Machine Learning is the fastest growing technology sector today. There are dozens of different libraries which can cover all your ML and AI needs. The wide array of options is a boon to experienced data scientists but can be intimidating for beginners.
In this article, we will discuss the most useful tools and libraries for starting your journey into ML and AI.
Programming language: Python or R?
R language is very popular among data scientists, but it has a lot of drawbacks if you need to do regular programming as well. On the other hand, Python is a general-purpose programming language which can be applied to many use cases.
This is probably the main reason why Python has become the leader in Machine and Deep Learning in recent years. Every decent library either provides a Python API or has it as the only target language.
Python is a very simple and beginner friendly language. Moreover, it’s not required to know all the intricacies of the language to apply it to ML.
Jupyter Notebook: mixing data, code, and plots in one page
While in traditional programming most of your time is spent in text editors or IDEs, in Data Science most of the code is written in Jupyter Notebook.
It is a simple and powerful tool for tinkering with data analysis problems. It allows you to write Python code, text descriptions and also can embed plots and charts directly into an interactive webpage.
To make things even easier, Google has created a free service Google Colab which provides CPU resources and access to a GPU unit which is very handy when you’re dealing with Neural Networks and Deep Learning.
Scikit-learn: best library for classical ML algorithms
Scikit-learn is one of the most popular ML libraries today. It supports most of the classical supervised and unsupervised learning algorithms: linear and logistic regressions, SVM, Naive Bayes, gradient boosting, clustering, k-means, and many, many more.
Along with different ML models, Scikit-learn provides various means for data preprocessing and results analysis. Scikit-learn focuses mostly on classical ML algorithms, thus it has very limited support for Neural Networks and can’t be used for Deep Learning problems.
For beginner data analysts Scikit-learn is more than sufficient, it’s a good tool to work with until you decide to move on to Deep Learning.
Tensorflow: Machine Learning and Deep Learning library
Tensorflow is an ML and DL library built by Google. It supports many classical ML algorithms for classification and regression analysis. But compared to Scikit-learn, it’s a more heavy-weight solution. Moreover, Scikit-learn is often used in conjunction with Tensorflow, so it’s good to know both.
The main advantage of using Tensorflow is that it has great support for Neural Networks and Deep Learning algorithms. Also, it’s focused on computational efficiency and supports CPU, GPU, and TPU calculations.
Usually, you can start prototyping in Scikit-learn and later port your model to Tensorflow if you need to speed things up or support bigger datasets.
Pandas: data extraction and preparation
Pandas is a very popular library for fetching and preparing data for later use in other ML libraries like Scikit-learn or Tensorflow.
It supports many different complex operations over datasets. Pandas provides the ability to easily fetch data from different sources: SQL databases, text, CSV, Excel, JSON files, and many other less popular formats.
Once the data is in memory, there are dozens of different operations to analyze, transform, backfill missed values and clean the dataset. Pandas offers a lot of SQL-like operations over datasets (e.g. joining, grouping, aggregating, reshaping, pivoting), but it also has a rich set of statistical functions to perform simple analysis.
Pandas data structures are also supported by Jupyter Notebook, which can make neat visualizations of them.
NumPy: arrays and linear algebra library
NumPy is a core component of Scikit-learn and Pandas, so it’s good to have a basic understanding of it. It supports multi-dimensional arrays and matrices along with all core linear algebra operations.
SciPy: scientific computing library
SciPy has a huge set of different scientific calculation operations. The abovementioned Scikit-learn relies heavily on it to implement different ML algorithms.
Usually, SciPy is rarely used directly because Scikit-learn tries hard to provide a higher-level API which covers all ML needs. But in some rare cases, you just need some advanced statistical algorithm(e.g. BoxCox normalization) which is not provided directly via Scikit-learn API.
Matplotlib: plotting and data visualization
Matplotlib is a standard tool in every data scientist’s toolbox. It provides the ability to draw many different kinds of plots and charts for visualizing results.
Matplotlib plots are easily embeddable in Jupyter Notebook. This way, you can always visualize data and results obtained from your models.
The library has a lot of complimentary packages. One of the most popular is Seaborn. It has a simpler interface and better support for Pandas data structures.
PyTorch: alternative Deep Learning library
PyTorch is a popular Deep Learning library built by Facebook. In addition to CPU, it supports GPU-accelerated computations. The library is focused on bringing users a fast and flexible modeling experience and has gained a lot of traction in the Deep Learning community.
Compared to Tensorflow, it is easier to learn and use. The downside is that PyTorch is less mature than Tensorflow, but the community is growing fast and there are already a lot of learning materials and tutorials.
Keras: high-level wrapper around TensorFlow
Keras is a popular high-level Deep Learning library which uses various low-level libraries like Tensorflow, CNTK, or Theano on the backend. In theory, Keras is a direct competitor to PyTorch, because they both strive to provide a simpler API for working with Neural Networks.
The current benefits of Keras are that it’s more mature, has a bigger community and plenty of ready to use tutorials. Also, unlike PyTorch, it can use Tensorflow under the hood. On the other hand, PyTorch provides some very compelling features (interactive debugging, dynamic graph definition) and is growing quickly.
Despite a huge ecosystem, there are strong leaders which are the most widely adopted in the Machine Learning and AI world. When you are just starting your journey into ML, it’s better to choose from these tools because they are extensively covered in various tutorials, courses, and have good community support.
To simplify the complexity of learning, you can start by getting a good grasp of classical ML algorithms and focus more on Scikit-learn usage. And after attaining a good understanding of it, it makes sense to move on to Tensorflow, PyTorch, or Keras and make your way into Deep Learning world.
Railsware’s journey in the spaces of ML and AI is in full swing. Our expertise covers the creation of self-learning algorithms, architecting human-like intelligent systems, providing data mining services and other activities that you can discover here. Certainly, we actively leverage the described technologies and share our experience in hopes to encourage you for new undertakings in AI/ML software development.