IBM Machine Learning Certificate Online

Add to your skill set with IBM certified machine learning courses that introduce you to the tools, data sets and algorithms behind deep learning, reinforcement learning and more.

Franklin University has partnered with Coursera Campus to provide cutting-edge certificates to learners seeking to advance. Courses are open to all learners. No application required.

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Get unlimited access to over 7,000 offerings found on the Coursera website – including guided projects, specializations and professional certificates offered by hundreds of leading universities and companies. You also get access to all 39 professional certificates found in the Franklin Marketplace.

What You Will Learn

• Develop a solid understanding of machine learning (ML), including deep learning and reinforcement learning
• Learn how to retrieve data, ready it for analysis and testing, train predictive models, and use clustering and dimensionality reduction algorithms
• Discover best practices for supervised and unsupervised ML, including how to select the best algorithm for the data
• Acquire hands-on experience in identifying common modeling challenges and applying Time Series classification and Survival Analysis models for forecasting and analyzing censored data

About the IBM Machine Learning (ML) Professional Certificate

If math, statistics and computer programming rank high on your list of interests, then the IBM Machine Learning (ML) Professional Certificate is for you. This specialization is ideal for anyone wanting to advance their career in data science, ML and AI applications.

Through six, self-paced courses under the instruction of IBM Data and AI Learning experts, you'll develop the in-demand knowledge and skills needed to work with the cloud services, dataset, libraries and tools used by machine learning professionals.

In this certificate program you'll start by learning the theoretical concepts and best practices of machine learning. Then you'll be introduced to regression techniques, classification and algorithm selection. Finally, you'll apply what you learn through project labs using real-world datasets to give you relevant experience with such machine learning essentials as ML algorithms, Jupyter Notebooks, Watson Studio, TensorFlow, Pandas, Keras and more.

In addition to coding your own programs using open source frameworks and libraries, you'll acquire hands-on experience with deep learning and reinforcement learning. Plus, you'll have the opportunity to learn about additional topics in machine learning, including Time Series Analysis and Survival Analysis.

Upon successful completion of all the courses and projects in this Professional Certificate, you'll earn both your certificate and and IBM digital badge, recognizing your proficiency in machine learning.

Required IBM Machine Learning Certificate Courses

Exploratory Data Analysis for Machine Learning

INTERMEDIATE | Data Science | Self-paced | 14 hours

This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn 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 should 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 why feature scaling is important and use a variety of scaling techniques   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Machine Learning and Artificial Intelligence in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

Supervised Machine Learning: Regression

INTERMEDIATE | Data Science | Self-paced | 21 hours

This course introduces you to one of the main types of modelling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques. By the end of this course you should 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 that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Supervised Machine Learning Regression techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Supervised Machine Learning: Classification

INTERMEDIATE | Data Science | Self-paced | 25 hours

This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to 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 should 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   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Unsupervised Machine Learning

INTERMEDIATE | Data Science | Self-paced | 23 hours

This course introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how 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 should 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 Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

Deep Learning and Reinforcement Learning

INTERMEDIATE | Data Science | Self-paced | 32 hours

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 frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few  Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future. After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. By the end of this course you should 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 Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.

Machine Learning Capstone

ADVANCED | Data Science | Self-paced | 20 hours

This Machine Learning Capstone course uses various Python-based machine learning libraries, such as Pandas, sci-kit-learn, and Tensorflow/Keras. You will also learn to apply your machine-learning skills and demonstrate your proficiency in them. Before taking this course, you must complete all the previous courses in the IBM Machine Learning Professional Certificate.   In this course, you will also learn to build a course recommender system, analyze course-related datasets, calculate cosine similarity, and create a similarity matrix. Additionally, you will generate recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering.  Finally, you will share your work with peers and have them evaluate it, facilitating a collaborative learning experience. 

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