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 […]
Sponsored Post Building successful machine learning products requires mastering ML Strategy, including problem formulation, evaluation, and tactics for dealing with stakeholders and project uncertainties. Many ML projects fail not for technical reasons, but because of poor understanding of the difference between doing ML well technically and actually realizing business impact with an ML solution. Professor […]
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 […]
Data visualization is an important aspect of all AI and machine learning applications. You can gain key insights into your data through different graphical representations. In this tutorial, we’ll talk about a few options for data visualization in Python. We’ll use the MNIST dataset and the Tensorflow library for number crunching and data manipulation. To […]
Calculus for Machine Learning Crash Course. Get familiar with the calculus techniques in machine learning in 7 days. Calculus is an important mathematics technique behind many machine learning algorithms. You don’t always need to know it to use the algorithms. When you go deeper, you will see it is ubiquitous in every discussion on the […]
Python is a neat programming language because its syntax is simple, clear, and concise. But Python would not be so successful without its rich third-party libraries. Python is so famous for data science and machine learning that it has become a de facto lingua franca just because we have so many libraries for those tasks. […]
Unit testing is a method for testing software that looks at the smallest testable pieces of code, called units, which are tested for correct operation. By doing unit testing, we can verify that each part of the code, including helper functions that may not be exposed to the user, works correctly and as intended. The […]
Sponsored Post Me, a data scientist, and Jupyter notebooks. Well, our relationship started back then when I began to learn Python. Jupyter notebooks were my refuge when I wanted to make sure that my code works. Nowadays, I teach coding and do several data science projects and still, notebooks are the best tools for interactive […]
Serialization refers to the process of converting a data object (e.g., Python objects, Tensorflow models) into a format that allows us to store or transmit the data and then recreate the object when needed using the reverse process of deserialization. There are different formats for the serialization of data, such as JSON, XML, HDF5, and […]
Working on a machine learning project means we need to experiment. Having a way to configure your script easily will help you move faster. In Python, we have a way to adapt the code from a command line. In this tutorial, we are going to see how we can leverage the command line arguments to […]
