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ML Algorithms is the fourth Course in the AWS Certified Machine Learning Specialty specialization. This Course enables learners to deep dive Machine Learning Algorithms. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:00-2:30 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.
Module 1: ML Algorithms- Part 1
Module 2: ML Algorithms- Part 2
Read more
ML Algorithms is the fourth Course in the AWS Certified Machine Learning Specialty specialization. This Course enables learners to deep dive Machine Learning Algorithms. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:00-2:30 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.
Module 1: ML Algorithms- Part 1
Module 2: ML Algorithms- Part 2
Read more
ML Algorithms is the fourth Course in the AWS Certified Machine Learning Specialty specialization. This Course enables learners to deep dive Machine Learning Algorithms. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:00-2:30 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.
Module 1: ML Algorithms- Part 1
Module 2: ML Algorithms- Part 2
Minimum two year of hands-on experience in architecting, building or running ML/deep learning workloads on the AWS Cloud. By the end of this course, learners will be able to :
- Determine algorithm concepts in ML
- Design Regression algorithms and Classification based algorithms
- Examine Reinforcement learning algorithms and Forecasting algorithms
Register for this course and see more details by visiting:
OpenCourser.com/course/2xcg6o/ml
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What's inside
Syllabus
ML Algorithms- Part 1
Welcome to week 1 ofML Algorithms Course. This week, we'll describe algorithm concepts in Machine Learning. We'll demonstrate working of Regression algorithms ad Clustering algorithms. Additonally, we'll also demonstrate working of Classification based algorithms
Read more
Syllabus
ML Algorithms- Part 1
Welcome to week 1 ofML Algorithms Course. This week, we'll describe algorithm concepts in Machine Learning. We'll demonstrate working of Regression algorithms ad Clustering algorithms. Additonally, we'll also demonstrate working of Classification based algorithms
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides a deep dive into Machine Learning Algorithms, suitable for learners with prior experience
Taught by Whizlabs Instructors, recognized for their expertise in the field
Covers a wide range of topics, including Regression, Classification, Reinforcement Learning, and Forecasting Algorithms
Offers a blend of theoretical and practical knowledge through video lectures and graded quizzes
Requires prior hands-on experience, which may not be suitable for absolute beginners
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Reviews summary
High-level ml algorithms for aws prep
According to learners, this course serves as a solid quick refresher and provides a good conceptual understanding of ML algorithms relevant for AWS certification exam preparation. Students appreciate its concise lectures and its ability to tie concepts to the AWS ecosystem. However, a significant number of reviews highlight a lack of in-depth coverage, particularly for those seeking true mastery or deeper mathematical understanding. While hands-on sections are present, some found them superficial or historically outdated/buggy. More recent feedback indicates improvements and updates to the content, making it a valuable resource primarily for experienced learners seeking an efficient overview rather than foundational knowledge.
Lectures are clear, making complex topics digestible.
"The lectures are clear and concise, making complex topics digestible. I particularly appreciated the hands-on sections..."
"The structure is logical, and the quiz at the end of each module helps reinforce learning."
"The instructors explain the concepts well, though sometimes I wished for more real-world use cases."
Recent reviews indicate positive updates to the course.
"The updated content, especially in the practical demos, is much better than what I saw in previous iterations."
"More recent feedback indicates improvements and updates to the content, making it a valuable resource..."
"I noticed the content has been updated recently, addressing some of the earlier criticisms regarding depth and relevance."
Well-suited for AWS ML Specialty exam preparation.
"As someone preparing for the AWS ML Specialty exam, this course was a solid quick refresher. It covers a lot of ground..."
"Excellent course for an overview of ML algorithms specific to the AWS ecosystem. Highly recommend for exam prep."
"Good course for quick revision of ML algorithms. The instructors did a good job of keeping it focused on the AWS context."
"It serves as an excellent accelerator if you're already familiar with ML concepts and need to map them to AWS services."
Often needs external resources for full understanding.
"The practical examples were useful, but I often had to consult external resources for a deeper understanding."
"I had to rely heavily on the official AWS documentation and other resources."
"I found myself wanting more examples and mathematical intuition behind the algorithms."
Hands-on sections are present, but experiences vary.
"The updated content, especially in the practical demos, is much better than what I saw in previous iterations."
"The practical examples were useful, but I often had to consult external resources for a deeper understanding."
"I also encountered some outdated references in the practical exercises, which made following along difficult at times."
"The 'hands-on' sections were poorly explained and often buggy. I spent more time debugging their provided code than actually learning."
Provides breadth but often lacks necessary depth.
"It's definitely a high-level overview. Don't expect deep dives into the math, but for quickly understanding algorithm concepts..."
"The course serves as a decent introduction to various ML algorithms, but I found it lacked the necessary depth for someone with existing AWS ML experience."
"Honestly, I was disappointed. The content felt very basic... There's not enough meat for the AWS ML Specialty exam."
"If you're looking for a deep dive, this isn't it. The modules are bite-sized, which is good... but bad for comprehensive understanding."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in ML Algorithms with these
activities:
Review the basics of ML algorithms
Show steps
Reviewing the basics of ML algorithms will help you build a stronger foundation for this course.
Browse courses on
ML Algorithms
Show steps
-
Review your notes from previous courses or textbooks.
-
Watch online videos or tutorials.
-
Read articles or blog posts about ML algorithms.
Find a mentor who has experience with ML algorithms
Show steps
Finding a mentor who has experience with ML algorithms can help you learn from their experience and guidance.
Browse courses on
Mentoring
Show steps
-
Network with people in your field.
-
Attend industry events.
-
Reach out to potential mentors.
Practice implementing ML algorithms
Show steps
Practicing the implementation of ML algorithms will help you solidify your understanding of the concepts covered in this module.
Browse courses on
ML Algorithms
Show steps
-
Use the AWS AI/ML Playground to implement Regression algorithms.
-
Implement Classification based algorithms.
-
Implement Reinforcement learning algorithms.
-
Implement Forecasting algorithms.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a study guide for this course
Show steps
Creating a study guide will help you organize the materials for this course and make it easier to review for exams.
Browse courses on
Study Skills
Show steps
-
Gather your notes, assignments, quizzes, and exams.
-
Review the materials and identify the key concepts.
-
Create a study guide that outlines the key concepts.
Create a step-by-step guide to using ML algorithms
Show steps
Creating a step-by-step guide will help you solidify your understanding of how to use ML algorithms.
Browse courses on
ML Algorithms
Show steps
-
Choose a specific ML algorithm.
-
Outline the steps involved in using the algorithm.
-
Write detailed instructions for each step.
-
Review and refine your guide.
Lead a peer learning session on ML algorithms
Show steps
Leading a peer learning session will help you practice your presentation skills and solidify your understanding of the concepts covered in this module.
Browse courses on
ML Algorithms
Show steps
-
Prepare a presentation on a topic related to ML algorithms.
-
Lead a peer learning session on the topic.
-
Answer questions from your peers.
Contribute to an open-source project related to ML algorithms
Show steps
Contributing to an open-source project will give you experience working on real-world ML projects and help you to learn from others.
Browse courses on
Open Source
Show steps
-
Find an open-source project related to ML algorithms.
-
Identify a way to contribute to the project.
-
Make your contributions to the project.
-
Review the feedback on your contributions.
-
Update your contributions based on the feedback.
Develop a project that uses ML algorithms
Show steps
Developing a project that uses ML algorithms will give you hands-on experience with the concepts covered in this module.
Browse courses on
ML Algorithms
Show steps
-
Identify a problem that can be solved using ML algorithms.
-
Design a solution using ML algorithms.
-
Implement your solution.
-
Evaluate the performance of your solution.
-
Document your project.
Review the basics of ML algorithms
Show steps
Reviewing the basics of ML algorithms will help you build a stronger foundation for this course.
Browse courses on
ML Algorithms
Show steps
Show steps
Show steps
- Review your notes from previous courses or textbooks.
- Watch online videos or tutorials.
- Read articles or blog posts about ML algorithms.
Find a mentor who has experience with ML algorithms
Show steps
Finding a mentor who has experience with ML algorithms can help you learn from their experience and guidance.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Network with people in your field.
- Attend industry events.
- Reach out to potential mentors.
Practice implementing ML algorithms
Show steps
Practicing the implementation of ML algorithms will help you solidify your understanding of the concepts covered in this module.
Browse courses on
ML Algorithms
Show steps
Show steps
Show steps
- Use the AWS AI/ML Playground to implement Regression algorithms.
- Implement Classification based algorithms.
- Implement Reinforcement learning algorithms.
- Implement Forecasting algorithms.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a study guide for this course
Show steps
Creating a study guide will help you organize the materials for this course and make it easier to review for exams.
Browse courses on
Study Skills
Show steps
Show steps
Show steps
- Gather your notes, assignments, quizzes, and exams.
- Review the materials and identify the key concepts.
- Create a study guide that outlines the key concepts.
Create a step-by-step guide to using ML algorithms
Show steps
Creating a step-by-step guide will help you solidify your understanding of how to use ML algorithms.
Browse courses on
ML Algorithms
Show steps
Show steps
Show steps
- Choose a specific ML algorithm.
- Outline the steps involved in using the algorithm.
- Write detailed instructions for each step.
- Review and refine your guide.
Lead a peer learning session on ML algorithms
Show steps
Leading a peer learning session will help you practice your presentation skills and solidify your understanding of the concepts covered in this module.
Browse courses on
ML Algorithms
Show steps
Show steps
Show steps
- Prepare a presentation on a topic related to ML algorithms.
- Lead a peer learning session on the topic.
- Answer questions from your peers.
Contribute to an open-source project related to ML algorithms
Show steps
Contributing to an open-source project will give you experience working on real-world ML projects and help you to learn from others.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source project related to ML algorithms.
- Identify a way to contribute to the project.
- Make your contributions to the project.
- Review the feedback on your contributions.
- Update your contributions based on the feedback.
Develop a project that uses ML algorithms
Show steps
Developing a project that uses ML algorithms will give you hands-on experience with the concepts covered in this module.
Browse courses on
ML Algorithms
Show steps
Show steps
Show steps
- Identify a problem that can be solved using ML algorithms.
- Design a solution using ML algorithms.
- Implement your solution.
- Evaluate the performance of your solution.
- Document your project.
Career center
Learners who complete ML Algorithms will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models to solve business problems. This course provides a deep dive into machine learning algorithms, which are the foundation of machine learning models. By taking this course, you will gain the skills and knowledge necessary to build and deploy your own machine learning models.
Data Scientist
Data Scientist
A Data Scientist is responsible for collecting, cleaning, and analyzing data to identify patterns and trends. This course provides a deep dive into machine learning algorithms, which are used to extract insights from data. By taking this course, you will gain the skills and knowledge necessary to become a successful Data Scientist.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and testing software applications. This course provides a deep dive into machine learning algorithms, which are increasingly being used in software applications. By taking this course, you will gain the skills and knowledge necessary to develop software applications that leverage machine learning.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is responsible for developing and using mathematical models to analyze financial data. This course provides a deep dive into machine learning algorithms, which are increasingly being used in quantitative finance. By taking this course, you will gain the skills and knowledge necessary to become a successful Quantitative Analyst.
Business Analyst
Business Analyst
A Business Analyst is responsible for analyzing business processes and identifying opportunities for improvement. This course provides a deep dive into machine learning algorithms, which can be used to automate business processes and improve decision-making. By taking this course, you will gain the skills and knowledge necessary to become a successful Business Analyst.
Product Manager
Product Manager
A Product Manager is responsible for managing the development and launch of new products. This course provides a deep dive into machine learning algorithms, which can be used to improve product design and marketing. By taking this course, you will gain the skills and knowledge necessary to become a successful Product Manager.
Marketing Analyst
Marketing Analyst
A Marketing Analyst is responsible for analyzing marketing data and identifying opportunities for improvement. This course provides a deep dive into machine learning algorithms, which can be used to automate marketing processes and improve decision-making. By taking this course, you will gain the skills and knowledge necessary to become a successful Marketing Analyst.
Sales Analyst
Sales Analyst
A Sales Analyst is responsible for analyzing sales data and identifying opportunities for improvement. This course provides a deep dive into machine learning algorithms, which can be used to automate sales processes and improve decision-making. By taking this course, you will gain the skills and knowledge necessary to become a successful Sales Analyst.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is responsible for developing and using mathematical models to solve business problems. This course provides a deep dive into machine learning algorithms, which are increasingly being used in operations research. By taking this course, you will gain the skills and knowledge necessary to become a successful Operations Research Analyst.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data and identifying opportunities for investment. This course provides a deep dive into machine learning algorithms, which are increasingly being used in financial analysis. By taking this course, you will gain the skills and knowledge necessary to become a successful Financial Analyst.
Actuary
Actuary
An Actuary is responsible for assessing and managing risk. This course provides a deep dive into machine learning algorithms, which are increasingly being used in risk management. By taking this course, you will gain the skills and knowledge necessary to become a successful Actuary.
Statistician
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data. This course provides a deep dive into machine learning algorithms, which are increasingly being used in statistics. By taking this course, you will gain the skills and knowledge necessary to become a successful Statistician.
Consultant
Consultant
A Consultant is responsible for providing advice and guidance to businesses. This course provides a deep dive into machine learning algorithms, which are increasingly being used in consulting. By taking this course, you will gain the skills and knowledge necessary to become a successful Consultant.
Teacher
Teacher
A Teacher is responsible for educating students. This course provides a deep dive into machine learning algorithms, which are increasingly being used in education. By taking this course, you will gain the skills and knowledge necessary to become a successful Teacher.
Researcher
Researcher
A Researcher is responsible for conducting research and developing new knowledge. This course provides a deep dive into machine learning algorithms, which are increasingly being used in research. By taking this course, you will gain the skills and knowledge necessary to become a successful Researcher.
For more career information including salaries, visit:
OpenCourser.com/course/2xcg6o/ml
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
ML Algorithms.
Comprehensive guide to machine learning algorithms, providing a deep dive into the theory, implementation, and applications of various algorithms.
Offers a practical introduction to machine learning with Python, focusing on essential concepts and hands-on implementation.
Save
Comprehensive reference on deep learning, covering the foundational concepts, architectures, and applications of deep learning models.
Provides a rigorous mathematical treatment of machine learning, focusing on the probabilistic foundations of various algorithms.
Classic reference on machine learning, providing a comprehensive overview of the field and its theoretical foundations.
Comprehensive introduction to reinforcement learning, covering the theory, algorithms, and applications of this important area of machine learning.
Provides a comprehensive overview of probabilistic graphical models, covering the theory, algorithms, and applications of these models in machine learning.
Provides a rigorous introduction to Bayesian inference and its applications in machine learning.
Provides a comprehensive introduction to convex optimization, which fundamental technique for solving machine learning problems.
Provides a comprehensive introduction to natural language processing (NLP), focusing on practical techniques and hands-on implementation in Python.
Provides a comprehensive guide to using Apache Spark for large-scale data analytics, covering a wide range of topics such as data processing, machine learning, and graph analytics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2xcg6o/ml
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Effort
2 Weeks of Study, 2-2:30 hours/Week
Level
Introductory
Via
Coursera
Institution
Whizlabs
Instructor
Whizlabs Instructor
Language
English
This course belongs to a
collection
called
Exam Prep MLS-C01: AWS Certified Specialty Machine Learning. It's comprised of five courses:
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