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The Complete R Programming for Data Science - 7 courses in 1

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Free Coupon Discount - The Complete R Programming for Data Science - 7 courses in 1, Beginner to Pro: Learn R programming language, R studio, ggplot2, dplyr, statistics, caret, machine learning, projects Created by Numyard Data Science Team, Selva Prabhakaran, Codestars by Rob Percival Preview this Udemy Course - GET COUPON CODE In The Complete R-Programming for Data Science & Statistics program, we have carefully designed 7 Full-Fledged courses into 1 Master Course of 200 videos, 50 R-Packages, Core Machine Learning and statistics concepts, 75 practice problems and 2 Industrial projects By end of this course, you will be able to solve Industry Data Science project in R starting including model building, model diagnostics and presenting actionable business insights Here's how you will progress across the 7 courses in the Master Course: Getting started with R-programming: First, you will learn to write your own R code and perform basic programming tasks. You will begin with the base R programming course, where you will master the fundamental data structures such as vectors, lists, dataframes, understand the core programming constructs and get enough coding practice. You will also create full featured plots for data analysis using base graphics. Advanced coding with Tidyverse: Then you will move to advanced coding in R based on the tidyverse using the dplyr package. You will start using the elegant pipe syntax provided by the magrittr package and the data manipulation verbs.


Top 10 Artificial Intelligence YouTube Channels in 2020

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We know that Artificial Intelligence (AI) is the main force moving the society into the future described in the movies in the past couple of decades. There are new heights achieved every day in different fields using Artificial Intelligence methods. Artificial Intelligence is a huge field and contains a lot of subfields, that contain a lot of subfields themselves. There are huge amounts of sources that claim that can help you learn Artificial Intelligence. These sources come in different forms and types, from books, blogs, projects, videos, etc. Today we are going to talk about the video sources, more precisely YouTube videos.


Feel free to learn Artificial Intelligence & Machine Learning

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With the help of this list, any person who is interested in artificial intelligence or machine learning can feel free to learn all about it. In this course, the instructor is going to talk about the meaning behind the common AI terminology. It includes explanations about neural networks, machine learning, data science, and deep learning. Then the instructor will talk about what AI can and can't do realistically. Similarly, you will also get to understand how to spot opportunities to apply AI to different problems in your own organization.


6 Key Concepts in Andrew NG's "Machine Learning Yearning" 7wData

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If you are diving into AI and Machine Learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers. Machine Learning Yearning is about structuring the development of machine learning projects. The book contains practical insights that are difficult to find somewhere else, in a format that is easy to share with teammates and collaborators. Most technical AI courses will explain to you how the different ML algorithms work under the hood, but this book teaches you how to actually use them.


How I'd Learn Data Science If I Were To Start All Over Again

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A couple of days ago I started thinking if I had to start learning machine learning and data science all over again where would I start? The funny thing was that the path that I imagined was completely different from that one that I actually did when I was starting. I'm aware that we all learn in different ways. Some prefer videos, others are ok with just books and a lot of people need to pay for a course to feel more pressure. And that's ok, the important thing is to learn and enjoy it. So, talking from my own perspective and knowing how I learn better I designed this path if I had to start learning Data Science again.