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Best Machine learning Courses on Coursera

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Are you looking for the Best Machine Learning Courses on Coursera?… If yes, then this article is for you. In this article, you will find the Best Machine Learning Courses on Coursera for beginners & advanced like Beginner courses, and Practice test coursesSo, check these Best Machine Learning Courses on Coursera and find the best Coursera Machine Learning Course according to your need. In the previous article, I have already shared the Best Machine Learning Books for Beginners and Advanced if you have missed the post you can read it now.

Now, without any further ado, let’s get started.

Machine Learning Course – Coursera

Rating: 4.9

Best Machine Learning Courses on Coursera

In this machine Learning 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. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include:

  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
  • Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
  • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

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.

Skills you will gain from this Course:

  • Logistic regression
  • Artificial Neural Networks
  • Machine Learning Algorithms
  • Machine Learning

Course Duration: 61hrs | Instructor: Andrew NG | Provider: Stanford | Best for: Beginners

Info: Visit this Course and get amazing financial aid and offers

IBM Machine Learning Professional Certificate – IBM

This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis.

This program consists of 6 courses providing you with a solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning. You will follow along and code your own projects using some of the most relevant open-source frameworks and libraries.

Skills you will gain from this Course:

  • Data Science
  • Deep Learning
  • Artificial Intelligence
  • Machine Learning
  • Python Programming
  • Feature Engineering
  • Statistical Hypothesis Testing
  • Supervised Learning
  • Regression Analysis
  • Linear Regression
  • Ridge Regression

The 6 Courses in IBM Machine Learning Professional Certification are:

Best for: Intermediate Level | Course Duration: 5 months | Provider: IBM

Info: Visit this course and get amazing financial aid and offers

Machine Learning Specialization

Best Coursera Machine Learning Courses

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data.

you will learn how to implement and apply predictive, classification, clustering, and information retrieval machine learning algorithms to real datasets throughout each course in the specialization. By the end of this course, you will walk away with applied machine learning and Python programming experience.

Skills you will gain from this Course:

  • Data Clustering Algorithms
  • Machine Learning
  • Classification Algorithms
  • Python Programming
  • Deep Learning
  • Linear Regression
  • Ridge Regression
  • Statistics
  • Regression Analysis
  • Logistic Regression

There are a total of 4 Courses in this Specialization

Best for: Intermediate Level | Course Duration: 5 months | Provider: University of Washington

Info: Visit this course and get financial aid for this course

Mathematics for Machine Learning Specialization

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics – stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.

The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.

At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

Skills you will gain from this course:

  • Eigenvalues And Eigenvectors
  • Principal Component Analysis (PCA)
  • Multivariable Calculus
  • Linear Algebra
  • Transformation Matrix
  • Linear Regression
  • Vector Calculus
  • Dimensionality Reduction
  • Python Programming

In this Mathematics for Machine Learning Specialization, you will learn 3 Courses. They are:

Best For: Beginners | Course Duration: 4 months | Provider: Imperial College London

Info: Visit this Course and Get financial aid for this course

Machine Learning Engineering for Production (MLOPS) Specialization

This Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.

In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.

Skills you will gain from this course:

  • Managing Machine Learning Production Systems
  • Deployment Pipelines
  • Model Pipelines
  • Data Pipelines
  • Machine Learning Engineering for Production
  • Human-level Performance (HLP)
  • Concept Drift
  • Model baseline
  • Project Scoping and Design
  • ML Deployment Challenges
  • ML Metadata
  • Convolutional Neural Network

In this Machine Learning Engineering for Production MLOPS there are 4 courses. They are:

Best For: Advanced | Course Duration: 3 months | Provider: DeepLearning.ai

info: Visit this Course and Get financial Aid for this course

Machine Learning with Python Course

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.

In this course, you will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms.

In this course, you will practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get.

  • New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy
  • New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more.
  • And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media.

Skills you will gain from this Course:

  • Python Libraries
  • Machine Learning
  • Regression
  • Hierarchical Clustering
  • K-Means Clustering

Course Duration: 23hrs | Best for: Beginners | Provider: IBM

Info: Visit this Course and get the best financial aid for this course

And here the list ends. So, these are the Best Machine Learning Courses on Coursera. I will keep adding more Best Coursera Machine Learning courses to this list.

Conclusion

I hope these Best Machine Learning Courses on Coursera will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.

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