The 36th AAAI Conference on Artificial Intelligence (AAAI2022) started on Tuesday 22 February and runs until Tuesday 1 March. Although we haven't yet had the official opening ceremony, the talks and posters are available to view, and the LatinX in AI event has taken place. Here, we round-up some thoughts from participants, and we look ahead to some of the other events planned for this week. If you are attending AAAI 2022, please come by to our tutorial (Feb 23, 2022, 10 pm – 11:30 pm IST) MQ3: Hate Speech: Detection, Mitigation and Beyondhttps://t.co/Uw1jIRyGmn A particularly relevant topic since healthcare raises unique problems & challenges that require new methodologies & ways of thinking.

Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease. All the important libraries you would need to work on Machine learning lifecycle. Full-fledged course on Statistics so that you don't have to take another course for statistics, we cover it all. Data cleaning and exploratory Data analysis with all the real life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course. All the mathematics behind the complex Machine learning algorithms provided in a simple language to make it easy to understand and work on in future. Hands-on practice on more than 20 different Datasets to give you a quick start and learning advantage of working on different datasets and problems. More that 20 assignments and assessments allow you to evaluate and improve yourself on the go. Total 10 beginner to Advance level projects so that you can test your skills.

This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course. Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks). While both of these have been around for quite some time, it's only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning. The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer. Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be.

This course is all about A/B testing. A/B testing is used everywhere. A/B testing is all about comparing things. If you're a data scientist, and you want to tell the rest of the company, "logo A is better than logo B", well you can't just say that without proving it using numbers and statistics. Traditional A/B testing has been around for a long time, and it's full of approximations and confusing definitions.

This course covers everything from how to install and import NumPy to how to solve complex problems involving array creation, transformations, and random sampling. The course is presented as a series of on-demand lecture style videos with lots of animated examples, code walkthroughs, and challenge problems to test your knowledge. Go as fast or as slow as you want. It's difficult to describe everything around us with just one number. The data we are consuming, product we use on daily basis, from non living organism to living organism require many feature to fully characterise and quantify it. So if you want to learn about fastest python based numerical multi dimensional data processing framework, which is the foundation for many data science package like pandas for data analysis, sklearn scikit-learn for machine learning algorithm, you are at right place.

Arrays are a powerful means of storing variables of the same data type (Integer, Float, String, etc.). To give you some context, if you have worked on Pandas DataFrames, which is a special case of 2 Dimensional Arrays, you would know what different operations you can perform and how you can handle datasets more effectively. Well with Arrays you can do most of that and much more and for that very reason they are used as the preferred Data Containers to run Machine Learning algorithms (in Modules such as Scipy and Scikit-learn). To simply put, "A good command on Arrays will take your understanding of Data Structures and their use to the next level", and this is exactly where this course comes in. Even if you've not worked on Arrays earlier, you can use this course to develop your understanding grounds-up.

The Common mistake by a data scientist is Applying the tools without the intuition of how it works and behaves. Having the solid foundation of mathematics will help you to understand how each algorithm work, its limitations and its underlying assumptions. With this, you will have an edge over your peers and makes you more confident in all the applications of Machine Learning, Data Science, and Deep Learning. It always pays to know the machinery under the hood, rather than being a guy who is just behind the wheel with no knowledge about the car. Linear Algebra is one of the areas where everyone agrees to be a starting point in the learning curve of Machine Learning, Data Science, and Deep Learning.. Its basic elements – Vectors and Matrices are where we store our data for input as well as output.

Why are Data Structures and Algorithms important?

Why are Data Structures and Algorithms important?

Python is an ideal first programming language for anyone interested in coding. Here are the top 20 Free Online Courses for Python from Udemy we've curated to help you learn Python. In this post you'll find 20 good beginners Python courses you can learn from and start your career as a software developer or web developer. All courses are free and you'll have lifetime access to the material! What better way to learn a new programming language than to dive right in? Python may be a general-purpose programming language, but it has specialized libraries that lend themselves to machine learning, artificial intelligence (AI), and scientific computing.