Advanced Machine Learning and Signal Processing faq

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learnersLearners: 26,600
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This course provides an introduction to advanced machine learning and signal processing techniques. Students will gain an understanding of the fundamentals and be able to apply them to real-world problems. Enrollment requires agreement to the End User License Agreement as outlined in the FAQ.

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Course Feature

costCost:

Free

providerProvider:

Coursera

certificateCertificate:

No Information

languageLanguage:

English

start dateStart Date:

Self Paced

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 06th, 2023]

Advanced Machine Learning and Signal Processing, is part of the IBM Advanced Data Science Specialization which IBM is currently creating and gives you easy access to the invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines.
We'll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce 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.
We learn how to tune the models in parallel by evaluating hundreds of different parameter-combinations in parallel. We'll continuously use a real-life example from IoT (Internet of Things), for exemplifying the different algorithms..
(Please note that we obtained the following content based on information that users may want to know, such as skills, applicable scenarios, future development, etc., combined with AI tools, and have been manually reviewed)
Learners can learn a variety of topics from Advanced Machine Learning and Signal Processing. Firstly, they can gain an understanding of the fundamentals of Linear Algebra, which is essential for comprehending how machine learning models work. Secondly, they can explore the popular Machine Learning Frameworks for Python Scikit-Learn and SparkML. Thirdly, they can learn how to tune models in parallel by evaluating hundreds of different parameter-combinations. Finally, they can gain practical experience by applying the algorithms to a real-life example from IoT (Internet of Things). With this course, learners can unlock the exciting world of learning and gain invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines.

[Applications]
After completing this course, students can apply their knowledge to develop and optimize supervised and unsupervised machine learning models. They can also use the frameworks learned in the course, such as Scikit-Learn and SparkML, to create and tune models in parallel. Additionally, students can use the real-life example from IoT to apply the algorithms learned in the course.

[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models and algorithms. They must have a strong understanding of mathematics, statistics, and computer science, as well as experience with programming languages such as Python, R, and Java. As the demand for machine learning increases, so does the need for Machine Learning Engineers.

2. Data Scientist: Data Scientists are responsible for analyzing large amounts of data and extracting meaningful insights from it. They must have a strong understanding of mathematics, statistics, and computer science, as well as experience with programming languages such as Python, R, and Java. Data Scientists are in high demand as organizations look to leverage data to make better decisions.

3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for developing and deploying AI-based solutions. They must have a strong understanding of mathematics, statistics, and computer science, as well as experience with programming languages such as Python, R, and Java. As AI technology continues to evolve, the demand for AI Engineers is expected to increase.

4. Deep Learning Engineer: Deep Learning Engineers are responsible for developing and deploying deep learning models and algorithms. They must have a strong understanding of mathematics, statistics, and computer science, as well as experience with programming languages such as Python, R, and Java. As the demand for deep learning increases, so does the need for Deep Learning Engineers.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree program provides students with a comprehensive understanding of computer science, including programming, software engineering, and data structures. Students will learn how to design, develop, and maintain computer systems and applications. This degree is ideal for those interested in pursuing a career in software engineering, data science, or artificial intelligence.

2. Master of Science in Artificial Intelligence: This degree program provides students with a comprehensive understanding of artificial intelligence, including machine learning, natural language processing, and robotics. Students will learn how to design, develop, and implement AI systems and applications. This degree is ideal for those interested in pursuing a career in AI research, development, or engineering.

3. Doctor of Philosophy in Machine Learning: This degree program provides students with a comprehensive understanding of machine learning, including supervised and unsupervised learning, deep learning, and reinforcement learning. Students will learn how to design, develop, and implement machine learning algorithms and applications. This degree is ideal for those interested in pursuing a career in machine learning research, development, or engineering.

4. Master of Science in Data Science: This degree program provides students with a comprehensive understanding of data science, including data mining, data visualization, and predictive analytics. Students will learn how to design, develop, and implement data science solutions. This degree is ideal for those interested in pursuing a career in data science, analytics, or business intelligence.

The development trends for these degree paths are focused on the use of AI and machine learning to solve complex problems. As the demand for AI and machine learning experts continues to grow, these degree paths will become increasingly important for those looking to pursue a career in this field. Additionally, the use of big data and analytics will continue to be a major focus of these degree paths, as data science and analytics become more important in the modern world.

Pros & Cons

Pros Cons
  • pros

    Clear and easy to follow lectures

  • pros

    Great machine learning material

  • pros

    Handon experience

  • pros

    Well structured course

  • pros

    Good course coverage

  • pros

    Introduction to IBM Watson

  • pros

    Deep understanding of ML and signal processing

  • cons

    Lack of rigour and depth

  • cons

    Simple assignments

  • cons

    Mediocre lecture videos

  • cons

    Poor instructor English

  • cons

    Not challenging enough coding exercises

Course Provider

Provider Coursera's Stats at AZClass

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faq FAQ for Machine Learning Courses

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