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For those who already have a basic understanding of machine learning, you should start with the advance machine learning videos. These videos will introduce you to various machine learning libraries, modeling techniques and other advanced concepts of machine learning. It covers theoretical & practical concepts on supervised, unsupervised and deep learning algorithms. It will introduce you to sentimental analysis, recommendation system, predicting stock prices, create neural network using python & tensorflow and introduction to genetic algorithms.

Introduction to Machine Learning with scikit-learn - Machine Learning Mastery


The scikit-learn library is one of the most popular platforms for everyday machine learning and data science. The reason is because it is built upon Python, a fully featured programming language. But how do you get started with machine learning with scikit-learn. Kevin Markham is a data science trainer who created a series of 9 videos that show you exactly how to get started in machine learning with scikit-learn. In this post you will discover this series of videos and exactly what is covered, step-by-step to help you decide if the material will be useful to you.

The Guerrilla Guide to Machine Learning with R


Sure, there are lots of tutorials and overviews on gaining the insight you need into picking up machine learning, but many (most?) of them take the long view: get a foundation first, learn the basics next, then learn a bit of complementary theory before getting too far ahead of yourself in practical terms, take a step back, try your hand at a few examples, undertake a project on your own... This is all great advice, and a great approach to learning... well, almost anything. But let's say you're not starting from scratch. Or you don't have the patience to go through all of the motions. Let's say you want to hit the ground running and scramble under pressure to learn everything right now.

Learning AI/ML: The Hard Way - DZone AI


Data science, Artificial Intelligence (AI) and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner's hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.