Welcome to our 7-part mini-course on data science and applied machine learning! Over these 7 chapters, our goal is to provide you with an end-to-end blueprint for applied machine learning, while keeping this as actionable and succinct as possible. With that, let's get started with a bird's eye view of the machine learning workflow. One really cool (optional) challenge you can do in the next hour is training your first machine learning model! That's right, we've put together a complete step-by-step tutorial for training a model that can predict wine quality.
The course contains more than 4 hours of content and 2 articles. Its step by step approach is great for beginners and Martin has done a wonderful job to keep this course hands-on and simple. You will start by setting up your own development environment by installing the R and RStudio interface, add-on packages, and learn how to use the R exercise database and the R help tools. After that, you will learn various ways to import data, first coding steps including basic R functions, loops, and other graphical tools, which is the strength of R The whole course should take approx.
If the math behind data science is an enigma, the Khan Academy is a great place for insight. There are courses for different levels, and Sal Khan's relaxed delivery will get you through even the most difficult concepts (I think I have a small crush on him after the hours I've spent listening to his narratives!).
Many programmers are moving towards data science and machine learning hoping for better pay and career opportunities -- and there is a reason for it. The Data scientist has been ranked the number one job on Glassdoor for last a couple of years and the average salary of a data scientist is over $120,000 in the United States according to Indeed. Data science is not only a rewarding career in terms of money but it also provides the opportunity for you to solve some of the world's most interesting problems. IMHO, that's the main motivation many good programmers are moving towards data science, machine learning, and artificial intelligence. If you are in the same boat and thinking about becoming a data scientist in 2018, then you have come to the right place.
Udemy – Python for Data Science and Machine Learning Bootcamp online course coupon, Learn how to use NumPy, Pandas, Seaborn, Matplotlib, Plotly, Scikit-Learn, Machine Learning, Tensorflow, and more! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning on Udemy!
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
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.
One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts by implementing practical exercises which are based on live examples.