So far until mid of the year 2017 has been the time of “Machine Learning and Deep Learning”. We have seen any semblance of Big Giants Google, Facebook, Amazon and numerous more turn out in open and recognize the effect machine learning and Deep learning had on their business.
How to choose your Self-learning way?
Many final year Engineering students asked me “How should I start learning machine learning?” after seeing my earlier post Six-Easy-Steps-to-Learn-Artificial-Learning.
There can not be only one response to their question. One needs to choose their own particular structure and day and age to get settled with machine learning. Through this post, I need to enable you to achieve that safe place.
In this article, I have aggregated mainstream and most saw machine learning recordings, instructional exercises, and courses until June 2017. I need to help you to begin with machine learning and pick up skill building prescient models utilizing machine learning. You are allowed to choose your own structure and day and age to watch these recordings.
For the individuals who as of now have a fundamental comprehension of machine learning, you should begin with the advanced machine learning recordings. These recordings will acquaint you with different machine learning libraries, demonstrating procedures and other propelled ideas of machine learning.
I have listed 10 Top Videos, Tutorials & Courses on Machine Learning for Beginners and Advanced.
Basic – Machine Learning Videos
1. What is Artificial Intelligence Exactly?
Artificial Intelligence is a way to make machines sufficiently brilliant to take activities all alone. There is a great deal of buzz around AI yet individuals regularly pose the inquiry what is AI precisely? Here is a concise video which takes you to the cause of AI. Figure out how AI has advanced into a standard subject and how its different applications are changing the world.
2. Machine Learning – CS50 2016
This is a video from CS50 course instructed at Harvard University. In this video, the speaker acquaints you with machine learning and its applications. It is a straightforward prologue to machine learning and it is influencing our lives today. Figure out how machine learning is being connected for building web crawlers, picture acknowledgment, voice acknowledgment, and normal dialect preparing. This instructional exercise will show you picture arrangement with Python and content grouping.
3. Machine Learning Tutorial
This tutorial aims to provide an introduction to machine learning and scikit-learn “from the ground up”. It starts with core concepts of machine learning, some example uses of machine learning, and how to implement them using scikit-learn. Going in detail through the characteristics of several methods, we will discuss how to pick an algorithm for your application, how to set its parameters, and how to evaluate performance.
4. Practical Machine Learning Tutorial with Python
The objective of this course is to give you a wholistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.
In this series, the speaker will be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.
For each major algorithm that is covered, the speaker will discuss the high-level intuitions of the algorithms and how they are logically meant to work. Next, he’ll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, he’ll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.
Most of the machine learning algorithms are actually quite simple since they need to be in order to scale to large datasets. The math involved is typically linear algebra. You will also need Scikit-Learn and Pandas installed, along with others that we’ll grab along the way.
5. Statistical Machine Learning Course
In this course from Carnegie Mellon University, it will take you through nuts and bolts of machine learning and factual modeling. This course is most appropriate for understudies with a sound foundation in insights and science. Nearby, there are assignments and arrangement which would additionally enhance your ideas.
6.Introduction – Learn Python for Data Science
Here’s yet another instructional exercise to learn Python for information science. This series will teach you Python and Data Science at the same time! In this video, we install Python and our text editor (Sublime Text), then build a gender classifier using the sci-kit learn library in just about 10 lines of code. This series will acquaint you with wistful investigation, proposal framework, foreseeing stock costs, make neural system utilizing Python and tensorflow and introduction to hereditary calculations.
7.Data Analysis in Python with Pandas
Pandas is a full-featured Python library for data analysis, manipulation, and visualization. This video series is for anyone who wants to work with data in Python, regardless of whether you are brand new to pandas or have some experience. Each video will answer a student question about pandas using a real dataset, which is available online so you can follow along!
Machine Learning: Advanced
8. Machine Learning Recipes
There are 7 Machine Learning Recipes in this series.
Machine learning is making frameworks so keen that they are getting nearer and nearer to replacing people. Six lines of Python is all it takes to write your first machine learning program! In this episode, speaker briefly introduces what machine learning is and why it’s important. Then, he will follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up. In these series of videos, you will find out about tree perception, scikit-learn, tensorflow, how to fabricate your own particular classifier, what is the most exact components for your model.
9.Machine Learning with Text in scikit-learn
Although numeric data is easy to work with in Python, most knowledge created by humans is actually raw, unstructured text. By learning how to transform text into data that is usable by machine learning models, you drastically increase the amount of data that your models can learn from. In this tutorial, he’ll build and evaluate predictive models from real-world text using scikit-learn. (Presented at PyCon on May 28, 2016.)
10.Introduction to Machine Learning on Apache Spark MLlib
Speaker: Juliet Hougland, Senior Data Scientist, Cloudera
Spark MLlib is a library for performing machine learning and associated tasks on massive datasets. With MLlib, fitting a machine-learning model to a billion observations can take only a few lines of code, and leverage hundreds of machines. This talk will demonstrate how to use Spark MLlib to fit an ML model that can predict which customers of a telecommunications company are likely to stop using their service. It will cover the use of Spark’s DataFrames API for fast data manipulation, as well as ML Pipelines for making the model development and refinement process easier.
Additionally I have added few more interesting new Machine Learning videos below.
Now that you know, all the popular and must videos in machine learning.I would also like to know your feedback on this post. Kindly, drop in your comments below and share your opinions.
I hope this article was a great value add to your knowledge.
Through this post, I wanted to ensure that you’re ahead of your learning goal. If there is any particular tool or technique you would like to focus on in 2017 then tell me which are they.