Machine learning and Data Science Student. Loves #Python and #C++. Student of CS at IIU. Team IJT ❤️. 🇵🇰 Writer for kdnuggets and TDataScience. Twitter: @AhmadMustafaAn1.
The process of mastering new knowledge often requires multiple passes to ensure the information is deeply understood. If you already began your journey into machine learning and data science, then you are likely ready for a refresher on topics you previously covered. This eight-week self-learning path will help you recapture the foundations and prepare you for future success in applying these skills.
If you are new to working with a deep learning framework, such as TensorFlow, there are a variety of typical errors beginners face when building and training models. Here, we explore and solve some of the most common errors to help you develop a better intuition for debugging in TensorFlow.
If you are working with big data, especially on your local machine, then learning the basics of Vaex, a Python library that enables the fast processing of large datasets, will provide you with a productive alternative to Pandas.
The full value of your deep learning models comes from enabling others to use them. Learn how to deploy your model to the web and access it as a REST API, and begin to share the power of your machine learning development with the world.
So much time and effort can go into training your machine learning models. But, shut down the notebook or system, and all those trained weights and more vanish with the memory flush. Saving your models to maximize reusability is key for efficient productivity.
If you are considering starting a career path in machine learning and data science, then there is a great deal to learn theoretically, along with gaining practical skills in applying a broad range of techniques. This comprehensive learning plan will guide you to start on this path, and it is all available for free.
Lift the curse of dimensionality by mastering the application of three important techniques that will help you reduce the dimensionality of your data, even if it is not linearly separable.
When handling time series data in your Data Science analysis work, a variety of common mistakes are made that are basic, but very important, to the processing of this type of data. Here, we review these issues and recommend the best practices.
There is always so much new to learn in machine learning, and keeping well grounded in the fundamentals will help you stay up-to-date with the latest advancements while acing your career in Data Science.
Data that is updated in real-time requires additional handling and special care to prepare it for machine learning models. The important Python library, Pandas, can be used for most of this work, and this tutorial guides you through this process for analyzing time-series data.
Machine learning and Data Science Student. Loves #Python and #C++. Student of CS at IIU. Team IJT ❤️. 🇵🇰 Writer for kdnuggets and TDataScience. Twitter: @AhmadMustafaAn1. 
How to deploy Machine Learning/Deep Learning models to the web - 05 Apr 2021
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