“I am a student of computer science/engineering. How do I get into the field of machine learning/deep learning/AI?”

It’s never been easier to get started with machine learning. In addition to structured MOOCs, there is also a huge number of incredible, free resources available around the web. Here are just a few that have helped me:

Is Python a good language of choice for Machine Learning/AI?

Familiarity and moderate expertise in at least one high-level programming language is useful for beginners in machine learning. Unless you are a Ph.D. researcher working on a purely theoretical proof of some complex algorithm, you are expected to mostly use the existing machine learning algorithms and apply them in solving novel problems. This requires you to put on a programming hat.

There’s a lot of debate on the ‘best language for data science’ (in fact, here’s a take on why data scientists should learn Swift).

While the debate rages on, grab a coffee and read this insightful article to get an idea and see your choices. Or, check out this post on KDnuggets. For now, it’s widely believed that Python helps developers to be more productive from development to deployment and maintenance. Python’s syntax is simpler and of a higher level when compared to Java, C, and C++. It has a vibrant community, open-source culture, hundreds of high-quality libraries focused on machine learning, and a huge support base from big names in the industry (e.g. Google, Dropbox, Airbnb, etc.).

This article will focus on some essential hacks and tricks in Python focused on machine learning.

Fundamental Libraries to know and master

There are few core Python packages/libraries you need to master for practicing machine learning effectively. Very brief description of those are given below,

Numpy

Short for Numerical Python, NumPy is the fundamental package required for high performance scientific computing and data analysis in the Python ecosystem. It’s the foundation on which nearly all of the higher-level tools such as Pandas and scikit-learn are built. TensorFlow uses NumPy arrays as the fundamental building block on top of which they built their Tensor objects and graphflow for deep learning tasks. Many NumPy operations are implemented in C, making them super fast. For data science and modern machine learning tasks, this is an invaluable advantage.

Pandas

This is the most popular library in the scientific Python ecosystem for doing general-purpose data analysis. Pandas is built upon Numpy array thereby preserving the feature of fast execution speed and offering many data engineering features including:

Reading/writing many different data formats

Selecting subsets of data

Calculating across rows and down columns

Finding and filling missing data

Applying operations to independent groups within the data

Reshaping data into different forms

Combing multiple datasets together

Advanced time-series functionality

Visualization through Matplotlib and Seaborn

Matplotlib and Seaborn

Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there. This is equally critical in pursuit of machine learning mastery too as often in your ML pipeline, you need to perform exploratory analysis of the data set before deciding to apply particular ML algorithm.

Matplotlib is the most widely used 2-D Python visualization library equipped with a dazzling array of commands and interfaces for producing publication-quality graphics from your data. Here is an amazingly detailed and rich article on getting you started on Matplotlib.

Seaborn is another great visualization library focused on statistical plotting. It’s worth learning for machine learning practitioners. Seaborn provides an API (with flexible choices for plot style and color defaults) on top of Matplotlib, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas. Here is a great tutorial on Seaborn for beginners.

Example of Seaborn plots

Scikit-learn

Scikit-learn is the most important general machine learning Python package you must master. It features various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN, and is designed to inter-operate with the Python numerical and scientific libraries NumPy and SciPy. It provides a range of supervised and unsupervised learning algorithms via a consistent interface. The vision for the library has a level of robustness and support required for use in production systems. This means a deep focus on concerns such as ease of use, code quality, collaboration, documentation, and performance. Look at this gentle introduction to machine learning vocabulary as used in the Scikit-learn universe. Here is another article demonstrating a simple machine learning pipeline method using Scikit-learn.