Linear Algebra for Machine Learning

Now available on-demand. The course includes the following modules

• Intro to Linear Algebra
• Linear Algebra II: Matrix Operations

Over the course of studying this topic, you’ll:

Calculus for Machine Learning

Now available on-demand. The course includes the following modules

• Calculus I: Limits & Derivatives
• Calculus II: Partial Derivatives & Integrals

Over the course of studying this topic, you’ll:

• Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning.
• Be able to more intimately grasp the details of machine learning papers as well as many of the other subjects that underlie ML, including partial-derivative calculus, statistics, and optimization algorithms.
• Compute the derivatives of functions, including by using AutoDiff in the popular TensorFlow 2 and PyTorch libraries.
• Develop an understanding of what’s going on beneath the hood of machine learning algorithms, including those used for deep learning.
• Be able to grasp the details of the partial-derivative, multivariate calculus that is common in machine learning papers as well as many in other subjects that underlie ML, including information theory and optimization algorithms.
• Use integral calculus to determine the area under any given curve, a recurring task in ML applied, for example, to evaluate model performance by calculating the ROC AUC metric.

Probability and Statistics

Now available on-demand. The course includes the following module

• Probability and Information Theory
• Intro to Statistics

Over the course of studying this topic, you’ll:

• Develop an understanding of what’s going on beneath the hood of predictive statistical models and machine learning algorithms, including those used for deep learning.
• Understand the appropriate variable type and probability distribution for representing a given class of data, as well as the standard techniques for assessing the relationships between distributions.
• Apply information theory to quantify the proportion of valuable signal that’s present amongst the noise of a given probability distribution.
• Develop an understanding of what’s going on beneath the hood of predictive statistical models and machine learning algorithms, including those used for deep learning.
• Hypothesize about and critically evaluate the inputs and outputs of machine learning algorithms using essential statistical tools such as the t-test, ANOVA, and R-squared.
• Use historical data to predict the future using regression models that take advantage of frequentist statistical theory (for smaller data sets) and modern machine learning theory (for larger data sets), including why we may want to consider applying deep learning to a given problem.

Computer Science

Now available on-demand. The course includes the following modules

• Algorithms and Data Structures
• Optimization

Over the course of studying this topic, you’ll:

• Use “Big O” notation to characterize the time efficiency and space efficiency of a given algorithm, enabling you to select or devise the most sensible approach for tackling a particular machine learning problem with the hardware resources available to you.
• Get acquainted with the entire range of the most widely-used Python data structures, including list-, dictionary-, tree-, and graph-based structures.
• Develop an understanding of all of the essential algorithms for working with data, including those for searching, sorting, hashing, and traversing.
• Discover how the statistical and machine learning approaches to optimization differ, and why you would select one or the other for a given problem you’re solving.
• Find out how the extremely versatile (stochastic) gradient descent optimization algorithm works, including how to apply it — from a low, in-depth level as well as from a high, abstracted level — within the most popular deep learning libraries, Tensorflow and PyTorch
• Get acquainted with the “fancy” optimizers that are available for advanced machine learning approaches (e.g., deep learning) and when you should consider using them.