Machine Learning can be easy and intuitive — here’s a complete from-scratch guide to Simple Linear Regression
Linear regression is the simplest algorithm you’ll encounter while studying machine learning. If we’re talking about simple linear regression, you only need to find values for two parameters — slope and the intercept — but more on that in a bit.
Today you’ll get your hands dirty implementing simple linear regression algorithm from scratch. This is the first of many upcoming from scratch articles, so stay tuned to the blog if you want to learn more.
Today’s article is structured as follows:
- Introduction to Simple Linear Regression
- Math Behind Simple Linear Regression
- From-Scratch Implementation
- Comparison with Scikit-Learn
- Conclusion
Easily visualize data beyond the 2nd dimension with Radar Charts — implemented in both Matplotlib and Plotly
Visualizing data beyond two dimensions isn’t a good idea — most of the time. That’s where radar charts come in, enabling you to visually represent one or more groups of values over multiple identically scaled variables.
Today you’ll learn how radar charts can visualize data across multiple dimensions, both with Matplotlib and Plotly. You’ll also learn what radar charts are and the pros and cons of using them.
The article is structured as follows:
- Introduction to Radar Charts
- Pros and Cons of Radar Charts
- Radar Charts with Matplotlib
- Radar Charts with Plotly
- Conclusion
You can download the corresponding Notebook here.
Introduction to Radar Charts
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An easy and straightforward guide to machine learning and classification with decision trees.
Decision trees are among the most fundamental algorithms in supervised machine learning, used to handle both regression and classification tasks. In a nutshell, you can think of it as a glorified collection of if-else statements, but more on that later.
Today you’ll learn the basic theory behind the decision trees algorithm and also how to implement the algorithm in R.
The article is structured as follows:
- Introduction to Decision Trees
- Dataset Loading and Preparation
- Modeling
- Making Predictions
- Conclusion
Introduction to Decision Trees
Decision trees are intuitive. All they do is ask questions, like is the gender male or is the value of a particular…
Theory + Hands-on guide on implementing concurrent execution with Python
Sequential execution doesn’t always make sense. For example, there’s no point in leaving the program sitting idle if the outputs aren’t dependent on one another. That’s the basic idea behind concurrency — a topic you’ll learn a lot about today.
This article will teach you how you can speed up your Python code by running tasks concurrently. Keep in mind — concurrent execution doesn’t mean simultaneous. For more info on simultaneous (parallel) execution, check out this article.
This article is structured as follows:
- Introduction to threading
- Implementing threading — Sending 1000 requests
- The results
- Conclusion
Learn how to explain predictions of convolutional neural networks with PyTorch and SHAP
Black-box models are a thing of the past — even with deep learning. You can use SHAP to interpret the predictions of deep learning models, and it requires only a couple of lines of code. Today you’ll learn how on the well-known MNIST dataset.
Convolutional neural networks can be tough to understand. A network learns the optimal feature extractors (kernels) from the image. These features are useful to detect any patterns that help the network to classify images correctly.
Your brain isn’t that much different. It also uses a series of patterns to recognize objects in front of you. For…
Master the basics of SQL with PostgreSQL, DVD rental database, and this beginner-friendly guide.
SQL has been around for decades, and is a go-to language for data analysis and lookups. With the rise of data-related programming languages such as R and Python, it’s easy to use SQL only for a simple SELECT * statement and perform the filterings and aggregations later. While tempting, that’s not the best solution.
Today you’ll learn the basics of SQL through a ton of hands-on examples. You’ll need to have a PostgreSQL database installed to follow along.
The article is structured as follows:
- Dataset introduction
- Select data
- Filter data
- Sort data
- Match data
- Join and group data
- Conclusion
Dataset introduction
M1 Macs vs. Google Colab for basic deep learning tasks
There’s a lot of hype behind the new Apple M1 chip. So far, it’s proven to be superior to anything Intel has offered. But what does this mean for deep learning? That’s what you’ll find out today.
The new M1 chip isn’t just a CPU. On the MacBook Pro, it consists of 8 core CPU, 8 core GPU, and 16 core neural engine, among other things. Both the processor and the GPU are far superior to the previous-generation Intel configurations.
I’ve already demonstrated how fast the M1 chip is for regular data science tasks, but what about deep learning?
Short…
Comparison with Intel Macbook Pro in Python, Numpy, Pandas, and Scikit-Learn
The new Intel-free Macbooks have been around for some time now. Naturally, I couldn’t resist and decided to buy one. What follows is a comparison between the 2019 Intel-based MBP and the new one in programming and data science tasks.
If I had to describe the new M1 chip in a single word, I would be this one — amazing. Continue reading for a more detailed description.
Data science aside, this thing is revolutionary. It runs several times faster than my 2019 MBP while remaining completely silent. I’ve run multiple CPU exhaustive tasks, and the fans haven’t kicked in even…
From scratch to Grid Search — hands-on examples included.
Machine learning models can be quite accurate out of the box. But more often than not, the accuracy can improve with hyperparameter tuning.
Hyperparameter tuning is a lengthy process of increasing the model accuracy by tweaking the hyperparameters — values that can’t be learned and need to be specified before the training.
Today you’ll learn three ways of approaching hyperparameter tuning. You’ll go from the most manual approach towards a GridSearchCV class implemented with the Scikit-Learn library.
The article is structured as follows:
- Dataset loading and preparation
- Manual hyperparameter tuning
- Loop-based hyperparameter tuning
- Hyperparameter tuning with GridSearch
- Conclusion
Create PDF reports with beautiful visualizations in 10 minutes or less
Reports are everywhere, so any tech professional must know how to create them. It’s a tedious and time-consuming task, which makes it a perfect candidate for automation with Python.
You can benefit from an automated report generation whether you’re a data scientist or a software developer. For example, data scientists might use reports to show performance or explanations of machine learning models.
This article will teach you how to make data-visualization-based reports and save them as PDFs. To be more precise, you’ll learn how to combine multiple data visualizations (dummy sales data) into a single PDF file.
And the best…
