PyCaret is an open-source, low-code machine learning library in Python, while caret is an R package that provides tools for predictive modeling. PyCaret is for Python users, and caret is for R users.
Intro to Python machine learning with PyCaret
Hunter Johnson
8 min read
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Python and machine learning are two prevalent topics among veteran and beginner developers alike. PyCaret is a relatively new Python library that represents a beautiful coupling of the two topics. There has been a boom in data in the past couple of decades. User activity is expanding rapidly along with the internet, creating massive amounts of information every day. This boom is referred to as “big data”, and it means that data scientists need a way to learn from all this useful information without drowning in it.
Data scientists in today’s environment require a faster and less complex method to experiment with data, which is a major reason why machine learning is so heavily used by data scientists today. To keep pace with these evolving demands, it’s essential for professionals in the field to continually learn machine learning techniques and stay updated with the latest advancements. Let’s explore the attributes of PyCaret, and how you can use it for machine learning with Python!
We’ll cover:
Machine learning overview#
Machine learning uses statistical functions and algorithms that allow models to make particular predictions and decisions. Machine learning uses algorithms to organize data, learn from that data, and utilize those learnings to make intelligent decisions and classifications without the direct input of the developer. This is the aim of machine learning models: to optimize computers to perform tasks without the need for human interaction or specific programming. This practice optimizes the functionality and overall efficiency of the computer.
Data analysis and data preparation, for example, become much more manageable when a computer performs the groundwork. All sci-fi references aside, machine learning is literally the practice of giving a functioning “brain” to our computers so that they can imitate how we grow and learn.
Machine learning is primarily used by data scientists to prepare and analyze a massive amount of data. This allows a data scientist to reach key insights in a fraction of the time it would take to evaluate all that data manually. Machine learning allows the computer to learn and adapt based on this constant stream of data, all without our help. There are three main types of machine learning:
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- Includes clustering (market segmentation) and [anomaly detection]
- Helps to discover hidden trends and structures in our data
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Supervised learning:
- Creates predictive models based on the training dataset (initial dataset)
- Includes regression and classification
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Reinforcement learning:
- Aims to create intelligence in a system so that it may interact with the surrounding environment (e.g., self-driving cars)
- Is not supported by PyCaret
- Is supported by Python libraries like Tensorforce and Keras-RL
Machine learning models can be trained to find solutions using data patterns to deal with problems too complex for humans to develop an algorithm for. You can thank machine learning algorithms if you’ve experienced any of these moments:
- LinkedIn knowing exactly whom to suggest as a potential connection
- Music services knowing what new music you’d enjoy
- GPS services being able to accurately predict traffic
- A search engine knowing which websites are most relevant for your question
Get hands-on with Machine learning today.#
Try one of our 300+ courses and learning paths: Become a Machine Learning Engineer.
What is PyCaret?#
PyCaret is one of several Python libraries created for machine learning. (Others include NumPy, Keras, and Pandas. It is this vast collection of libraries and modules that have distinguished Python as a favorite among data scientists. PyCaret was inspired by the popular Caret package of R and joins the other renowned modules of Python. Caret is an acronym that stands for Classification And REgression Training. The acronym refers to both libraries’ ability to automate the machine learning pipelines for classification and regression problems. PyCaret comes with a set of modules that contain a variety of functions for specific machine learning tasks. A dataset that contains a classification problem will primarily use the classification module. There are also PyCaret modules for unsupervised learning, including anomaly detection, clustering, and natural language processing.
Each module houses specific algorithms for each distinction of machine learning while still recognizing universally used functions. For example, the create_model function will train and evaluate models in all PyCaret modules.
PyCaret is an open-source and low-code machine learning library. Being “low-code” refers to the automation of certain aspects of the development process, therefore reducing dependencies on the usual process of hand-coding. Low-code modules make it easier for those without specific training to participate in machine learning tasks. With low-code platforms, inexperienced employees can take more ownership and control over projects and produce required results. Even if you’re a seasoned developer, you can use low-code tools to accomplish more in far less time.
PyCaret also seeks to bypass some of the tedious processes of machine learning through automations. Some PyCaret automations that can be performed with a simple command include:
- Analyzing and comparing standard models
- Automatic model hyperparameters tuning
- Data transformation (converting raw data sets into usable formats)
- Model selection
- Training models
- Experiment logging
PyCaret is a Python wrapper that is built on other machine learning libraries and frameworks such as Scikit-learn, LightGBM, Catboost, and XGBoost. Because PyCaret works seamlessly with existing modules and programs, there is no steep learning curve to conquer. This also means that you can transport work done with PyCaret between multiple frameworks and libraries. In addition, PyCaret’s single API flattens the learning curve further and makes communication even more seamless.
Why use PyCaret for machine learning?#
This question doesn’t require too much analysis. Why wouldn’t you want to replace hundreds of lines of code with a few? If machine learning is already considered a champion sprinter in the world of data science, then PyCaret can speed up building machine learning projects even more. Not only is it faster, but simpler too. PyCaret provides a tremendous step forward in making the big data capabilities of machine learning more accessible.
PyCaret was designed with the “citizen data scientist” in mind. PyCaret simplifies the machine learning process so that someone who isn’t a highly skilled data scientist can handle sophisticated analytical tasks. Due to the rising dependence on machine learning across many industries, skilled data scientists are becoming increasingly scarce as they get scooped up by competing companies. But with tools like PyCaret, business analysts need no longer rely on the small expert community to get the predictive analysis they need.
Get hands-on with Python Machine Learning today.#
Try one of our 300+ courses and learning paths: A Practical Guide to Machine Learning with Python.
If you’re a beginner looking to get into machine learning, this is obviously great news. If you’re a skilled data scientist, then this is still great news. Being able to hire from a bigger pool of people who can work with datasets will boost your productivity as a team leader. Making advanced technical skills and expertise available to everybody is something that we at Educative and PyCaret have in common, it seems.
PyCaret can obviously handle essential data science functions, such as data visualization, as well as machine learning algorithms and models. But what specifically can you do with PyCaret today? As with many Python libraries, plenty of interesting projects are out there just waiting for contributors. For instance, take a look at the FIFA Player Market Value Predictions and Wine Quality Dataset projects on GitHub. After a little practice, you could jump into projects like these to refine your PyCaret and Python machine learning skills!
If you’re an emerging data scientist looking to make your mark, then Kaggle competitions are a great place to start. Kaggle hosts a vast collection of machine learning competitions with a diverse range of topics and datasets to work with. No matter where you are in your machine learning journey, Kaggle hosts a competition that is a great fit for your skillset! Checking your model’s accuracy on the leaderboard is a convenient way to compare your machine learning abilities against your peers. Reaching the top of that competitive leaderboard is also a great chance to earn some bragging rights amongst the machine learning and data science community.
Wrapping up and next steps#
Machine learning is complex by nature, so it’s refreshing to work with a Python library designed to expand the field to so many more people. Eager to give PyCaret a shot? Downloading PyCaret is as easy as typing the command pip install pycaret [full].
Even with PyCaret, breaking into the field of machine learning still requires plenty of training. Be sure to check out our Simplifying Machine Learning with PyCaret in Python course if you want to get started with PyCaret!
Happy learning!
Continue learning about Python and machine learning#
· Deep learning vs. Machine learning
· 7 top Python libraries for data science and machine learning
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What is the difference between PyCaret and Caret?
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