Python fundamentals for machine learning

Introduction

Python is a programming language used widely across machine learning and artificial intelligence. If you are starting out in this absorbing field, knowing the fundamentals of Python is essential. In this article we go through the Python fundamentals for machine learning that will help you succeed as a programmer in the field.

Why learn Python for machine learning?

Before we get to the fundamentals, let's look at why this particular language is in such demand in machine learning.

• Simple, clear syntax

  Python's syntax is clean and readable, which makes it an excellent choice for beginners. You get to concentrate on the algorithms and the machine learning problems rather than spending time on complicated language constructs.

• A large community and plenty of libraries

  Python has an enormous community of developers ready to support you and answer questions. On top of that there are libraries such as NumPy, Pandas and Scikit-learn that make working with data and building models far easier.

• Well suited to prototyping

  Python lets you prototype machine learning algorithms quickly. That is particularly useful at the start of a project, when you need to judge whether an idea works.

The Python fundamentals you need

Now to the fundamentals themselves, the ones you will use in machine learning work.

• Variables and data types

  In Python, variables are declared without specifying a type. The simplest types are numbers (integers and floating point), strings and booleans.

  age = 25
  name = "John"
  is_student = True

• Operators

  Python supports all the basic mathematical operators: addition (+), subtraction (-), multiplication (*), division (/) and others. They work on both numbers and variables.

• Data structures

  In machine learning you need to be comfortable with data. Python offers a range of structures: lists, tuples, sets and dictionaries.

  numbers = [1, 2, 3, 4, 5]

• Conditionals and loops

  Conditionals (if, elif, else) and loops (for, while) let you control how a program runs depending on the conditions.

  if age >= 18:
      print("You are an adult")
  else:
      print("You are a minor")

• Functions

  Functions simplify code by breaking it into smaller pieces.

  def add_numbers(x, y):
      return x + y

• Machine learning libraries

  An important part of machine learning programming is using purpose-built libraries: NumPy for working with arrays, Pandas for data analysis and Scikit-learn for building machine learning models.

  import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split

In closing

Python is an excellent choice for anyone starting out in machine learning, thanks to its clear syntax and its rich ecosystem of libraries. In this article we have gone through the fundamentals you will need. Start from this footing and work your way gradually into the harder concepts to become a capable specialist in this fascinating field. Good luck on your journey into machine learning.