An Introduction to Machine Learning with Python

03 Mar 2020

Author: Aditya Khandelwal

This blog is a part of “Introductory Lecture Series” conducted by Aditya in 2019

An Introduction to Machine Learning with Python

Taken from blog.alore.io

Artificial Intelligence refers to making computers behave intelligently. Computers, who are not capable of intelligent behavior on their own, are made to simulate intelligent behavior using some algorithm.

Behind all AI robots/computers, there is an algorithm that makes it look that way. AI is a broad field that encompasses all such algorithms, including hard-coded rules to simulate intelligence.

Machine Learning is a subfield of AI. Here, we try to make the machine learn by itself. Naturally, there is an algorithm involved, but here, instead of telling the computer what to look for, you tell it how to learn. There are no hard-coded rules. We do not use the computer just to speed up something we do, or to memorize a bunch of rules that we cannot. Instead, the machine simulates “learning” on the given “data”.

Deep Learning is a subfield of Machine Learning. It is to do with Neural Networks, algorithms that are inspired by the human brain.

Artificial Intelligence and Machine Learning: An Overview

This lecture gives an overview of AI and ML. It covers:

  • Artificial Intelligence: Goals and Approaches
  • Machine Learning:
    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning
  • Deep Learning

Jupyter Notebook: Machine Learning - 1

Video: Artificial Intelligence and Machine Learning Overview

Important ML Libraries in Python (NumPy, Pandas and ScikitLearn)

This lecture covers the basics of NumPy, Pandas and SciKit Learn, 3 of the most popular libraries for Machine Learning in Python. Topics covered include:

  • Numpy:
    • Datatypes
    • Functions
    • Operations
    • Indexing
  • Pandas:
    • Series
    • DataFrame
    • Indexing
    • Functions
  • Walkthrough of the Iris Dataset using ScikitLearn.

Jupyter Notebook: Important Libraries in Python (for Machine Learning)

Video: NumPy, Pandas and ScikitLearn for ML

Linear Regression From Scratch

Linear Regression is among the simplest Machine Learaning algorithms, which can be used to fit a line through a set of points giving the least error.

In this lecture, we code logistic regression from scratch. We delve into the basic concepts of backpropagation and use NumPy to first code a stochastic version, and then code the vectorized version of Logistic Regression.

Jupyter Notebook: Linear Regression

Video: Linear Regression From Scratch

That’s all for our Introductory series on Machine Learning with Python! Kaggle is a great platform to experiment with datasets and gain hands on practice with ML techniques, so I encourage you to create a Kaggle account and start applying ML methods to real datasets!

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