Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Also gain practice in specialized topics such as Time Series Analysis and Survival Analysis.

• Compare and contrast different machine learning algorithms by creating recommender systems in Python
• Develop a final project using machine learning methods and evaluate your peers’ projects
• Predict course ratings by training a neural network and constructing regression and classification models
• Create recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering

Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics.

This program consists of 6 courses providing you with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning . You will follow along and code your own projects using some of the most relevant open source frameworks and libraries and you will apply what you have learned in various courses by completing a final capstone project. Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning.

This Professional Certificate has a strong emphasis on developing the skills that help you advance a career in Machine Learning. All the courses include a series of hands-on labs and final projects that help you focus on a specific project that interests you. Throughout this Professional Certificate, you will gain exposure to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will provide you with practical skills with applicability to Machine Learning jobs. These skills include:

Jupyter Notebooks and Watson Studio

Pandas, NumPy, Matplotlib, Seaborn, ipython-sql, Scikit-learn, ScipPy, Keras, and TensorFlow.

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.

• 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

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.

• 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

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.

• 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

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.

• 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

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.

• 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

In this Machine Learning Capstone course, you will be using various Python-based machine learning libraries such as Pandas, scikit-learn, Tensorflow/Keras, to:

• build a course recommender system,
• analyze course related datasets, calculate cosine similarity, and create a similarity matrix,
• create recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering,
• build similarity-based recommender systems,
• predict course ratings by training a neural network and constructing regression and classification models,
• build a Streamlit app that displays your work, and
• share your work then evaluate your peers.

Ph.D., Data Scientist at IBM IBM Developer Skills Network

Data Science Content Developer Skills Network

Digital Content Delivery Lead IBM Data & AI Learning

Machine Learning Curriculum Developer Data and AI Learning

Ph.D., Data Scientist and Developer IBM