Are you looking for the **Best Online Courses on Machine Learning?**. But confused because of so many courses available online. So, don’t worry. Your search will end after reading this article. In this article, you will find the **20 Best Online Courses on Machine Learning**. So, give your few minutes to this article and find out the Best Online Courses on Machine Learning for you.

- Best Machine Learning Courses at Coursera
- Best Machine Learning Courses at Udacity
- Best Machine Learning Courses at Codecademy
- Best Machine Learning Courses at Datacamp
- Best Machine Learning Courses at Udemy
- Best Machine Learning Courses at Pluralsight
- Best Machine Learning Courses at edX
- Best Machine Learning Courses at Edureka

Machine Learning is very powerful and popular. Many people are shifting their careers into the ML field. The reason behind the popularity of Machine Learning is its power to **make useless data into more meaningful data.**

Machine Learning models allow us to predict of various outcomes from the data.

There are huge demands and high salaries in the Machine Learning field. That is the reason a lot of people are shifting their careers into Machine Learning.

But, the next question that comes is where to begin and from where to learn?

Right?

So, there are various resources that are available like **books, online courses, YouTube videos, Some Institutions, and many more.**

And then the next question that comes is, “Which resource is Best for Machine Learning?”

The answer depends upon various factors like- Your availability means if you are a working person, then it’s difficult to join any Institution.

The easiest way to learn Machine Learning is via **Online Sources or Via Books**.

Books are good for the Theoretical Understanding of Machine Learning. These are two books on Machine Learning you should have.

1. **An Introduction to Statistical Learning**– This book will give a** better understanding of Mathematics** for Machine Learning.

2. **Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow**– This book will teach you how to **implement Machine Learning algorithms in Python.**

But if you are a person who loves visual learning, then Online Courses are good for you. Online Courses are more interactive than books.

Before I discuss the **Best Online Courses On Machine Learning**, I would like to mention the criteria to call these courses “Best”.

**Criteria-**

- Rating of these Courses.
- Coverage of Machine Learning Topics.
- Engaging trainer and Interesting lectures.
- Number of Students Benefitted.
- Good Reviews from various aggregators and forums.

On these criteria, I have filtered out Courses from Different platforms like **Coursera, Udemy, Udacity, edX, Pluralsight, Edureka, Codecademy, and Data Camp.**

So, without wasting your time, let’s start finding Best Online Courses On Machine Learning for you.

**Best Machine Learning Courses at Coursera**

### 1. **Machine Learning **

**Provider-** Stanford University

**Instructor-** Andrew Ng

**Rating-** 4.9/5

**Time to Complete- **60 hours

This is one of the Best Online Courses for Machine Learning. This course is created by **Andrew Ng** the **Co-founder of Coursera, and an Adjunct Professor of Computer Science at Stanford University**.

This course provides you with a broad introduction to machine learning, data mining**, and statistical pattern recognition.**

All the math required for Machine Learning is well discussed in this course.

This course uses the open-source programming language **Octave**. Octave gives an easy way to understand the fundamentals of Machine Learning.

Now, let’s see What you will learn in this Course-

**Topics Covered-**

- Linear Regression with One Variable
- Linear Algebra Review
- Linear Regression with Multiple Variables
- Octave/Matlab Tutorial
- Logistic Regression
- Regularization
- Neural Networks: Representation
- Neural Networks: Learning
- Advice for Applying Machine Learning
- Machine Learning System Design
- Support Vector Machines
- Unsupervised Learning
- Dimensionality Reduction
- Anomaly Detection
- Recommender Systems
- Large Scale Machine Learning
- Application Example: Photo OCR

**Extra Benefits-**

- You will get a Shareable Certificate. Along with that, you will learn various case studies and applications. That will teach you how to apply machine learning algorithms to building
**smart robots.**

- You will also learn
**text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and others.**

**Who Should Enroll?**

- This Course is Most Suitable for
**Complete Beginners**. But people with some basic understanding of ML can also enroll.

**Cost of the Course-**

- This course is
**FREE to audit**but for a certificate, videos, quizzes, and programming assignments you have to enroll yourself for a Certificate. The cost of a**$79**.

**Interested to Enroll?**

If yes, then You can **Sign Up **here.

### 2. **Deep Learning Specialization**

**Provider-** deeplearning.ai

**Instructor-** Andrew Ng, Younes Bensouda Mourri, Kian Katanforoosh

**Rating-** 4.8/5

**Time to Complete- **4 months ( If you spend 5 hours per week)

This course is also taught by** Andrew Ng**. This is a Specialization Program that contains 5 courses.

This Deep Learning Specialization is an advanced course series for those who want to learn **Deep Learning and Neural Networks.**

**Python and TensorFlow** are used in this specialization program for Neural Networks. This is the best follow-up to Andrew Ng’s Machine Learning Course.

More than **250,000** learners from all over the globe have already enrolled in this Specialization Program.

Now, let’s see all the 5 courses of this Specialization Program-

**Courses Include-**

**Neural Networks and Deep Learning****Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization****Structuring Machine Learning Projects****Convolutional Neural Networks****Sequence Models**

Now, let’s see what benefits you will get after completing this Course?

**Extra Benefits-**

- You will get a Shareable Certificate.
- You will get a chance to work on case studies on
**healthcare, autonomous driving, sign language reading, music generation, and natural language processing**. - Along with that, you will get a chance to hear from many
**top leaders in Deep Learning**, who will share with you their personal stories and give you career advice.

**Who Should Enroll?**

NOTE- This Specialization Program is **not for Beginners.** This program is suitable for those-

- Those who have some
**basic understanding of Python.** - Those who have a
**basic knowledge of Linear Algebra and Machine Learning.**

**Cost of the Specialization Program-**

- This course is
**FREE to audit**but for Certificate**$49/month**.

**Interested to Enroll?**

If yes, then You can **Sign Up** here.

### 3. **Machine Learning with Python**

**Provider-** IBM

**Rating-** 4.7/5

**Time to Complete- **22 hours

This is another Machine Learning course for Beginners. This course starts with the basics of Machine Learning. **Python** is used in this course to implement Machine Learning algorithms.

The best part of this course is the **practical advice given after each machine learning algorithm**. Before starting a new algorithm, the trainer gives you the details of how the algorithm works, its pros, and cons, and which type of problem can be solved by this algorithm.

Now, let’s see the Topics covered in that course-

**Topics Covered-**

- Introduction to Machine Learning
- Regression
- Classification
- Clustering
- Recommender Systems
- Final Project

Now, let’s see the benefits you will get after completing this course-

**Extra Benefits-**

- You will get a
**Shareable Certificate**. Along with that, you will earn an I**BM digital badge**. - You will get
**FREE**career resources after completing the Professional Certificate. - This course includes
**Resume builder and Mock interviews.**

**Who Should Enroll?**

- This course is good for beginners in Machine Learning, who wanna learn Machine Learning with
**Python**.

**Cost of the Course-**

7-Day Free Full Access Trial, but after the trial ends 39$/ month.

**Interested to Enroll?**

If yes, then You can **Sign Up **here.

### 4. **Advanced Machine Learning Specialization**

**Provider-** National Research University Higher School of Economics

**Rating-** 4.5/5

**Time to Complete- **10 months (If you spend 6 hours per week)

This Specialization series is an advanced series of courses. If you want to learn** more than the basics of Machine Learning**, then this is the best choice for you.

This specialization program fills out all the gaps in your knowledge of Machine Learning. As this is an advanced series of courses, that’s why you need to have **more math knowledge.**

In short, this specialization program is for those **who are already in the industry**. This course will sharpen their skills.

Throughout this Specialization program,** you will create several projects, that will help you to build a more powerful portfolio.**

This Specialization Program contains 7 Courses. Let’s see all these courses-

**Courses Include-**

**Introduction to Deep Learning****How to Win a Data Science Competition: Learn from Top Kagglers****Bayesian Methods for Machine Learning****Practical Reinforcement Learning****Deep Learning in Computer Vision****Natural Language Processing****Addressing Large Hadron Collider Challenges by Machine Learning**

**Extra Benefits-**

- You will get a Shareable Certificate.
- You will get a chance to work on a wide variety of real-world problems like
**image captioning and automatic game playing.** - Along with that, you will get a chance to take advice from
**Top Kaggle machine learning practitioners and CERN scientists.**

**Who Should Enroll?**

- Those who have
**Intermediate level knowledge**in Machine Learning. - Or the one who is already in the industry and wants to sharpen Machine Learning skills.

**Cost of the Course-**

7 Day Free Full Access Trial, but after the trial ends 48$/ month.

**Interested to Enroll?**

If yes, then You can **Sign Up **here.

### 5. **Mathematics for Machine Learning Specialization **

**Provider-** Imperial College London

**Rating-** 4.6/5

**Time to Complete- **4 Months (4 hours/week)

To learn Machine Learning, Mathematics knowledge is crucial. This specialization program will fill out the gap in **Mathematics knowledge**.

This Specialization program will help you to understand the concepts of Mathematics required in Machine Learning.

This program starts with the basics of **Linear Algebra and Multivariate Calculus**. At the end of this specialization program, you will have a clear understanding of mathematical knowledge to continue your journey in Machine Learning.

This specialization program is consist of 3 courses. Let’s see these courses-

**Courses Include-**

**Mathematics for Machine Learning: Linear Algebra****Mathematics for Machine Learning: Multivariate Calculus****Mathematics for Machine Learning: PCA**

**Extra Benefits-**

- You will get a
**Shareable Certificate.** - You will have a good level of Mathematics Knowledge for Machine Learning.

**Who Should Enroll?**

- Those who are beginners and want to learn Mathematics for Machine Learning.

**Cost of the Specialization Program-**

- 7 Day Free Full Access Trial and after $49/ month.

**Interested to Enroll?**

If yes, then You can **Sign Up** here.

Now let’s see Best Online Courses On Machine Learning at Udacity.

**Best Machine Learning Courses at Udacity**

**Best Machine Learning Courses at Udacity**

**6. ** **Intro to Machine Learning with TensorFlow**

**Intro to Machine Learning with TensorFlow**

**Time to Complete- **3 months (if you spend 10 hrs/week)

**Rating- **4.7/5

This is Nano-Degree Program. In that program, you will learn foundational machine learning algorithms, starting with data cleaning and supervised models. Then this program will cover **deep and unsupervised learning.**

The best part of this program is that at each step, you will get **practical experience** by applying your skills to code exercises and projects.

Now, let’s see the topics covered in this Nano-Degree Program-

**Topics Covered-**

- Supervised Learning
- Deep Learning
- Unsupervised Learning

**Extra Benefits-**

- You will get a chance to work on
**Real-world projects.** - You will get
**Technical mentor support.** - Along with that, you will get
**Resume services, Github review, LinkedIn profile review.**

**Who Should Enroll?**

- Those who have Intermediate Python programming knowledge.
- Who is familiar with data structures like dictionaries and lists.
- Those who have basic knowledge of probability and statistics.
- This is especially
**for those who have experience in Python but have not yet studied Machine Learning topics.**

**Interested to Enroll?**

If yes, then check it out here- **Intro to Machine Learning with TensorFlow**

You can also check this Course- **Intro to Machine Learning with PyTorch**

**7**.** ****Become a Machine Learning Engineer**

**Become a Machine Learning Engineer**

**Rating- **4.6/5

**Time to Complete-** 3 months (If you spend 10 hours per week)

This is another Nano-Degree program but not for beginners. This program is designed for those who have a basic understanding of Machine Learning concepts.

In that program, you will gain practical experience using **Amazon SageMaker **to deploy trained models to a web application and evaluate the performance of your models.

Let’s see the topics covered in that Nano-Degree program-

**Topics Covered-**

- Software Engineering Fundamentals
- Machine Learning in Production
- Machine Learning Case Studies
- Machine Learning Capstone

**Extra Benefits-**

- You will get a chance to work on
**Real-world projects.** - You will get
**Technical mentor support.** - Along with that, you will get
**Resume services, Github review, LinkedIn profile review.**

**Who Should Enroll?**

- Who has
**Intermediate Python programming knowledge and Intermediate knowledge of machine learning algorithms**.

**Interested to Enroll?**

If yes, then check it out **here**– **Become a Machine Learning Engineer**

### 8. **Deep Learning **

**Time to Complete- **4 months (If you spend 12 hours per week)

**Rating- **4.7/5

This Nano-Degree program from Udacity will give you a complete understanding of Deep Learning.

In this program, you will build **convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation**.

Now, let’s see the topics covered in that program-

**Topics Covered-**

- Introduction to Deep Learning.
- Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Generative Adversarial Networks
- Deploying a Sentiment Analysis Model

**Extra Benefits-**

- You will get a chance to work on
**Real-world projects.** - You will get
**Technical mentor support.** - Along with that, you will get
**Resume services, Github review, LinkedIn profile review.**

**Who Should Enroll?**

- Who has
**intermediate-level Python programming knowledge**, and experience with**NumPy and pandas**. - Who has
**math knowledge, including- algebra and some calculus**. - It’s a beginner-friendly program only Python knowledge is mandatory.

**Interested to Enroll?**

If yes, then check it out **here**– **Deep Learning (Udacity)**

Now let’s see Best Online Courses On Machine Learning at Codecademy.

**Best Machine Learning Courses **at Codecademy

**Best Machine Learning Courses**at Codecademy

**9**. **Get started with Machine Learning **

**Time to Complete- **7 weeks

**Type- **Skill Path

This is another Beginner-friendly skill path for Machine Learning from Codecademy. The best part of this course is its **Step by Step** guide.

This course starts with the basics of machine learning. After completing the basics of machine learning, you will work on 3 different projects- **Handwriting Recognition, Sports Vector Machine, and Breast Cancer Classifier.**

Now, let’s see the topics covered in that course-

**Topics Covered-**

- Introduction to Machine Learning
- Supervised Learning: Regression
- Regression Cumulative Project
- Supervised Learning: Introduction to Classification
- Supervised Learning: Advanced Classification
- Supervised Machine Learning Cumulative Project
- Unsupervised Learning
- Unsupervised Machine Learning Cumulative Project
- Perceptrons and Neural Nets
- Machine Learning Capstone Project

**Extra Benefits-**

- After completing this course you are able to clean and manipulate the data, you know which model to choose for different problems.
- Along with that, you will get a step by step guidance.

**Who Should Enroll?**

- Who wants to level up their Python Learning.
- Who is a beginner and want to learn Machine Learning

**Interested to Enroll?**

If yes, then You can **Sign Up** here.

### 10. **Learn the Basics of Machine Learning**

**Provider-** Codecademy

**Time to Complete- **20 hours

This is also a good course on machine Learning for Beginners. This course covers the fundamentals of machine learning.

More than **75,000 learners** have already taken this course. Now, let’s see the topics covered in that course-

**Topics Covered-**

- Introduction to Machine Learning
- Linear Regression
- Multiple Linear Regression
- Yelp Regression Project
- Classification Vs Regression
- Classification: K-Nearest Neighbors
- Logistic Regression
- Decision Trees
- Clustering: K-Means
- Perceptron
- Artificial Intelligence Decision Making: Minimax

**What project Will You Create?**

- Honey Production
- Breast Cancer Classifier
- Predict Titanic Survival

**Who Should Enroll?**

- Who has basic knowledge of Python.

**Interested to Enroll?**

If yes, then You can **Sign Up** Here.

**FYI-** If you are Interested to Build **Chatbot with Python**, you can check this Course-**Build Chatbots with Python**. You can also check this one- **Learn to Program Alexa**

Now let’s see Best Online Courses On Machine Learning at Datacamp.

**Best Machine Learning Courses at Datacamp**

**11. Machine Learning Scientist with Python**

**Time to Complete- **93 hours

**Type- **Career Track

This is a career track offered by **Datacamp**. There are 23 courses in this career track and begin with supervised learning with scikit learn. In this course, you will learn **supervised, unsupervised, and deep learning.**

Along with this, you will learn **natural language processing, image processing, **and libraries such as** Spark and Keras.**

In this career track, you will also learn how to approach and **win Kaggle competitions.**

**Who Should Enroll?**

- Who is a beginner in Machine learning and looking for step by step career guidance.

**Interested to Enroll?**

If yes, then check out the course details here- **Machine Learning Scientist with Python**

**12. ****Machine Learning for Everyone**

**Machine Learning for Everyone**

**Time to Complete- **4 hours

**Type- **Course

This Datacamp course is best for you if you are an absolute beginner in machine learning. In this course, you will learn all the basics of machine learning such as What is machine learning, machine learning models, and how does machine learning work.

There are 3 chapters in this course-

**What is Machine Learning?****Machine Learning Models****Deep Learning**

**Who Should Enroll?**

- Who is an absolute beginner in machine learning.

**Interested to Enroll?**

If yes, then check out the course details here- **Machine Learning for Everyone**

Now let’s see Best Online Courses On Machine Learning at Udemy.

**Best Machine Learning Courses **at Udemy

**Best Machine Learning Courses**at Udemy

### 13. **Machine Learning A-Z™: Hands-On Python & R In Data Science**

**Rating-** 4.5/5

**Provider-** SuperDataScience Team

**Time to Complete- **44 hours

This is the **Bestseller Course at Udemy**. I personally love this course. This course not only teaches you the theory related to Machine Learning but also provide the implementation of each Machine Learning Algorithms.

The best part of this course is that you will find implementation in Both Languages **Python and R**. If you are a complete beginner in Machine Learning, then this course is best for you.

This course **doesn’t cover advanced topics **but covers all basic topics of Machine Learning. You will also learn the basics of Deep Learning and Natural Language Processing.

Now, let’ see the topics covered in this course-

**Topics Covered-**

- Part 1 – Data Preprocessing
- Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 – Clustering: K-Means, Hierarchical Clustering
- Part 5 – Association Rule Learning: Apriori, Eclat
- Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
- Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

**Extra Benefits-**

- You will get a Certificate of Completion.
- You will also get 74 articles and 38 downloadable resources.
- Along with that, you will get lifetime access to the course material.

**Who Should Enroll?**

- This course is for anyone who wants to learn Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.

**Interested to Enroll?**

If yes, then **Sign Up** here.

### 14. **Python for Data Science and Machine Learning Bootcamp**

**Rating-** 4.6/5

**Provider-**Jose Portilla

**Time to Complete- **25 hours

This is also one of the most popular courses available at Udemy. This course will teach you how to implement Machine Learning Algorithms. This course will also teach you how to use** Pandas for Data Analysis**, and **Seaborn for statistical plots**.

Now, let’s see the topics covered in this course-

**Topics Covered-**

- Programming with Python
- NumPy with Python
- Using pandas Data Frames to solve complex tasks
- Use pandas to handle Excel Files
- Web scraping with python
- Connect Python to SQL
- Use matplotlib and seaborn for data visualizations
- Use plotly for interactive visualizations
- Machine Learning with SciKit Learn, including:
- Linear Regression
- K Nearest Neighbors
- K Means Clustering
- Decision Trees
- Random Forests
- Natural Language Processing
- Neural Nets and Deep Learning
- Support Vector Machines
- and much more.

**Extra Benefits-**

- You will get a
**Certificate of Completion.** - You will also get 13 articles and 5 downloadable resources.
- Along with that, you will get lifetime access to the course material.

Now, let’s the how much time this course will take to complete?

**Who Should Enroll?-**

- Who has at least some programming experience.

**Interested to Enroll?**

If yes, then **Sign Up** here.

Now let’s see Best Online Courses On Machine Learning at Pluralsight.

**Best Machine Learning Courses **at Pluralsight

**luralsight**

**Best Machine Learning Courses**at P**15. Understanding Machine Learning with Python**

**Time to Complete- **1 hour 53 minutes

**Level- **Beginner

In this course, you will learn how to perform **Machine Learning with Python.** After completing the course, you will be able to use **Python** and the **scikit-learn library** to create Machine Learning solutions.

Throughout the course, you will utilize Python and its libraries to make machine learning models.

**Who Should Enroll?**

- Who wants to learn the basics of
**machine learning with Python**. And who is familiar with software development in general and**basic statistics.**

**Interested to Enroll?**

If yes, then check out the details here- **Understanding Machine Learning with Python**

**16. Designing Machine Learning Solutions on Microsoft Azure**

**Time to Complete- **1 hour 42 minutes

**Level- **Intermediate

In this course, you will learn how to leverage Azure’s Machine Learning capabilities. In the beginning, you will learn how Microsoft’s Team Data Science Process (TDSP) enables best practices across disciplines.

Then you will learn the workflow of the **Azure Machine Learning Service**. In the next step, you will review how to make a pipeline for your **data preparation, model training, and model registration. **

At the end, you will explore the **infrastructure approaches** that can be leveraged for machine learning.

**Who Should Enroll?**

- Who has intermediate level knowledge in machine learning.

**Interested to Enroll?**

If yes, then check out the details here- **Designing Machine Learning Solutions on Microsoft Azure**

**17. AWS Machine Learning / AI**

**Time to Complete- **17 hours

**Type- **Career Path

This is a **career path with 9 courses **offered by Pluralsight. In this career path, you will learn **Amazon Lex, Amazon Translate, Sagemaker, Amazon Comprehend, Amazon Transcribe, Deep Learning on AWS, AWS Polly, and AWS Rekognition.**

In the beginning, you will learn about your first artificial intelligence service that can be used in your application, **Amazon Lex.** Then you will learn about the **bulk of the options for AI on AWS for developers.**

At the end of this career path, you will learn about **AI tools, Rekognition, machine learning with the powerful Sagemaker and Deep Learning Instances on AWS.**

**Who Should Enroll?**

- Who is familiar with
**cloud computing and application development.**

**Interested to Enroll?**

If yes, then check out the details here- **AWS Machine Learning / AI**

**Best Machine Learning Courses **at edX

**Best Machine Learning Courses**at edX

**18. Advanced Machine Learning**

**Provider- **ITMO University

**Time to Complete- **5 weeks (If you spend 2-4 hours per week)

This is an advanced machine learning course offered by edX. In this course, you will learn advanced concepts of machine learning such as **factor analysis, multiclass logistic regression, resampling and decision trees, support vector machines, and reinforced machine learning.**

This course considered various examples and software applications. Now let’s see the syllabus of the course-

**Course Syllabus-**

**Factor analysis****Multiclass logistic regression****Resampling and decision trees****Support vector machines****Reinforced machine learning**

**Who Should Enroll?**

- Those who have basic knowledge of
**calculus, statistics, and linear algebra.**

**Interested to Enroll?**

If yes, then check out the program details here- **Advanced Machine Learning**

**Best Machine Learning Courses **at Edureka

**Best Machine Learning Courses**at Edureka

**19**. **Machine Learning Engineer Masters Program **

**Provider-** Edureka

**Rating-** 4.5/5

**Time to Complete- **28 Weeks

This is the Master’s Program for Machine Learning from Edureka especially for those who are looking for a Machine Learning Engineer Job.

Edureka provides **Live Classes** based on your convenience. This Master’s Program consists of 9 courses, that will give you a complete understanding of Machine Learning and make you a Job Ready in the Machine Learning field.

Now, let’s see the courses includes in this Master Program-

**Courses Include-**

- Python Programming Certification Course
- Machine Learning Certification Training using Python
- Graphical Models Certification Training
- Reinforcement Learning
- Natural Language Processing with Python Certification
- AI & Deep Learning with TensorFlow
- Python Spark Certification Training using PySpark
- Machine Learning Engineer Master Capstone Project

Along with that, you will get 2 FREE Elective Courses-

- Python Scripting Certification Training
- Python Statistics for Data Science Course

**Extra Benefits-**

- You will get a
**Masters’s Course Certification**from Edureka. - You will get Lifetime access to
**presentations, quizzes, installation guides.** - Along with that, you will get a
**Personal Learning Manager**, who is committed to answering all your queries.

**Who Should Enroll?**

- Anyone can enroll, there is no prerequisite for enrolling in that Master Course.

**Interested to Enroll?**

If yes, then you can **visit** here.

**20**. **AI & Deep Learning with TensorFlow**

**Rating-** 4/5

**Provider- **Edureka

This is one of the courses from the previous Master Program for Machine Learning. You can enroll in a single course instead of enrolling for a full Master’s Program.

In that course, you will learn about **what is AI, explore neural networks, understand deep learning frameworks, implement various machine learning algorithms using Deep Networks**.

This course will provide you rich hands-on training on Deep Learning in TensorFlow with Python.

Now, let’s see the topics covered in that course-

**Topics Covered-**

- Introduction to Deep Learning
- Understanding Neural Networks with TensorFlow
- Deep dive into Neural Networks with TensorFlow
- Master Deep Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Restricted Boltzmann Machine (RBM) and Autoencoders
- Keras API
- TFLearn API
- In-Class Project

**Extra Benefits-**

- You will get a
from Edureka.**Deep Learning Engineer Certificate** - Additionally, you will receive guidance from a Deep Learning expert who is currently working in the industry on real-life projects.
- Along with that, you will get 24 x 7 Expert Support, Lifetime Access to the study material.
- You will work on Real-life Case Studies.
- You will also get 60 days of
**Cloud Lab**access - You will get a chance to create an image classifier using CNN, and create a script generator using LSTM.

**Who Should Enroll?**

**Who Should Enroll?**

- Those who have basic programming knowledge in Python and basics of concepts about Machine Learning.

**Interested to Enroll?**

If yes, then **visit here.**

So, these are the Best Online Courses On Machine Learning selected by me for you.

**Summary of Best Online Courses on Machine Learning**

Course Name | Rating | Time to Complete | Best for |

1. Machine Learning-Stanford University | 4.5/5 | 60 hours | Beginners |

2. Deep Learning Specialization– deeplearning.ai | 4.8/5 | 4 months ( If you spend 5 hours per week) | Intermediate |

3. Machine Learning with Python– IBM | 4.7/5 | 22 hours | Beginners |

4. Advanced Machine Learning Specialization-National Research University Higher School of Economics | 4.5/5 | 10 months (If you spend 6 hours per week) | Advanced |

5. Mathematics for Machine Learning Specialization– Imperial College London | 4.5/5 | 4 months (4 hours/week) | Beginners |

6. -UdacityIntro to Machine Learning with TensorFlow | 4.7/5 | 3 months (if you spend 10 hrs/week) | Intermediate |

7. Become a Machine Learning Engineer– Udacity | 4.6/5 | 3 months (If you spend 10 hours per week) | Intermediate |

8. – UdacityDeep Learning | 4.7/5 | 4 months (If you spend 12 hours per week) | Advanced |

9. Get started with Machine Learning– Codecademy | NA | 7 weeks | Beginners |

10. Learn the Basics of Machine Learning– Codecademy | NA | 20 hours | Beginners |

11. Machine Learning Scientist with Python– Datacamp | NA | 93 hours | Beginners |

12. Machine Learning for Everyone– Datacamp | NA | 4 hours | Beginners |

13. Machine Learning A-Z™: Hands-On Python & R In Data Science-Udemy | 4.5/5 | 44 hours | Beginners |

14. Python for Data Science and Machine Learning Bootcamp-Udemy | 4.6/5 | 25 hours | Intermediate |

15. Understanding Machine Learning with Python– Pluralsight | NA | 1 hour 53 minutes | Beginners |

16. Designing Machine Learning Solutions on Microsoft Azure– Pluralsight | NA | 1 hour 42 minutes | Intermediate |

17. AWS Machine Learning / AI– Pluralsight | NA | 17 hours | Beginners |

18. Advanced Machine Learning– edX | NA | 5 weeks (If you spend 2-4 hours per week) | Advanced |

19. Machine Learning Engineer Masters Program-Edureka | 4.5/5 | 28 Weeks | Beginners |

20. AI & Deep Learning with TensorFlow– Edureka | 4.5/5 | NA | Intermediate |

**Personal Note-**

I would like to share my personal suggestions with you. In order to learn Machine Learning, these are some prerequisites-

**Basic Calculus-**Machine Learning is based on optimization, which requires knowledge of Calculus. So, it is important that you have basic knowledge of limits, functions, maxima, minima, etc.**Linear Algebra-**The next prerequisite for ML is Linear algebra because, in ML, you have to deal with vectors and matrices. That’s why you should be aware of the concepts of linear algebra. Eigenvalues and Eigenvectors are also the main topics for ML.**Probability/ Statistics-**You should have basic knowledge of probability too.

Advanced Courses will require this knowledge before starting the course. But Beginner-friendly courses will cover most of these topics.

This **Mathematics for Machine Learning Specialization (Coursera)** will give you a complete understanding of all math required for Machine Learning.

This ** Machine Learning (Coursera)** by Andrew Ng will also cover most of the math you’ll need for Machine Learning.

- Along with this Mathematics knowledge, you should have
**Programming knowledge**. Most of the courses discussed here use**Python**.

This **Machine Learning with Python****(Coursera)** course is good for Beginners. But if you don’t have any knowledge of Python, then you can check out this course- **Programming for Everybody (Getting Started with Python)**

So, these are the prerequisites to learning Machine Learning.

After learning all these skills, many people have a question, What are the most important Machine Learning algorithms?

Right.

So, this is the list of some must-have algorithms for Machine Learning-

**Linear Regression****Logistic Regression****k-Means Clustering****k-Nearest Neighbors****Support Vector Machines (SVM)****Decision Trees****Random Forests****Naive Bayes**

These are must-haves but there is much more. Almost all courses discussed here covered all these algorithms.

Once you start learning these algorithms, start practicing some problems with **Kaggle**. Kaggle is a very popular machine learning contest platform where you can practice with real-world data so that you get an idea of how ML is used in the real world.

The reason why I select these courses is for you is their Real-World Projects. All these courses will also teach you how to work on real-world projects.

Because the more you practice, the more you will learn the concepts of Machine Learning.

You can use **Data Camp** to choose projects according to your interest.

You can check these **Best Machine Learning Projects for Beginners**–

**1. Recommendation System**

As a beginner in machine learning, you can start your first project as a **Recommendation** system. Where you have to build a system that will recommend the products based on user history. Something like Amazon or Netflix.

**Portal Link- Recommender Systems Datasets**

And you can also check this complete project on **Movie Recommendation System in R.**

**2. Fake News Detection**

There is a lot of fake news spreading all over the world. So how can we differentiate between true news and false news?… The answer is with the help of **Machine Learning**. In this project, you have to **build a model** by using the Python programming language, which can identify whether the news is true or fake.

You can check the tutorial for this project in **Datacamp** and in **DataFlair.**

**3. Improve Health Care**

The Healthcare industry is widely using machine learning. So you can work on a project that is related to health care such as **disease prediction, Diagnostic care, etc.**

You can check this tutorial for **building a machine learning model for disease prediction.**

For health care datasets, you can check the following portal-

**4. Stock Price Predictor**

This is another Best machine learning project for beginners. Various companies and businesses are looking for software that can **monitor and analyze the company’s performance and predict future prices of various stocks**.

You can check this tutorial for **Stock Price Prediction in Python**. In this tutorial, you will learn how to predict stock prices using the **LSTM neural network**. And how to build a dashboard using **Plotly dash for stock analysis.**

**5. Handwritten Digit Recognition using Python**

To explore and test your deep learning skills, I think this is the best project to consider. In this project, you will build a r**ecognition system that recognizes human handwritten digits.**

You can check this tutorial for **Handwritten Digit Recognition using Python**.

So, that’s all about the Learning path for Machine Learning. Now it’s time to wrap up.

**Conclusion-**

These Best Online Courses on Machine Learning will help you to **start your Machine Learning journey. **These Best Online Courses On Machine Learning will definitely help you whether you are totally a beginner or you have intermediate-level knowledge.

My aim is to provide you best resources for Learning. I hope you found this article helpful. If you have any doubt or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

**FAQ-**

**1. Where can I learn Machine Learning Online?**

There are various Online Platforms are available from where you can learn. Some most popular platforms are Coursera, Udacity, Codecademy, Edureka, and Udemy. In these platforms, you can find the best courses on Machine Learning.

**2. How long it will take to learn Machine Learning?**

It depends upon How much hours you spend daily on Machine Learning. If you spend 5 to 6 hours daily, then approximately, in 6 months you can learn Machine Learning.

**3. Is Machine Learning a good career?**

In short, Yes. If you are good in Mathematics and in Programming, then definitely Machine Learning is a good choice for you. The scope of Machine Learning in present as well as in the future is very broad.

**4. What is the salary of Machine Learning Engineer?**

According to Glassdoor, the average salary of a Machine Learning Engineer in **India** is- INR 750k per year.

In the USA, it is- $ 114k per year.

**5. How do I get a machine learning job with no experience?**

If you are fresher and want to come to the Machine Learning field, then you should focus on Projects. Theoretical Knowledge is not enough, you should do some real-world projects. Real-world projects will show that you have also Hands-on experience similar to the Experience person. The courses listed in that article will cover Real-World projects.

**6. Does Machine Learning require coding?**

Knowledge of Programming language is necessary for Machine Learning. If you choose Python then you don’t need to write lots of code. It only requires a few lines of code.

**7. What should I learn before Machine Learning?**

Before learning ML, you should have knowledge of the following topics-***** Linear Algebra.***** Calculus.*** **Statistics and Probability.***** Programming Knowledge.

#### Learn the Basics of Machine Learning Here

Read K-Means Clustering here-K Means Clustering Algorithm: Complete Guide in Simple Words

Are you ML Beginner and confused, about where to start ML, then read my BLOG – How do I learn Machine Learning?

If you are looking for Machine Learning Algorithms, then read my Blog – Top 5 Machine Learning Algorithms.

If you are wondering about Machine Learning, read this Blog- What is Machine Learning?

Thank YOU!

Though of the Day…

– Henry Ford

‘Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

### Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.

Pingback: Best Online Courses On Machine Learning You Must Know in 2020 – Autonomation.ir