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Calculus and Optimization for Machine Learning
Description Hi! Our course aims to provide necessary background in Calculus sufficient for up-following Data Science courses. Course starts with basic introduction to concepts concerning functional mappings. Later students are assumed to study limits (in case of sequences, single- and multivariate functions), differentiability (once again starting from single variable up to multiple cases), integration, thus…
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Predictive Modeling and Machine Learning with MATLAB
Description In this course, you will build on the skills learned in Exploratory Data Analysis with MATLAB and Data Processing and Feature Engineering with MATLAB to increase your ability to harness the power of MATLAB to analyze data relevant to the work you do. These skills are valuable for those who have domain knowledge and…
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Practical Machine Learning
Description One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error…
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Mathematics for Machine Learning: Linear Algebra
Description In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to solve problems. Finally we…
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Mathematics for Machine Learning: PCA
Description 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. Using all these…
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Mathematics for Machine Learning: Multivariate Calculus
Description This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build…
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Machine Learning with Python
Description This course dives into the basics of machine learning using an approachable, and well-known programming language, Python. In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of…
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Advanced Machine Learning and Signal Processing
Description >>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area
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AI Workflow: Machine Learning, Visual Recognition and NLP
Description This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up…
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Scalable Machine Learning on Big Data using Apache Spark
Description This course will empower you with the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. Most real world machine learning work involves very large data sets that go beyond the CPU, memory and storage limitations of a single computer. Apache Spark is an open source…
