-learn
  3. Getting Started — scikit-learn 1.3.0 documentation

PyTorch

PyTorch is a powerful and versatile framework that caters to both beginners and experienced researchers in the field of machine learning and artificial intelligence. Its dynamic computation graph, GPU acceleration, and emphasis on flexibility make it a preferred choice for building and experimenting with various types of machine-learning models.

Resources:

  1. Deep Learning with PyTorch: Zero to GANs — Jovian
  2. PyTorch Official Site

Mathematics

First Year College maths is very important for basics.

Resources:

  1. An Introduction to Statistical Learning — Book
  2. Ncert Class-12 — Book

Thanks for Reading, Readers are Leaders.

Author - Singh, Rakshit Kr.
Mail - rakshitsingh421@gmail.com
LinkedIn - https://www.linkedin.com/in/rakshit-singh-ai/
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