Machine Learning Certification

Experfy in Harvard Innovation Labs, in collaboration with subject matter experts, prepares you for a career in Machine Learning

$1,619.94 Enroll Now
Specialization fact course
12 Self-paced Courses

Forged by experts to help you succeed in your career. 9 courses are live and 3 will be added soon.

Specialization fact certificate
Certification

Industry recognized certification enables you to add this credential to your resume upon completion of all courses.

Machine Learning Certification

Experfy's Machine Learning Certification program was molded by industry experts to arm you with the skills that you need to succeed in this ever-evolving job market. You will learn popular machine learning algorithms such as Supervised Learning: Decision Trees, Naïve Bayes Classification, Ordinary Least Squares Regression, Logistic Regression, Support Vector Machines; Unsupervised Learning: Clustering Algorithms, Principal Component Analysis, Singular Value Decomposition along with Python code examples tackling real-life use cases from different industries.

Introduction to Python

  • Veysel Kocaman, Instructor - Introduction to Python Veysel Kocaman

Self-paced

Course 1
About the Course
  • Learn why Python dominates the Data Science community around the world 
  • Learn the most powerful programming language in Machine Learning.
  • Instructor has a B.S degree in Computer Engineering and a M.S degree in Operations Research from Penn State University. He's the Head of AI and CTO at Talent Envoy.

Machine Learning for Predictive Analytics

  • Dr. Larry Bookman, Instructor - Machine Learning for Predictive Analytics Dr. Larry Bookman

Self-paced

Course 2
About the Course
  • Learn what is possible with the state of machine learning in today’s world, its limits, risks and rewards, and how to apply this knowledge to benefit your organization.
  • The course contains over 6+ hours of video instruction and quizzes and demos to test and further your understanding of the material.
  • Instructor has 25 years of experience in applying data science and machine learning methods to solve a variety of business problems in multiple industries.

Data Pre-Processing

  • Dr. Rich Huebner, Instructor - Data Pre-Processing Dr. Rich Huebner

Self-paced

Course 3
About the Course
    • Understand what data preprocessing is and why it is needed as part of an overall data science and machine learning methodology.
    • Be able to summarize your data by using some statistics and data visualization.
    • Instructor has 25 years experience with data design, data architecture, and analytics. He holds two graduate degrees in Information Systems & Management with a Ph.D. in IT.

Supervised Learning: Linear Regression

  • Dr. Stephen Huff, Instructor - Supervised Learning: Linear Regression Dr. Stephen Huff

Self-paced

Course 4
About the Course
    • Acquire improved ability to discriminate, differentiate, and conceptualize appropriate methods of supervised machine learning methods.
    • Instructor has a Ph.D. in Bioinformatics and works as a consultant for the DHS Agency.

Supervised Learning: Classification

  • Dr. Rukmini Vijaykumar, Instructor - Supervised Learning: Classification Dr. Rukmini Vijaykumar

Self-paced

Course 5
About the Course
  • Learn several classification models that are widely in use.
  • Gain the knowledge and skills to effectively apply existing classification algorithms and tools to solve real-world problems.
  • Instructor has a Ph.D in Computer Science with specialization in AI from University of Massachusetts, Amherst with over 30 years of professional experience.

Unsupervised Learning: Clustering

  • Peter Chen, Instructor - Unsupervised Learning: Clustering Peter Chen

Course 6
About the Course
  • Understand the power of Gaussian Mixture Models (GMM) to go beyond simple clustering needs
  • Evaluate the quality of clustering using Silhouette plots
  • Instructor has a B.S in Management Science from the Massachusetts Institute of Technology/Sloan School of Management, Masters in General Management from Harvard.

Unsupervised Learning: Dimensionality Reduction and Representation

  • Dr. Yogesh Kulkarni, Instructor - Unsupervised Learning: Dimensionality Reduction and Representation Dr. Yogesh Kulkarni

Course 7
About the Course
  • Understand dimension reduction techniques, problems associated with it, and its practical applications.  
  • Instructor holds a PhD in Geometric Modeling and works in areas such as NLP and Deep Learning.
  • Continue towards your Machine Learning certification with Experfy

Graphical Models for Machine Learning

  • Dr. Stephen Huff, Instructor - Graphical Models for Machine Learning Dr. Stephen Huff

Self-paced

Course 8
About the Course

COMING SOON

Tuning & Fusing Models

  • Experfy Team

Self-paced

Course 9
About the Course

COMING SOON

Model Assessment

  • David Sanchez, Instructor - Model Assessment David Sanchez

Self-paced

Course 10
About the Course
  • Learn how to choose the right supervised or unsupervised model for your problem.
  • Instructor has 14 years of experience in industry software development and statistics. He has worked in many industries including health care and health insurance (Sierra Health/Health Plan of Nevada), higher learning (California State University, Fullerton), real estate and finance (First American Title Software), and communications (Flash of Genius/UpdatePromise).

Feature Engineering in Machine Learning

  • Kasra Manshaei, Instructor - Feature Engineering in Machine Learning Kasra Manshaei

Course 11
About the Course
  • Get a comprehensive overview on Feature Engineering strategies.
  • Hands-on style of learning for theoretical concepts.
  • Instructor has 10 years experience working with and teaching Machine Learning, Pattern Recognition, and Data Mining.

Hands-on Project - Data Preparation, Modeling & Visualization

  • Dr. Yogesh Kulkarni, Instructor - Hands-on Project - Data Preparation, Modeling & Visualization Dr. Yogesh Kulkarni

Self-paced

Course 12
About the Course
  • Implement real-life machine learning workflows.
  • Hands-on projects, including data preparation, modeling & visualization tasks.
  • Instructor holds a Ph.D. in Geometric Modeling and works in areas such as NLP and Deep Learning.

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