Online Course – Certified Professional Internship in Machine Learning: Real-World Algorithms from the Alberta Artificial Intelligence Institute

Real-world machine learning applications. Master the techniques for implementing machine learning projects.

Suggested by: Coursera (What is Coursera?)

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קורס אונליין - התמחות מקצועית מוסמכת במכונה למידה: אלגוריתמים בעולם האמיתי של מכון אינטליגנציה מלאכותית של אלברטה

Professional Certificate

Intermediate level

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • project management
  • Machine learning algorithms
  • Machine learning
  • Practical Machine Learning
  • Classification algorithms

What you will learn in the course

Courses for which the course is suitable

  • Data Analyst
  • Machine Learning Engineer
  • Algorithm developer
  • Automation expert
  • Data Scientist
  • Technology consultant in the field of finance
  • Medical Systems Analyst
  • Software Engineer in the Engineering Field
  • Machine Learning Project Manager
  • Artificial Intelligence Expert

Expertise – Series of 4 Courses

This specialization is for professionals who have heard about the hype around machine learning and want to apply it to data analytics and automation. Whether it’s in finance, medicine, engineering, business, or other fields, this specialization will make you proficient in setting up, training, and maintaining a successful machine learning implementation.

What will you learn?

  • Clearly define a machine learning problem
  • Identify appropriate data
  • Train a classification algorithm
  • Improve your results
  • Implement it in the real world

The learning process

Once you complete all four courses, you will go through the entire process of building a machine learning project.

Common problems

Additionally, you will be able to anticipate and prevent common problems in applied machine learning.

Details of the courses that make up the specialization

Introduction to Practical Machine Learning

Course 1 • 6 hours • 4.7 (737 ratings)

Course Details
What you’ll learn
  • This course is designed for professionals who have heard of machine learning and want to apply it in data analysis and automation.
  • Whether in finance, medicine, engineering, business, or other fields, this course will introduce you to problem definition and data preparation in a machine learning project.
  • At the end of the course, you will be able to define a machine learning problem using two approaches.
  • You will learn to review available data resources and identify potential applications of machine learning.
  • You will learn to take a business need and turn it into a machine learning application.
  • You will also prepare data for effective machine learning applications.

Machine Learning Algorithms: End-to-End Supervised Learning

Course 2 • 9 hours • 4.7 (411 ratings)

Course Details
What you’ll learn
  • This course takes you from understanding the basics of a machine learning project.
  • Learners will understand and apply supervised learning techniques to a real case study to analyze business scenarios.
  • Learners will also acquire skills to discern the practical implications of various data preparation steps.
  • To be successful, you should have at least a basic level of Python programming knowledge.
  • You should have a basic understanding of linear algebra and statistics.

Data for machine learning

Course 3 • 11 hours • 4.4 (97 ratings)

Course Details
What you’ll learn
  • This course is about data and its importance to the success of your hands-on learning model.
  • Completion of this course will provide learners with the skills to:
    • Understand the critical components of data in the learning, training, and operation phases.
    • Understand biases and data sources.
    • Apply techniques to improve the generality of your model.
    • Explain the consequences of overfitting and identify steps to minimize the problems.
    • Implement appropriate testing and verification measures.
    • Demonstrate how you can improve the accuracy of your model with deep feature engineering.
    • To investigate the effect of algorithm parameters on model robustness.
  • To succeed in this course, you should have at least a basic level of knowledge in Python programming.
  • You should have a basic understanding of linear algebra and statistics.

Skills you will gain

  • Category: Computer Programming
  • Category: Python Programming
  • Category: Machine Learning
  • Category: Statistical Analysis
  • Category: Linear Algebra

Machine learning performance optimization

Course 4 • 11 hours • 4.4 (48 ratings)

Course Details
What you’ll learn
  • This course summarizes everything you learned in the Practical Machine Learning specialization.
  • Now you will go through an entire machine learning project to prepare a machine learning maintenance roadmap.
  • Understand and analyze how to deal with changing data.
  • You can also identify and interpret potential unintended effects in your project.
  • Understand and define rules for operating and maintaining your model.
  • At the end of this course, you will have all the tools and understanding you need to execute a machine learning project.
Additional requirements
  • To be successful, you should have at least a basic level of Python programming knowledge.
  • You should have a basic understanding of linear algebra and statistics.
ending

This is the latest course in the Practical Machine Learning specialization presented by Coursera and the Alberta Machine Intelligence Institute (AMI).

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