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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.
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.
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
ML Algorithms- Part 2
Welcome to Week 2 of ML Algorithm course. This week, we'll describe Image analysis algirithms with demonstration on Text analysis algorithms. By the end of this week, we'll be able to describe Reinforcement learning algorithms and Forecasting algorithms.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save ML Algorithms to your list so you can find it easily later:
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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:
Good to know
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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