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Machine learning (ML) is a critical technology in the growing artificial intelligence (AI) market. ML allows companies to build neural networks that can learn from data and use the resulting models to make predictions and classify new data. Much of the work of creating neural networks can be done by staff with a working knowledge of Python and a reasonable understanding of statistics, probability and algebra. Universities and technology companies are offering technical training in machine learning for a range of experience levels. Here, we look at some of the top certifications in ML.
1. IBM Machine Learning Professional Certificate
Certifying Organization: IBM
Skills Learned: Gain a working knowledge of KNN, PCA and non-negative matrix collaborative filtering. Learn how to compare and contrast different machine learning algorithms by creating recommender systems in Python. Predict course ratings by training a neural network and constructing regression and classification models.
Requirements: Recommended experience in Python programming, statistics linear algebra.
Duration: Six courses each lasting between 14 and 31 hours. Overall, three months at 10 hours a week.
Test: Final presentation
Cost: Currently $49 a month at Coursera
2. Machine Learning Specialization
Certifying Organization: Stanford University
Skills Learned: Build ML models with NumPy and scikit-learn, build and train supervised models for prediction and binary classification tasks. Build and train a neural network with TensorFlow to perform multi-class classification as well as build and use decision trees and tree ensemble methods. Build recommender systems with a collaborative filtering approach and a content-based deep learning method and build a deep reinforcement learning model.
Requirements: Knowledge of basic coding and high school-level math
Duration: Three courses of 34, 33 and 27 hours. Overall, two months at 10 hours a week.
Cost: Currently $49 a month at Coursera
Skills Learned: Gain applied experience in major areas of machine Learning, including prediction, classification, clustering and information retrieval. Learn to analyze large and complex data sets, create systems that adapt and improve over time and build intelligent applications that can make predictions from data.
Requirements: Some related experience
Duration: Four courses of 17 to 22 hours. Overall, two months at 10 hours a week.
Test: An applied learning project. Implement and apply ML algorithms to real data sets.
Cost: Currently $49 a month at Coursera
4. Designing and Implementing a Data Science Solution on Azure
Certifying Organization: Microsoft
Skills Learned: Learn how to operate machine learning solutions at cloud scale using Azure machine learning.
Requirements: Existing knowledge of Python and machine learning frameworks, such as scikit-learn, PyTorch and Tensorflow
Duration: Four days; self-paced or instructor-led
Test: 100-minute exam
Cost: $165 for exam
5. Databricks Machine Learning Associate
6. Databricks Machine Learning Professional
Certifying Organization: Databricks
Skills Learned: Use MLflow to track the machine learning life cycle, package models for deployment and manage model versions. Examine various production issues, different deployment paradigms and post-production concerns.
Requirements: Intermediate experience with Python and pandas, working knowledge of machine learning and data science (scikit-learn, TensorFlow, etc.), familiarity with Apache Spark
Duration: One day; live online in different time zones; also self-paced
Test: Proctored exam
Cost: $1,000 course; $200 exam
Skills Learned: Implement machine learning algorithms using Python. Understand how data scientists approach these problems programmatically.
Duration: 3.5 months at six to nine hours a week
Test: Applied projects
Cost: $3,750
8. Machine Learning With Python: From Linear Models to Deep Learning
Certifying Organization: Massachusetts Institute of Technology (MIT)
Skills Learned: Understand principles behind machine learning problems, such as classification, regression, clustering and reinforcement learning. Implement and analyze models, such as linear models, kernel machines, neural networks and graphical models. Implement and organize machine learning projects, from training, validation and parameter tuning to feature engineering.
Requirements: Proficiency in Python programming. Probability theory course. Both available as other MIT courses.
Duration: 15 weeks at 10-14 hours a week; instructor-paced
Test: Graded assignments and exams
Cost: $300
Skills Learned: Essential mathematical concepts used in machine learning: equations, functions and graphs; differentiation and optimization; vectors and matrices; statistics and probability.
Requirements: Basic knowledge of math and of working with Python
Duration: Six weeks, six to eight hours a week
Test: Graded assignments and exams
Cost: $9910. Python for Data Science and Machine Learning Boot Camp
1. IBM Machine Learning Professional Certificate
Certifying Organization: IBMSkills Learned: Gain a working knowledge of KNN, PCA and non-negative matrix collaborative filtering. Learn how to compare and contrast different machine learning algorithms by creating recommender systems in Python. Predict course ratings by training a neural network and constructing regression and classification models.
Requirements: Recommended experience in Python programming, statistics linear algebra.
Duration: Six courses each lasting between 14 and 31 hours. Overall, three months at 10 hours a week.
Test: Final presentation
Cost: Currently $49 a month at Coursera
2. Machine Learning Specialization
Certifying Organization: Stanford UniversitySkills Learned: Build ML models with NumPy and scikit-learn, build and train supervised models for prediction and binary classification tasks. Build and train a neural network with TensorFlow to perform multi-class classification as well as build and use decision trees and tree ensemble methods. Build recommender systems with a collaborative filtering approach and a content-based deep learning method and build a deep reinforcement learning model.
Requirements: Knowledge of basic coding and high school-level math
Duration: Three courses of 34, 33 and 27 hours. Overall, two months at 10 hours a week.
Cost: Currently $49 a month at Coursera
3. Machine Learning Specialization
Certifying Organization: University of WashingtonSkills Learned: Gain applied experience in major areas of machine Learning, including prediction, classification, clustering and information retrieval. Learn to analyze large and complex data sets, create systems that adapt and improve over time and build intelligent applications that can make predictions from data.
Requirements: Some related experience
Duration: Four courses of 17 to 22 hours. Overall, two months at 10 hours a week.
Test: An applied learning project. Implement and apply ML algorithms to real data sets.
Cost: Currently $49 a month at Coursera
4. Designing and Implementing a Data Science Solution on Azure
Certifying Organization: MicrosoftSkills Learned: Learn how to operate machine learning solutions at cloud scale using Azure machine learning.
Requirements: Existing knowledge of Python and machine learning frameworks, such as scikit-learn, PyTorch and Tensorflow
Duration: Four days; self-paced or instructor-led
Test: 100-minute exam
Cost: $165 for exam
5. Databricks Machine Learning Associate
Certifying Organization: Databricks
Skills Learned: Scale ML pipelines with Spark, including distributed training, hyperparameter tuning and inference. Build and tune ML models with SparkML while leveraging MLflow to track, version and manage these models.
Requirements: Intermediate experience with Python, experience building machine learning models, beginner experience with PySpark DataFrame API
Duration: Two days; live online in different time zones; also self-paced
Test: Proctored exam
Cost: $1,500 course, $200 exam
See more: Transforming Ecommerce With Artificial Intelligence & Machine Learning
6. Databricks Machine Learning Professional
Certifying Organization: DatabricksSkills Learned: Use MLflow to track the machine learning life cycle, package models for deployment and manage model versions. Examine various production issues, different deployment paradigms and post-production concerns.
Requirements: Intermediate experience with Python and pandas, working knowledge of machine learning and data science (scikit-learn, TensorFlow, etc.), familiarity with Apache Spark
Duration: One day; live online in different time zones; also self-paced
Test: Proctored exam
Cost: $1,000 course; $200 exam
7. Machine Learning
Certifying Organization: Cornell UniversitySkills Learned: Implement machine learning algorithms using Python. Understand how data scientists approach these problems programmatically.
Duration: 3.5 months at six to nine hours a week
Test: Applied projects
Cost: $3,750
8. Machine Learning With Python: From Linear Models to Deep Learning
Certifying Organization: Massachusetts Institute of Technology (MIT) Skills Learned: Understand principles behind machine learning problems, such as classification, regression, clustering and reinforcement learning. Implement and analyze models, such as linear models, kernel machines, neural networks and graphical models. Implement and organize machine learning projects, from training, validation and parameter tuning to feature engineering.
Requirements: Proficiency in Python programming. Probability theory course. Both available as other MIT courses.
Duration: 15 weeks at 10-14 hours a week; instructor-paced
Test: Graded assignments and exams
Cost: $300
9. Math for Machine Learning With Python
Certifying Organization: EdXSkills Learned: Essential mathematical concepts used in machine learning: equations, functions and graphs; differentiation and optimization; vectors and matrices; statistics and probability.
Requirements: Basic knowledge of math and of working with Python
Duration: Six weeks, six to eight hours a week
Test: Graded assignments and exams
Cost: $99
10. Python for Data Science and Machine Learning Boot Camp
Certifying Organization: Udemy
Skills Learned: How to use NumPy, pandas, Seaborn, Matplotlib, Plotly, scikit-learn, Tensorflow and more.
Requirements: Some programming experience
Duration: Over 25 hours
Cost: $109.99
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