Machine Learning Online Course by Coursera is perhaps the most mainstream seminars on Coursera. The free online AI course is offered by Stanford University through Coursera.

The educator for this AI course is Andrew Ng. Andrew Ng is Co-author of Coursera and an Adjunct Professor of Computer Science at Stanford University.

Stanford University, one of the world’s driving instructing and examination establishments. It positions as one of the world’s top colleges.

AI is the study of getting PCs to act without being expressly modified. In the previous decade, AI has given us self-driving vehicles, reasonable discourse acknowledgment, viable web search, and a boundlessly improved comprehension of the human genome.

In this class, you will find out about the best AI strategies, and gain work on actualizing them and getting them to work for yourself. All the more significantly, you’ll find out about the hypothetical underpinnings of learning as well as gain the reasonable expertise expected to rapidly and capably apply these procedures to new issues. At long last, you’ll find out about some of Silicon Valley’s accepted procedures in development in accordance with AI and AI.

Course Details

Host countryOnline
Education LevelN/A
Financial coverageFully Funded
EligibilityInternational Students
DeadlineAll year round

Course Value

Machine Learning Online Course by Coursera provides various scholarship advantages. Below is a list of them:

  • Free of Cost
  • Unlimited access to this course
  • A PDF Certificate of Achievement 

Course Details

The Machine Learning Online Course by Coursera provides:

Week 1: Introduction

Welcome to Machine Learning! In this module, we present the center thought of encouraging a PC to learn ideas utilizing information—without being unequivocally modified. The Course Wiki is under development. Kindly visit the assets tab for the most complete and state-of-the-art data.

Week 2: Linear Regression with Multiple Variables

Imagine a scenario where your information has more than one worth. In this module, we show how straight relapse can be stretched out to oblige various information highlights. We additionally talk about accepted procedures for actualizing straight relapse.

Week 3: Logistic Regression

Strategic relapse is a strategy for grouping information into discrete results. For instance, we may utilize strategic relapse to arrange an email as spam or not spam. In this module, we present the idea of characterization, the expense work for calculated relapse, and the utilization of strategic relapse to multi-class grouping.

Week 4: Neural Networks: Representation

A neural organization is a model enlivened by how the mind functions. Today is generally utilized in numerous applications: when your telephone deciphers and comprehends your voice orders, all things considered, a neural organization is assisting with understanding your discourse; when your money a check, the machines that consequently perused the digits likewise utilize neural organizations.

Week 5: Neural Networks: Learning

In this module, we present the backpropagation calculation that is utilized to help learn boundaries for a neural organization. Toward the finish of this module, you will execute your own neural organization for digit acknowledgment.

Week 6: Advice for Applying Machine Learning

Applying AI practically speaking isn’t generally direct. In this module, we share best practices for applying AI practically speaking and talk about the most ideal approaches to assess the presence of the learned models.

Week 7: Support Vector Machines

Backing vector machines, or SVMs, is an AI calculation for grouping. We present the thought and instincts behind SVMs and talk about how to utilize them by and by.

Week 8: Unsupervised Learning

We utilize unaided figuring out how to construct models that assist us with understanding our information better. We talk about the k-Means calculation for bunching that empowers us to become familiar with the groupings of unlabeled information focuses.

Week 9: Anomaly Detection

Given countless information focuses, we may in some cases need to sort out which ones change fundamentally from the normal. For instance, in assembling, we might need to recognize imperfections or abnormalities. We show how a dataset can be demonstrated utilizing a Gaussian dissemination, and how the model can be utilized for peculiarity identification.

Week 10: Large Scale Machine Learning

AI works best when there is a bounty of information to use for preparing. In this module, we examine how to apply AI calculations with huge datasets.

Week 11: Application Example: Photo OCR

Distinguishing and perceiving articles, words, and digits in a picture is a difficult assignment. We examine how a pipeline can be worked to handle this issue and how to break down and improve the presentation of such a framework.

How to Apply– Process

To apply for the Machine Learning Online Course by Coursera:

Visit the official website and click on the Enroll for Free.

Note: The course is totally free but if you want a certificate then you will need to pay. If you cannot afford to pay for a certificate, you can apply for Coursera financial aid.

Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the “Enroll” button on the left. You’ll be prompted to complete an application and will be notified if you are approved

FAQs about Machine Learning Online Course by Coursera

What are the course details?

In this class, you will find out about the best AI strategies, and gain work on actualizing them and getting them to work for yourself. All the more significantly, you’ll find out about the hypothetical underpinnings of learning as well as gain the reasonable expertise expected to rapidly and capably apply these procedures to new issues. At long last, you’ll find out about some of Silicon Valley’s accepted procedures in development in accordance with AI and AI.

What is financial aid?

Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the “Enroll” button on the left. You’ll be prompted to complete an application and will be notified if you are approved

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