Image by the author (made using Canva). Data science is one of the rapidly growing fields that demands that a data scientist grows up daily, and I can't see this demand slowing down anytime soon. It is an interdisciplinary field that can help us analyze the data around us to make our life better and our future brighter. Luckily, becoming a data scientist does not require a degree. As long as you are open to learning new things and willing to put in the effort and time, you can become a data scientist.

TensorFlow is a state-of-the-art, open source machine learning framework created by Google to design, build, and train Machine Learning and Deep learning models. TensorFlow has a comprehensive and flexible ecosystem of tools and community resources that make it easy to develop and train ML and Deep Learning models. I know the options out there; prerequisites and the skills you need to acquire to overcome the learning blocks. So, Please refer to the Closing Notes section at the tail end of this piece, where you will find helpful resources for bootstrapping your intellectual abilities. My goal in this piece is to help you find some interactive courses from the Notable Educators that will edify you with a solid understanding of TensorFlow.

If there are any problems, you should be able to just reload the page. The code and notebooks are in a GitHub repo. The video is here YouTube.

Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so. In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Natural language processing has garnered interest in helping people interact with computer systems to make sense and meaning of the world. In the area of visual analytics, natural language has been shown to help improve the overall cognition of visualization tasks. In this latest Data Science Central webinar, Vidya will discuss how natural language can be leveraged in various aspects of the analytical workflow ranging from smarter data transformations, visual encodings, autocompletion to supporting analytical intent. More recently, chatbot systems have garnered interest as conversational interfaces for a variety of tasks. Machine learning approaches have proven to be promising for approximating the heuristics and conversational cues for continuous learning in a chatbot interface.

Welcome to the exhaustive course series "MBA in Programming and Trending Technologies". Ready to know what it is all about? "Digishock 1.0" is the ultimate technology course in 2021 to make you a technology expert today. This is the first beginner's part course of getting involved with Machine Learning and the unique technologies associated with the field. Most of my students have been waiting for me to launch this course.

In a world where nearly all manual tasks are being automated, the definition of manual is changing. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. We are living in an era of constant technological progress, and looking at how computing has advanced over the years, we can predict what's to come in the days ahead. One of the main features of this revolution that stands out is how computing tools and techniques have been democratized. In the past five years, data scientists have built sophisticated data-crunching machines by seamlessly executing advanced techniques.

There are literally countless resources available to learn R -- blogs, online tutorials, Massive Open Online Courses (MOOCs), platforms, and books. For somebody new to the field of Data Science, it is thus hard to identify really great resources. While everybody has a different way of acquiring new knowledge, I believe that the following books are particularly useful, especially for beginners. This book is co-authored by Hadley Wickham who is best known for the hugely popular R package ggplot2. This book provides a well-written overview of how to import, tidy, transform and model data in R. The book is also available free of charge as an online version. It includes many exercises after each section which makes the book particularly interesting for beginners.

As the world of Data Science progresses, more engineers and professionals need to deploy their work. This can be due to testing, obtaining user input, demonstrating model capabilities, or deploying a model to production. Due to this, we need to understand and know how to take a Data Science model and deploy it to a Web App and API using some of the most in-demand and popular libraries, including Flask, NodeJS, and ReactJS. Being able to deploy models will make a DS more versatile and in-demand, but it will also benefit the development and ops teams within the company. In this course, we will take a DS model and learn how to deploy it in a practical and hands-on manner, allowing us to simulate a real-world scenario that can be applied to industry practices.

Data augmentation is a technique to increase the diversity of dataset without an effort to collect any more real data but still help improve your model accuracy and prevent the model from overfitting. In this post, you will learn to implement the most popular and efficient data augmentation procedures for object detection task using Python and OpenCV. Firstly, let's import several libraries and prepare some necessary subroutines before going ahead. The below image is used as a sample image during this post. Random Crop selects randomly a region and crops it out to make a new data sample, the cropped region should have the same width/height ratio as the original image to maintain the shapes of objects.