IBM Machine Learning

This first course introduces you to Machine Learning and the content of the professional certificate. You will learn common techniques to retrieve 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
• Use common feature selection and feature engineering techniques
• Handle categorical and ordinal features, as well as missing values
• Detect and deal with outliers
• 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 use error metrics to compare across different models.

• 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
• 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 use error metrics to compare across different models.

• 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 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 and several clustering and dimension reduction algorithms for unsupervised learning.

• Explain the kinds of problems suitable for Unsupervised Learning approaches
• 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. You will learn about the theory behind Neural Networks, as well as modern architectures of Deep Learning, and focus on Reinforcement Learning.

• Explain the kinds of problems suitable for Unsupervised Learning approaches
• 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 module focuses on comparing and contrasting different machine learning algorithms, creating recommender systems in Python, and developing a final project using machine learning methods. You will also evaluate your peers' projects and create recommendation systems by applying your knowledge of KNN, PCA, and non-negative matrix collaborative filtering.