Datasets from real-world scenarios are important for building and testing machine learning models. You may just want to have some data to experiment with an algorithm. You may also want to evaluate your model by setting up a benchmark or determining its weaknesses using different sets of data. Sometimes, you may also want to create synthetic datasets, where you can test your algorithms under controlled conditions by adding noise, correlations, or redundant information to the data. In this post, we’ll […]

Compared to other programming exercises, a machine learning project is a blend of code and data. You need both to achieve the result and do something useful. Over the years, many well-known datasets have been created, and many have become standards or benchmarks. In this tutorial, we are going to see how we can obtain those well-known public datasets easily. We will also learn how to make a synthetic dataset if none of the existing datasets fits our needs. After […]

When working on code, whether we know it or not, we often come across the decorator design pattern. This is a programming technique to extend the functionality of classes or functions without modifying them. The decorator design pattern allows us to mix and match extensions easily. Python has a decorator syntax rooted in the decorator design pattern. Knowing how to make and use a decorator can help you write more powerful code. In this post, you will discover the decorator […]

import datetime   import tensorflow as tf import matplotlib.pyplot as plt import numpy as np import numba   def tSNE(X, ndims=2, perplexity=30, seed=0, max_iter=500, stop_lying_iter=100, mom_switch_iter=400):     “”“The t-SNE algorithm   Args: X: the high-dimensional coordinates ndims: number of dimensions in output domain     Returns:         Points of X in low dimension     ““”     momentum =

When we talk about managing data, it is quite inevitable to see data presented in tables. With column header, and sometimes with names for rows, it makes understanding data easier. In fact, it often happens that we see data of different types staying together. For example, we have quantity as numbers and name as strings in a table of ingredients for a recipe. In Python, we have the pandas library to help us handle tabular data. After finishing this tutorial, […]

In the old days, it was a tedious job to collect data, and it was sometimes very expensive. Machine learning projects cannot live without data. Luckily, we have a lot of data on the web at our disposal nowadays. We can copy data from the web to create our dataset. We can manually download files and save them to the disk. But we can do it more efficiently by automating the data harvesting. There are several tools in Python that […]

Big data, labeled data, noisy data. Machine learning projects all need to look at data. Data is a critical aspect of machine learning projects, and how we handle that data is an important consideration for our project. When the amount of data grows, and there is a need to manage them, allow them to serve multiple projects, or simply have a better way to retrieve data, it is natural to consider using a database system. It can be a relational […]

After all the hard work developing a project in Python, we want to share our project with other people. It can be your friends or your colleagues. Maybe they are not interested in your code, but they want to run it and make some real use of it. For example, you create a regression model that can predict a value based on input features. Your friend wants to provide their own feature and see what value your model predicts. But […]

When we finish a Python project and roll it out for other people to use, the easiest way is to present our project as a command-line program. If you want to make it friendlier, you may want to develop a GUI for your program so people can interact with it with mouse clicks while it runs. Developing a GUI can be difficult as the model of human-computer interaction is complex. Therefore, a compromise is to create a web interface for […]