Getting Started With ML Using Python


With Big Data, Machine Learning (ML), and Artificial Intelligence (AI) rapidly becoming the order of the day, an increasing number of people are diving into these trending fields. Today, we’re going to focus on ML and show you how you can step into the world of machine learning using one of the most powerful programming languages in the world right now – Python.

If you are a beginner, this guide to using Python for ML is just what you need.

Let’s get started without further ado!

  1. Developing Basic Knowledge of Python

This is a no-brainer. To start off with ML using Python, one must have some ground knowledge about the programming language. You can begin by installing Anaconda, an industrial-strength Python implementation for Linux, Windows, and OSX, replete with all the necessary tools required for ML.

Get your hands on useful study material on the Internet. Here are some excellent picks:

  1. Acquire Foundational Machine Learning Skills

No, you do not need an extensive and in-depth knowledge of ML to be able to practice it. However, you must have basic knowledge about machine learning to get started in the field. Having a strong background in Mathematics and programming skills will come in very handy here. So, brushing up on your statistical and programming skills (in C, C++, Java, Python) is highly recommended.

Also, you need to be familiar with popular ML algorithms like linear and logistic regression, neural networks, decision trees, random forest, and clustering, to name a few. Try to get accustomed to trending ML frameworks like TensorFlow and Azure.

  1. Scientific Python Packages

Not many are aware of the fact that there exist open source Python libraries that can be efficiently put to use for practical machine learning applications. These libraries are known as scientific Python libraries, primarily used for performing basic ML tasks. Below are the most popular Python libraries:

  • Scikit-learn – Includes all the tools used for ML and data mining. It is considered to be the de facto standard library for ML in Python.
  • Matplotlib – It is a 2D plotting library that can be used in Python scripts and iPython shells, to create publication quality figures.
  • NumPy – It is the most suitable package for scientific computing using Python. It can also be used as a multi-dimensional container of generic data.
  • Pandas – This is great for accessing high-performance, handy data structures and data analysis tools for Python.
  1. Explore ML Topics With Python

After you’ve thoroughly explored the Python libraries, it’s time to move on to learning the useful machine learning algorithms. You can start with Jake VanderPlas’ K-means Clustering and then move onto Decision Trees (The Grimm Scientist). Linear Regression by Jake VanderPlas is also great for getting acquainted with ML linear regression algorithms.

  1. Deep Learning With Python

Deep learning techniques and deep neural networks are increasingly becoming the buzzwords in the industry. If you are a stranger to deep learning, start off with Michael Nielsen’s book, Neural Networks, and Deep Learning.

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Python has two very resourceful deep learning libraries – Theano and Caffe. While Theano efficiently allows you to function with mathematical expressions involving multi-dimensional arrays, all the while allowing you to define, optimize, and evaluate them, Caffe’s deep learning infrastructure focuses on speed, modularity, and expression.

Python is a versatile programming language extensively used for scientific computing and machine learning. It is indeed an excellent choice for Machine Learning because of three primary reasons – first, it is a simple language; second, it is backed by a strong community, and third, it has impressive stack of useful libraries. And with so many tutorials, informative content, and online study materials, now is the best time to get started in ML with Python.  Also, if you need expert guidance, you can always drop by at Coding Ninjas, where our courses on Machine Learning help you understand the nitty-grittys of ML using Python.