Ahmad Anis is a passionate machine learning engineer and researcher currently working at redbuffer.ai. Beyond his day job, Ahmad actively engages with the machine learning community. He serves as a regional lead for Cohere for AI, a nonprofit dedicated to open science, and is an AWS community builder. Ahmad is an active contributor at Stackoverflow, where he has 2300+ points. He has contributed to many famous open-source projects, including Shap-E by OpenAI.
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.
Regularization techniques are crucial for preventing your models from overfitting and enables them perform better on your validation and test sets. This guide provides a thorough overview with code of four key approaches you can use for regularization in TensorFlow.
As a Data Scientist, you are already spending most of your time getting your data ready for prime time. Follow these real-world scenarios to learn how to leverage the advanced techniques in Python of list comprehension, Lambda expressions, and the Map function to get the job done faster.
Learn about the latest version of TensorFlow with this hands-on walk-through of implementing a classification problem with deep learning, how to plot it, and how to improve its results.
Google Colab is a widely popular cloud service for machine learning that features free access to GPU and TPU computing. Follow this detailed guide to help you get up and running fast to develop your next deep learning algorithms with Colab.
Learn Data Science for free in 2021
Do’s and Don’ts of Analyzing Time Series
Top 5 Free Machine Learning and Deep Learning eBooks Everyone should read
Introduction to Time Series Analysis in Python
4 ways to improve your TensorFlow model – key regularization techniques you need to know
Getting Started with TensorFlow 2
A Complete guide to Google Colab for Deep Learning
Start Your Machine Learning Career in Quarantine