Introduction to Different Machine Leaning Tools

Introduction to Different Machine Leaning Tools

We have heard about tools used in many professions. A carpenter, a tailor, a cobbler, an electrician, a sportsman and most others, including you and me, have used our own set of tools in our professions at some or another point of time. In the...

We have heard about tools used in many professions. A carpenter, a tailor, a cobbler, an electrician, a sportsman and most others, including you and me, have used our own set of tools in our professions at some or another point of time. In the same manner, Machine Learning, too, has its set of tools. They perform much of the same functions that the tools in all these other professions serve: they make work faster, smother, and easier.

With regard to Machine Learning tools, the primary goal of a tool is to help the programmer or user deliver results from a Machine Learning project in a smooth manner. In this sense, Machine Learning tools go beyond being just algorithms, although implementing algorithms is a function of Machine Learning tools. More importantly, they can arm the user with vital capabilities that can be used at any stage of a Machine Learning project to facilitate work.

Seen in this sense, Machine Learning tools work best when they:

 Are adaptive;
 Have been developed by using industry best practices, and
 A well-knit community contributes to their development.

Now, a look at a few popular Machine Learning tools

So, which are the Machine Learning tools that are widely used across the technological world today? This is our pick of the Machine Learning tools that are both popular and meet the criteria listed above:

Scikit-learn:

Scikit-learn originated in 2007, and is essentially developed for Machine Learning. This Open Source Machine Learning tool, written in Python, can be used for a number of Machine Learning models such as classification, clustering, regression and so on.

Google Cloud ML Engine:

Best-known for its suitability for training complex models; Google Cloud ML Engine offers all the elements of Machine Learning, such as predictive modeling, deep learning, and model building and training. It cuts down companies’ response time to customer emails.

Microsoft Cognitive Toolkit:

Microsoft claims that the Microsoft Cognitive Toolkit can train deep learning algorithms to think like humans. The Microsoft Cognitive Toolkit can handle data from BrainScript, C++, or Python. It comes with a few other features, such as integrability with Azure, interoperability with NumPy, and efficient utilization of resources.

Amazon Machine Learning:

This managed service is essentially suited for helping to make predictions out of Machine Learning models. Towards facilitating this end, it uses visualization tools and wizards. Batch predictions, Data sources, Machine Learning models, Real-time predictions and Evaluations are some of its core concepts.

PyTorch:

Torch based PyTorch uses the Autograd Module for building neural networks, to do which, it provides a variety of optimization algorithms. It can be used on the cloud, and comes with tools, libraries, and distributed learning.
Do Machine Learning tools fascinate you? Are you looking for learning that will take you headlong into this field? Try this vast spread of online courses in Machine Learning from Simpliv, the learning platform. You can enroll for any of these courses to start making an impact in the area of Machine Learning. These course are designed to help you find your feet in Machine Learning.

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Machine Learning Full Course - Learn Machine Learning

Machine Learning Full Course - Learn Machine Learning

This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.

Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial

It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.

Below topics are explained in this Machine Learning course for beginners:

  1. Basics of Machine Learning - 01:46

  2. Why Machine Learning - 09:18

  3. What is Machine Learning - 13:25

  4. Types of Machine Learning - 18:32

  5. Supervised Learning - 18:44

  6. Reinforcement Learning - 21:06

  7. Supervised VS Unsupervised - 22:26

  8. Linear Regression - 23:38

  9. Introduction to Machine Learning - 25:08

  10. Application of Linear Regression - 26:40

  11. Understanding Linear Regression - 27:19

  12. Regression Equation - 28:00

  13. Multiple Linear Regression - 35:57

  14. Logistic Regression - 55:45

  15. What is Logistic Regression - 56:04

  16. What is Linear Regression - 59:35

  17. Comparing Linear & Logistic Regression - 01:05:28

  18. What is K-Means Clustering - 01:26:20

  19. How does K-Means Clustering work - 01:38:00

  20. What is Decision Tree - 02:15:15

  21. How does Decision Tree work - 02:25:15 

  22. Random Forest Tutorial - 02:39:56

  23. Why Random Forest - 02:41:52

  24. What is Random Forest - 02:43:21

  25. How does Decision Tree work- 02:52:02

  26. K-Nearest Neighbors Algorithm Tutorial - 03:22:02

  27. Why KNN - 03:24:11

  28. What is KNN - 03:24:24

  29. How do we choose 'K' - 03:25:38

  30. When do we use KNN - 03:27:37

  31. Applications of Support Vector Machine - 03:48:31

  32. Why Support Vector Machine - 03:48:55

  33. What Support Vector Machine - 03:50:34

  34. Advantages of Support Vector Machine - 03:54:54

  35. What is Naive Bayes - 04:13:06

  36. Where is Naive Bayes used - 04:17:45

  37. Top 10 Application of Machine Learning - 04:54:48

  38. How to become a Machine Learning Engineer - 04:59:46

  39. Machine Learning Interview Questions - 05:09:03

Machine Learning Tutorial - Learn Machine Learning - Intellipaat

Machine Learning Tutorial - Learn Machine Learning - Intellipaat

This Machine Learning tutorial for beginners will enable you to learn Machine Learning algorithms with python examples. Become a pro in Machine Learning.

Mastering the Machine Learning Course would easily develop one's career. This is the reason why studying Machine Learning Tutorial becomes so important in the career of a particular student.
Making a part of the machine learning course would enact and studying the Machine Learning Tutorial would make one carve out a new niche.

The Common myths about Machine Learning by Rebecca Harrison

The Common myths about Machine Learning by Rebecca Harrison

Machine learning is changing the dimensions of business in many industries. A report projects that the value added by machine learning systems shall reach up to $3.9 Trillion by 2022.Machine lear...

Machine learning is proving it's worth in many industries like manufacturing, financial services, healthcare, and retail, to name a few. We hope that we have dispelled some of the myths associated with Machine Learning. It wouldn't be MLan incorrect to say that we have both overestimated and underestimated the potential of Machine learning systems.