Coursera Machine Learning by Andrew Ng is an online non-credit course authorized by Stanford University, to deeply understand the inner algorithms in Machine Learning. as well as for those who are the complete beginners in Machine Learning.
Machine learning is a core sub-area of artificial intelligence, it enables computers to get into a mode of self-learning without being explicitly programmed.
When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.
Coursera Machine Learning Review
Table of Contents
- Coursera Machine Learning Andrew Ng Provides
- Skills Gained
- Process of taking Coursera Machine Learning
- Advice for Applying Machine Learning
- What to do After Completing the Course
- Earn a Career Credential
- What is the best machine learning course on Coursera?
- Machine Learning Career Opportunities
- Starting a Career in Machine Learning
- Is Machine Learning Coursera free?
- How much is the Coursera Machine Learning course?
Coursera Machine Learning Andrew Ng Provides
The instructor of Coursera Machine Learning is Andrew Ng. He is the co-founder of Coursera, and an Adjunct Professor of Computer Science at Stanford University. Previously, he was head of the AI Division at Baidu (A Chinese research engine).
About 2.5M+ students have already enrolled for this course. You will learn most of the traditional machine learning algorithms and neural networks. Here’s a list of important sections you will learn from this online course.
- Linear Regression
- Polynomial Regression
- Logistic Regression
- Multi-class Classification
- Neural Network
- Support Vector Machine (SVM)
- K-means Clustering
- Primary Component Analysis (PCA)
- Anomaly Detection
- Recommender System
This online course helps you to get a broad introduction to machine learning, data mining, and statistical pattern recognition. You will gain skills in:
- Supervised Learning
Linear regression, logistic regression, neural networks, SVMs.
- Unsupervised Learning
K-means, PCA, Anomaly detection
- Special Applications/ Topics
Recommender system, large scale machine learning
- Advice on building a machine learning system
Bias/variance, regularization, evaluation of learning algorithm, learning curves, error analysis, ceiling analysis.
The most important part of the course is the highlights of all of the tools, tricks, and tips that you will need to build the state of the art ML system.
While solving a real problem using ML, you will often find yourself stuck at some issues, this is where these tools will come to rescue.
Process of taking Coursera Machine Learning
This course is very interactive and highly involving, you should be ready to spend 5–7 hours/week to get the most out of this course.
This Coursera machine learning will take 11 weeks long and it is worth 11 weeks for you to gain skills. The course includes:
- Video Lecture and Quizzes
Each lecture consists of multiple videos with an average length of 10–15 minutes. Almost every video has a quiz question to help you make sure that you understand the concept covered in the video. At the end of each lecture, there is also a quiz. Lecture notes under the resource section provide a great reference for topics covered in the lecture.
- Programming Assignments
Assignments are the important and also fun part of this course, they come with the pre-setup environment in which you need to add the snippet of code, usually the implementation of concepts you learned during the lecture.
You can use Matlab (paid software) or Octave (free software) to do the assignments. If you already have access to Matlab then use it. If not, no need to spend the money just use octave. The programming assignment lets you implement the stuff you learned from the lecture videos from scratch.
Advice for Applying Machine Learning
In this great course, 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.
The course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
This course has many of the greatest Machine Learning algorithms that you can use to work in many applications.
What to do After Completing the Course
After the completion of this course successfully, you will have an expert level of understating concepts of ML. But there is a lot more to do on the implementation side.
Andrew did a great job to explain for the most important sections in Machine Learning which will always help you to add value to get a perfect job. The sections list as:
- Neural Network
- Convolutional Neural Network
- Recurrent Neural Network
Earn a Career Credential
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page – from there, you can print your Certificate or add it to your LinkedIn profile.
What is the best machine learning course on Coursera?
The best machine learning course on Coursera is
- Machine Learning Certification by Stanford University
- Deep Learning Certification by deeplearning.ai
These Career Credentials will help you to unlock access to work in top universities and organizations as well as you can get a chance to get a career credential from the world’s best educational institution.
Machine Learning Career Opportunities
Starting a career in Machine Learning is not very hard. Quality material is available in this online, all you have to do stay motivated and patient, at the end it all worth it.
In modern times, Machine Learning is one of the most popular career choices. Machine Learning is very popular as it reduces a lot of human efforts and increases machine performance by enabling machines to learn for themselves. Consequently, there are many career paths in Machine Learning that are popular and well-paying such as;
- Machine Learning Engineer
- Machine learning Researcher
- Data Scientist
- NLP (Natural Language Processing) Scientist
- Business Intelligence Developer
- Human-Centered Machine Learning Designer
Starting a Career in Machine Learning
Starting a career in Machine Learning is not very hard. Quality material is available online for the course, all you have to do stay motivated and patient, in the end, it all worth it.
You need to have a basic understanding of Linear Algebra and Calculus in order to make an intuition about the inner workings of the algorithms. This Machine Learning Course worth 11 Weeks of your life.
Is Machine Learning Coursera free?
This is not a free course, but you can apply for financial aid to get it for free.
How much is the Coursera Machine Learning course?
You can still audit the course for free, but you won’t have access to the assignments. To get the certification it needs $80.
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Farzana Ahmed Sabera, Working as Digital Marketing Executive at Reinforce Lab. Writer at JA DIRECTIVES with diverse knowledge of writing content and articles. Completed Bachelor of Science in Computer Science & Engineering, from University of Asia Pacific, Dhaka, Bangladesh. Done several research works focused on Digital Image Processing. Published research paper on International Journal. Always love to work with new technologies.