Machine Learning Free Online Course 2020

Machine Learning Free Online Course 2020:

Applications are invited to apply for the Machine Learning Free Online Course 2020 at Coutsera the world’s best website to learn online. The free online machine learning course is offered by Stanford University through Coursera. Students who want to learn Machine Learning from all over the world are encouraged to enroll for this course.

The instructors for this machine learning course is Andrew Ng. Andrew Ng is Co-founder of Coursera and an Adjunct Professor of Computer Science at Stanford University. Stanford University, one of the world’s leading teaching and research institutions. It ranks as one of the world’s top universities. 

Machine Learning Free Online Course 2020 Details:

• Offered by: Stanford University, USA
• Course name: Machine Learning
• Course Fee: Free of Cost
• Access Mode: Online
• Eligible nationality: All Applicants
• Last Date: No Deadline.

• Machine Learning Online Course Benefits:

  • Course is offered 100% Free
  • You can learn online anytime

  About Machine Learning Free Course:

  Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.
  In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

• Online Machine Learning Course Syllabus: 

  What you will learn from this course
  Week 1: Introduction:
  Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.
  Week 2: Linear Regression with Multiple Variables:
  What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression.
  Week 3: Logistic Regression:
  Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.
  Week 4: Neural Networks: Representation:
  A neural network is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understands your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.
• Week 5: Neural Networks: Learning:
  In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition.
  Week 6: Advice for Applying Machine Learning:
  Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice and discuss the best ways to evaluate the performance of the learned models.
  Week 7: Support Vector Machines:
  Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use them in practice.
  Week 8: Unsupervised Learning:
  We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn the groupings of unlabeled data points.
  Week 9: Anomaly Detection:
  Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection.
• Week 10: Large Scale Machine Learning:
  Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply machine learning algorithms with large datasets.
  Week 11: Application Example: Photo OCR:
  Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system.

  How to Apply for the Free Course?

  Enrollment process is very easy. Visit link given below and create your account if you don’t have otherwise just log in into your account and enroll for the course and start learning.
  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

  Official Link