Top Free ML courses 2020

In this article, we will be looking at what machine learning is & why do we need machine learning? Also, we will be looking at Top Free ML courses 2020 to get you on board.

What is Machine Learning?

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. 

Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

Why Machine Learning?

Data is considered as the lifeblood for all the fields including science and business.

Machine Learning uses the previously collected data and accordingly trains and develops the model.

So, Machine Learning basically learns from data and makes necessary predictions and recommendations thus reducing the man-work.

Machine Learning is also, one of the most in-demand skills in the 21st century, & will be a prominent skill for more than 3 decades to come! 

Companies are always looking for candidates, who have some experience in ML since sooner or later, ML will be a basic pre-requisite for many jobs.

Applications Machine Learning:

  • Image and speech recognition.
  • Traffic signs recognition.
  • Recommendations system in the entertainment field as well as in products.
  • Email spam and Malware filtering.
  • Self-driven vehicles.
  • Personal virtual assistant and language translator.
  • Medical diagnosis.
  • Automatic stock market prediction.
  • Fraud detection.

Note: Read the complete article, so that you find the right course for yourself. You may require a student account with Coursera to access some of these free courses!

So, here are the Top Free ML courses 2020 for you to get started with: 

1.Machine Learning by Stanford:

Stanford University

This course is offered by Stanford. This course starts from the very beginning introductory section and covers all the aspects and topics required to get you on board with Machine Learning.

In this course, not only the theoretical concepts are covered but also practical implementation is covered, which is a plus point.

Also, Silicon Valley’s best practices in innovation & technology are also covered in this course.

The topics included in this course are data mining, supervised and unsupervised learning, neural networks, clustering, best practices used for machine learning etc.

Skills you will gain:

  • Logistic Regression
  • Artificial Neural Network
  • Machine Learning (ML) Algorithms
  • Machine Learning

Duration: 60 Hours

Rating: 4.9/5.0 (360k+ enrolled)Register Now

2.Machine Learning Specialization by UW:

UofW

This course is offered by the University of Washington. This specialization course will make you walk through some practical case studies, clustering, prediction, classification, and all the other aspects which are important in this field.

By this specialization, you yourself will be able to analyze large and complex datasets, build and improve your own model, and get outputs accordingly.

This is a perfect specialization for all those interested in Machine learning since the course covers some of the deep & advanced concepts, which will help you grasp ML more efficiently.



Skills you will gain:

  • Data Clustering Algorithms
  • Machine Learning
  • Classification Algorithms
  • Decision Tree
  • Python Programming
  • Machine Learning Concepts
  • Deep Learning
  • Linear Regression
  • Ridge Regression
  • Lasso (Statistics)
  • Regression Analysis
  • Logistic Regression

Duration: 6-7 Months

Rating: 4.7/5.0 (150k+ enrolled)Register Now

3.Advanced Machine Learning Specialization:

This specialization course is offered by the National Research University, especially for advanced learners. 

This course will introduce you to deep learning, machine learning, natural language understanding, computer vision, Bayesian methods, reinforcement learning, and many more.

Real-world problems are also being introduced in these courses.& is designed to lessen the gap between practical & theoretical concepts.

Skills you will gain:

  • Recurrent Neural Network
  • Tensorflow
  • Convolutional Neural Network
  • Deep Learning
  • Data Analysis
  • Feature Extraction
  • Feature Engineering
  • Xgboost
  • Bayesian Optimization
  • Gaussian Process
  • Markov Chain Monte Carlo (MCMC)
  • Variational Bayesian Methods

Duration: 10 Months.Register Now

4.Machine Learning by Columbia University

Columbia University

An advanced-level course, with various models and methods that can be applied to real-world concepts.

Major topics such as probabilistic versus non-probabilistic modeling and supervised versus unsupervised learning are also included.

Other topics included are matrix factorization, modeling, and training, clustering, etc. Methods such ask-means, Gaussian mixture models, Markov models, MAP interference, etc. are also included.

Skills you will gain:

  • Supervised learning techniques for regression and classification
  • Unsupervised learning techniques for data modeling and analysis
  • Probabilistic versus non-probabilistic viewpoints
  • Optimization and inference algorithms for model learning

Duration: 12 Weeks(151k+ Enrolled)Register Now

-Contributed by Dhwani Parekh, ScholarsXP always values your content!

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