The Data Science Blogathon by Analytics Vidhya began with a simple mission: To bring together a large community of data science enthusiasts to share their knowledge with the world. With 4000 articles under our belt on various topics such as Data Science, Machine Learning, Deep Learning, Data Lakes, and Data Engineering published by over 700 authors who are avid data science enthusiasts, students, professionals and researchers from across the globe. We bring to you the 20th edition of the Data Science Blogathon. This month's Data Science Blogathon brings you more rewards for you through our special referral programme. Yes, you read that right!

We are excited to announce that our Data Science Blogathon 19th Edition is LIVE. The Data Science Blogathon by Analytics Vidhya began with a simple mission: To bring together a large community of data science enthusiasts to share their knowledge with the world. With 4000 articles under our belt on various topics such as Data Science, Machine Learning, Deep Learning, Data Lakes, and Data Engineering published by over 700 authors who are avid data science enthusiasts, students, professionals and researchers from across the globe. We bring to the 19th edition of the Data Science Blogathon, this time with a new reward system. Choose the Data Science content you want to create and win for each published article.

Kaggle is synonymous with competitions and hackathons in the world of data science, but it is also a great resource to learn more about the field through community-driven notebooks. In contrast to textbooks and lectures, Kaggle notebooks or kernels provide data scientists with tutorials in their language. These are essentially Jupyter notebooks that run in the browser free of charge and without even needing to set up a local environment for Jupyter. In addition, these notebooks explore and run machine learning code and discover vast public and open-sourced repositories. While there are hundreds of thousands of notebooks on Kaggle, all data enthusiasts must-read are the top eight underrated notebooks.

Are you ready to master Machine Learning techniques and Kick-off your career as a Data Scientist?! You came to the right place! Machine Learning skill is one of the top skills to acquire in 2019 with an average salary of over $114,000 in the United States according to PayScale! The total number of ML jobs over the past two years has grown around 600 percent and expected to grow even more by 2020. In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques.

In this article, we will discuss the mathematical intuition behind Naive Bayes Classifiers, and we'll also see how to implement this on Python. This model is easy to build and is mostly used for large datasets. It is a probabilistic machine learning model that is used for classification problems. The core of the classifier depends on the Bayes theorem with an assumption of independence among predictors. That means changing the value of a feature doesn't change the value of another feature.

I have been in the data science field for the last half-decade when python programming came into the trend. Back then, in 2016, neural networks and deep learning were just some buzzy words. At that time, there was a hype about Google self-driving cars and reinforcement learning. But, most of the data science enthusiasts were not even aware of the working of neural networks. Today in 2021, most companies are adopting a data science strategy to make more revenue by automating different scenarios and replacing dozens of IT people with a single data scientist who can automate the task of those IT people using various automating tools like BluePrism, UI Path, Python and machine learning algorithms.

Continuous learning and applying our knowledge can be powerful and critical success factors for achieving our professional goals. The Cognitive Class AI offers a wide variety of professional learning paths, as free of charge, to learners globally. In this article, I provide you with some prominent learning path samples with links so that you commence achieving your 2020 professional education and career development goals. I also provide you with a list of sample industry badges that you can earn by undertaking these online training courses. The badges can help you promote your knowledge, skills, experience, and expertise globally hosted in a centralised industry recognised digital program governance organisation such as Credly's Acclaim which is the world's largest network of individuals and organizations using verified achievements to unlock opportunities. You can join millions of professionals in sharing your achievements online with a simple link.

It is not just leading enterprises but even mid-sized firms that are investing heavily in data science and big data projects. And that's why executing a data science model with the correct predictions has become one of the top priorities for data science teams. In this article, we list down 5 common mistakes while exploring a data analysis and how to avoid them. It is very crucial to choose the right visualisation tool. Most of the time, data scientists fail to focus on visualising the data while concentrating more on the technical aspects of data analysis. In order to monitor the exploratory data analysis or representing the final results in an eye-catching way, it is very important to choose the right kind of visualisation of the data in the model.

Data science has evolved into one of the most lucrative career options over the past few years. It will, therefore, come as no surprise that data scientists are one of the top paid professionals in the industry. In fact, they are hired after a lot of thought and diligence. This sometimes leaves them with very less room for mistakes. On the other hand, this is even applicable for beginners who wish to perfect data science.

AI coupled with the right deep learning framework has truly amplified the overall scale of what businesses can achieve and obtain within their domains. The machine learning paradigm is continuously evolving. The key is to shift towards developing machine learning models that run on mobile in order to make applications smarter and far more intelligent. Deep learning is what makes solving complex problems possible. As put in this article, Deep Learning is basically Machine Learning on steroids.