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Mathematics for Machine Learning

Mathematics for Machine Learning

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science.

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  • English
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Matrix Methods

Matrix Methods

Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction. Optional examples using Python are used to illustrate the concepts and allow the learner to experiment with the algorithms.

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  • 7 hours
  • English
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Machine Learning: Theory and Hands-on Practice with Python

Machine Learning: Theory and Hands-on Practice with Python

In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems.

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Unsupervised Learning and Its Applications in Marketing

Unsupervised Learning and Its Applications in Marketing

Welcome to the Unsupervised Learning and Its Applications in Marketing course! In this course, you will delve into the fascinating world of unsupervised machine learning and its relevance to the field of marketing. Unsupervised learning is a powerful approach that allows us to uncover hidden patterns and insights from vast amounts of historical data without the need for explicit labels or human intervention.

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  • 22 hours
  • English
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Supervised Machine Learning: Regression and Classification

Supervised Machine Learning: Regression and Classification

In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online.

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  • 33 hours
  • English
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Big Data Science with the BD2K-LINCS Data Coordination and Integration Center

Big Data Science with the BD2K-LINCS Data Coordination and Integration Center

The Library of Integrative Network-based Cellular Signatures (LINCS) was an NIH Common Fund program that lasted for 10 years from 2012-2021. The idea behind the LINCS program was to perturb different types of human cells with many different types of perturbations such as drugs and other small molecules, genetic manipulations such as single gene knockdown, knockout, or overexpression, manipulation of the extracellular microenvironment conditions, for example, growing cells on different surfaces, and more.

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  • 9 hours
  • English
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Materials Data Sciences and Informatics

Materials Data Sciences and Informatics

This course aims to provide a succinct overview of the emerging discipline of Materials Informatics at the intersection of materials science, computational science, and information science. Attention is drawn to specific opportunities afforded by this new field in accelerating materials development and deployment efforts. A particular emphasis is placed on materials exhibiting hierarchical internal structures spanning multiple length/structure scales and the impediments involved in establishing invertible process-structure-property (PSP) linkages for these materials.

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  • 9 hours
  • English
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Advanced Learning Algorithms

Advanced Learning Algorithms

In the second course of the Machine Learning Specialization, you will: • Build and train a neural network with TensorFlow to perform multi-class classification • Apply best practices for machine learning development so that your models generalize to data and tasks in the real world • Build and use decision trees and tree ensemble methods, including random forests and boosted trees The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online.

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  • English
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Mathematics for Machine Learning: PCA

Mathematics for Machine Learning: PCA

This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces.

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  • 21 hours
  • English
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Unsupervised Learning, Recommenders, Reinforcement Learning

Unsupervised Learning, Recommenders, Reinforcement Learning

In the third course of the Machine Learning Specialization, you will: • Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection. • Build recommender systems with a collaborative filtering approach and a content-based deep learning method. • Build a deep reinforcement learning model. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online.

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  • 28 hours
  • English
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Network Analysis in Systems Biology

Network Analysis in Systems Biology

This course introduces data analysis methods used in systems biology, bioinformatics, and systems pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, clustering, dimensionality reduction, differential expression, enrichment analysis, and network construction. The course contains practical tutorials for using several bioinformatics tools and setting up data analysis pipelines, also covering the mathematics behind the methods applied by these tools and workflows.

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  • 30 hours
  • English
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Unsupervised Algorithms in Machine Learning

Unsupervised Algorithms in Machine Learning

One of the most useful areas in machine learning is discovering hidden patterns from unlabeled data. Add the fundamentals of this in-demand skill to your Data Science toolkit. In this course, we will learn selected unsupervised learning methods for dimensionality reduction, clustering, and learning latent features. We will also focus on real-world applications such as recommender systems with hands-on examples of product recommendation algorithms. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course.

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  • 38 hours
  • English
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