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Best Advanced Machine Learning Courses

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Best Advanced Machine Learning Courses
Best Advanced Machine Learning Courses

Are you looking for the Best Advanced Machine Learning Courses?… If yes, then this article is for you. In this article, you will find the Best Advanced Machine Learning Courses for beginners & advanced like Beginner courses, and Practice test coursesSo, check these Best Advanced Machine Learning Courses and find the best Advanced Machine Learning Courses according to your need.

Advanced Machine Learning on Google Cloud Specialization – Coursera

This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs.

This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems.

What you will learn from this course:

  • Compare static vs. dynamic training and inference
  • Set up distributed training for fault tolerance, replication, and more
  • Manage model dependencies
  • Export models for portability

Skills you will gain from this Course:

  • Tensorflow
  • Convolutional Neural Network
  • Estimator
  • Advanced Machine Learning

This Advanced Machine Learning on Google Cloud Specialization consists of 5 courses they are:

Extra Benefits you will gain from this course:

  • Shareable Specialization and Course Certificates
  • Self-Paced Learning Option
  • Course Videos & Readings
  • Practice Quizzes

Prerequisites to learn this course:

  • Experience coding in Python
  • Knowledge of basic statistics
  • Knowledge of SQL and cloud computing

course duration: 3 months | Best for: Professionals | Provider: Google

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

Machine Learning Algorithms in the Real World Specialization – Coursera

This specialization is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this specialization will set you up to define, train, and maintain a successful machine learning application.

After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.

Skills you will learn from this Course:

  • Applied Machine Learning
  • Classification Algorithms
  • Machine Learning (ML) Algorithms
  • Project Management
  • Statistical Analysis
  • Computer Programming
  • Linear Algebra

In this Machine Learning Algorithms in the real world specialization consists of 4 courses. They are:

The prerequisites required to do these courses are a background in analytics, math (linear algebra, matrix multiplication), statistics and beginner level python programming.

Course Duration: 4 months (3hrs/week) | Best for: Intermediate Level | Provider: AMII

Info: Visit this course and get amazing offers on this course

Machine Learning Engineer for Production (MLOPS) Specialization – Coursera

The 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.

What you will learn from this course:

  • Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
  • Build data pipelines by gathering, cleaning, and validating datasets. Establish data lifecycle by using data lineage and provenance metadata tools.
  • Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
  • Apply best practices and progressive delivery techniques to maintain and monitor a continuously operating production system.

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

This Machine Learning Engineer for Production (MLOPS) consists of 4 courses. They are:

Course Duration: 4 months | Best for: Professionals | Provider: DeepLearning.ai

Info: Visit this course and get amazing offers in this course

Advanced Machine learning and Signal Processing – Coursera

You will learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then you will move onto the most popular Machine Learning Frameworks for python Scikit-Learn and SparkML. SparkML is making up the greatest portion of this course since scalability is key to address performance bottlenecks.

You will learn how to tune the models in parallel by evaluating hundreds of different parameter-combinations in parallel. You will continuously use a real-life example from IoT (Internet of Things), for exemplifying the different algorithms.

For passing the course you are even required to create your own vibration sensor data using the accelerometer sensors in your smartphone. So you are actually working on a self-created, real dataset throughout the course.

The Topics you will learn from this Advanced Machine Learning and Signal Processing are:

  • Setting the stage
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Digital Signal Processing in Machine Learning

Course Duration: 28hrs | Best for: Professionals / Advanced | Provider: IBM

Info: Visit this course and get amazing offers on this course

Production Machine Learning Systems – Coursera

This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.

What you will learn from this Course:

  • Compare static vs. dynamic training and inference
  • Set up distributed training for fault tolerance, replication, and more
  • Manage model dependencies
  • Export models for portability

The topics that you will learn from this course are:

  • Introduction to Advanced Machine Learning on Google Cloud
  • Architecting Production ML Systems
  • Designing Adaptable ML Systems
  • Designing High-Performance ML Systems
  • Building Hybrid ML Systems

Course Duration: 21 hrs | Best for: Advanced | Provider: Google

Info: Visit this course and get amazing offers on this course

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

Conclusion

I hope these Best Advanced Machine Learning Courses 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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