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.

In this course we aim AI enthuasist from any work dicipline. You can be a engineering studeny in Cımputer, Machine, Indsurty, .. Engineering Departments or you can be a biologist trying to find a new methodology for disease detection, or a finance expert who want to cluster his/her customers into segments.. You can use Machine Learning in any field of your real life! By studying the projects in this course you will have a general understanding about machine learning and its aim to use in real life. After this course you will have a clear concept of AI and machine learning in your head and it's upto you to deep dive into more detailed subjects of machine learning, deep learning or artificial intelligence.

Support Vector Machines (SVM) are one of the most powerful machine learning models around, and this topic has been one that students have requested ever since I started making courses. These days, everyone seems to be talking about deep learning, but in fact there was a time when support vector machines were seen as superior to neural networks. One of the things you'll learn about in this course is that a support vector machine actually is a neural network, and they essentially look identical if you were to draw a diagram. The toughest obstacle to overcome when you're learning about support vector machines is that they are very theoretical. This theory very easily scares a lot of people away, and it might feel like learning about support vector machines is beyond your ability.

Mathematical optimization (MO) technologies are being utilized today by leading global companies across industries – including aviation, energy, finance, logistics, telecommunications, manufacturing, media, and many more – to solve a wide range of complex, real-world problems, make optimal, data-driven decisions, and achieve greater operational efficiency. An increasing number of data scientists are adding MO into their analytics toolbox and developing applications that combine MO and machine learning (ML) technologies. In this series of webinars, we will show you how – with MO techniques – you can build interpretable models to tackle your prediction and classification problems. How to formulate an MO model. How to build an MO model using the Gurobi Python API.