IBM Machine Learning

This certificate program features IBM deep learning, a machine learning technology, and uses Python. Python is a straight-forward, well-known programming language used to teach the basics of machine learning.

In 2019, the position of machine learning engineer ranked as the number one job in the U.S., with a base salary of $146,000. Between 2015 and 2018, job openings in the field increased by more than 300%!

Practice with real-world examples of machine learning and by the end of the program you will have:

  • New Python programming skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy.
  • New projects to add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines and many more.
  • A certificate in machine learning to prove your competency and share online or offline (LinkedIn profiles and social media).

Machine Learning

Develop working skills in machine learning including supervised learning, unsupervised learning and deep learning

Time Series Analysis

Gain practice in specialized skills such as Time Series Analysis and Survival Analysis

Code Your Own

Code your own projects using the most relevant open source frameworks and libraries and gain a solid theoretical and practical understanding of the main algorithms, uses and best practices of machine learning

Professional Certificate Programs enable you to become empowered and successful in every phase of your job!

Dana Baker

Dana Baker, Executive Director of Regional Campuses

"We are committed to developing current and relevant coursework to help transform our next generation of leaders."

IBM Machine Learning

100% Online

Learn on your own schedule

Flexible Schedule

Set and maintain flexible deadlines

Entry Level

No previous experience required

6-Months to Complete

Suggested pace of 10 hours/week; 6 Courses

IBM Machine Learning with Python Professional Certificate Courses

Exploratory Data Analysis for Machine Learning

In this course you will realize the importance of quality data and learn the common techniques to retrieve your data, clean it, apply feature engineering and have it ready for preliminary analysis and hypothesis testing.

By the end of this course, you will be able to:

  • Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud.
  • Describe and use common feature selection and feature engineering techniques.
  • Handle categorical and ordinal features, as well as missing values.
  • Use a variety of techniques for detecting and dealing with outliers.
  • Articulate the importance of feature scaling and use a variety of scaling techniques.

Supervised Machine Learning: Regression

This course introduces you to regression, a main modeling family for supervised machine learning. You will learn to train regression models to predict continuous outcomes and use error metrics to compare across different models. This course also explores best practices, including train and test splits and regularization techniques.

By the end of this course, you will be able to:

  • Differentiate uses and applications of classification and regression in the context of supervised machine learning.
  • Describe and use linear regression models.
  • Use a variety of error metrics to compare and select a linear regression model to best suit your data.
  • Articulate why regularization may help prevent overfitting.
  • Use regularization regressions: Ridge, LASSO, and Elastic net.

Supervised Machine Learning: Classification

This course introduces you to classification, another main modeling family for supervised machine learning. You will learn to train predictive models to classify categorical outcomes and use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits and handling data sets with unbalanced classes.

By the end of this course, you will be able to:

  • Differentiate uses and applications of classification and classification ensembles.
  • Describe and use logistic regression models.
  • Describe and use decision tree and tree-ensemble models.
  • Describe and use other ensemble methods for classification.
  • Use a variety of error metrics to compare and select the classification model that best suits your data.
  • Use oversampling and undersampling as techniques to handle unbalanced classes in a data set.

Unsupervised Machine Learning

This course introduces you to unsupervised learning, one of the main types of machine learning: You will learn to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension-reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.

By the end of this course, you will be able to:

  • Explain the kinds of problems suitable for unsupervised learning approaches.
  • Explain the curse of dimensionality and how it makes clustering difficult with many features.
  • Describe and use common clustering and dimensionality-reduction algorithms.
  • Try clustering points where appropriate, compare the performance of per-cluster models.
  • Understand metrics relevant for characterizing clusters.

Deep Learning and Reinforcement Learning

This course introduces you to two of the most sought-after disciplines in machine learning…deep learning and reinforcement learning. Deep learning is a subset of machine learning that has applications in both supervised and unsupervised learning and is most frequently used to power most AI applications. You will learn the theory behind neural networks, the basis of deep learning. After you develop some deep learning models, you will focus on reinforcement learning, a type of machine learning currently trending. Although used for just a few practical applications today, reinforcement learning is a promising area of  AI research that might become relevant in the near future.

By the end of this course, you will be able to:

  • Explain the kinds of problems suitable for unsupervised learning approaches.
  • Explain the curse of dimensionality and how it makes clustering difficult with many features.
  • Describe and use common clustering and dimensionality-reduction algorithms.
  • Try clustering points where appropriate, compare the performance of per-cluster models.
  • Understand metrics relevant for characterizing clusters.

Specialized Models: Time Series and Survival Analysis

This course introduces you to additional machine learning topics that complement essential tasks, including forecasting and analyzing censored data. You will learn to find analyze data with a time component and censored data that needs outcome inference. You will learn techniques for time series analysis and survival analysis. The hands-on section of this course focuses on using best practices and verifying assumptions derived from statistical learning.

By the end of this course, you will be able to:

  • Identify common modeling challenges with time series data.
  • Explain how to decompose time series data: trend, seasonality, and residuals.
  • Explain how autoregressive, moving average and ARIMA models work.
  • Understand how to select and implement various time series models.
  • Describe hazard and survival modeling approaches.
  • Identify types of problems suitable for survival analysis.

Skills you will gain: