Math for Machine Learning

Offered by the University of Amsterdam, this course covers the fundamentals of statistics, including descriptive statistics, probability, and inferential statistics.

Offered by the University of California, Santa Cruz, this course introduces the Bayesian approach to statistics, covering probability, data analysis, and the key differences from the Frequentist approach.

This course covers fundamental mathematical concepts for machine learning, including derivatives, integrals, and optimization techniques like gradient descent.

This course in the DeepLearning.AI specialization covers the essential calculus concepts needed to understand and build machine learning models.

Offered by Duke University, this beginner-level course covers the foundational math skills needed for data science.

This course covers making inferences from sample data to the broader population. It delves into the principles of significance testing, including p-values, power, and Type I and II errors, and covers a wide range of statistical tests for different data types and research designs.

This course covers the essential concepts of multivariate calculus required for machine learning, including gradient descent and optimization. It is part of the Mathematics for Machine Learning Specialization.

An intermediate-level course that introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. It covers basic statistics of data sets, such as mean values and variances and the computation of distances and angles between vectors using inner products.

This Johns Hopkins University specialization provides a comprehensive overview of the entire data science pipeline, including statistical modeling and machine learning algorithms.

This specialization from DeepLearning.AI provides a foundational understanding of the mathematics essential for AI and machine learning. It covers linear algebra, calculus, probability, and statistics, with a focus on their application in data science. Learners will gain skills in statistical hypothesis testing, Bayesian statistics, and exploratory data analysis.

A self-paced course that provides a solid knowledge base in statistics, linear algebra, multivariable calculus, and probability for AI.

This beginner's course builds an understanding of the essential math required for data analytics.

A free and in-depth course on multivariable calculus, an essential topic for understanding optimization in machine learning.

Unlock the Power of Calculus in Machine Learning, Deep Learning, Data Science, and AI with Python: A Comprehensive Guide to Mastering Essential Mathematical Skills"Are you striving to elevate your status as a proficient data scientist? Do you seek a distinctive edge in a competitive landscape? If you're keen on enhancing your expertise in Machine Learning and Deep Learning by proficiently applying mathematical skills, this course is tailor-made for you.Calculus for Deep Learning: Mastering Calculus for Machine Learning, Deep Learning, Data Science, Data Analysis, and AI using Python Embark on a transformative learning journey that commences with the fundamentals, guiding you through the intricacies of functions and their applications in data fitting. Gain a comprehensive understanding of the core principles underpinning Machine Learning, Deep Learning, Artificial Intelligence, and Data Science applications.Upon mastering the concepts presented in this course, you'll gain invaluable intuition that demystifies the inner workings of algorithms. Whether you're crafting self-driving cars, developing recommendation engines for platforms like Netflix, or fitting practice data to a function, the essence remains the same.Key Learning Objectives:Function Fundamentals: Initiate your learning journey by grasping the fundamental definitions of functions, establishing a solid foundation for subsequent topics.Data Fitting Techniques: Progress through the course, delving into data fitting techniques essential for Machine Learning, Deep Learning, Artificial Intelligence, and Data Science applications.Approximation Concepts: Explore important concepts related to approximation, a cornerstone for developing robust models in Machine Learning, Deep Learning, Artificial Intelligence, and Data Science.Neural Network Training: Leverage you

Welcome to the exciting world of Matrix Calculus, a fundamental tool for understanding and solving problems in machine learning and data science. In this course, we will dive into the powerful mathematics that underpin many of the algorithms and techniques used in these fields. By the end of this course, you'll have the knowledge and skills to navigate the complex landscape of derivatives, gradients, and optimizations involving matrices.Course Objectives:Understand the basics of matrix calculus, linear and quadratic forms, and their derivatives.Learn how to utilize the famous Matrix Cookbook for a wide range of matrix calculus operations.Gain proficiency in optimization techniques like gradient descent and Newton's method in one and multiple dimensions.Apply the concepts learned to real-world problems in machine learning and data science, with hands-on exercises and Python code examples.Why Matrix Calculus? Matrix calculus is the language of machine learning and data science. In these fields, we often work with high-dimensional data, making matrices and their derivatives a natural representation for our problems. Understanding matrix calculus is crucial for developing and analyzing algorithms, building predictive models, and making sense of the vast amounts of data at our disposal.Section 1: Linear and Quadratic Forms In the first part of the course, we'll explore the basics of linear and quadratic forms, and their derivatives. The linear form appears in all of the most fundamental and popular machine learning models, including linear regression, logistic regression, support vector machine (SVM), and deep neural networks. We will also dive into quadratic forms, which are fundamental to understanding optimization problems, which appear in regression, portfolio optimization in finance, signal processing, and control theory.The Mat

This course is designed to get an in-depth knowledge of Statistics and Probability for Data Science and Machine Learning point of view. Here we are talking about each and every concept of Descriptive and Inferential statistics and Probability. We are covering the following topics in detail with many examples so that the concepts will be crystal clear and you can apply them in the day to day work. Extensive coverage of statistics in detail: The measure of Central Tendency (Mean Median and Mode) The Measure of Spread (Range, IQR, Variance, Standard Deviation and Mean Absolute deviation) Regression and Advanced regression in details with Hypothesis understanding (P-value) Covariance Matrix, Karl Pearson Correlation Coefficient, and Spearman Rank Correlation Coefficient with examples Detailed understanding of Normal Distribution and its properties Symmetric Distribution, Skewness, Kurtosis, and KDE. Probability and its in-depth knowledge Permutations and Combinations Combinatorics and Probability Understanding of Random Variables Various distributions like Binomial, Bernoulli, Geometric, and Poisson Sampling distributions and Central Limit Theorem Confidence Interval Margin of ErrorT-statistic and F-statistic Significance tests in detail with various examples Type 1 and Type 2 Errors Chi-Square Test ANOVA and F-statistic By completing this course we are sure you will be very much proficient in Statistics and able to talk to anyone about stats with confidence apply the knowledge in your day to day work.

This course will help you in understanding of the Linear Algebra and math’s behind Data Science and Machine Learning. Linear Algebra is the fundamental part of Data Science and Machine Learning. This course consists of lessons on each topic of Linear Algebra + the code or implementation of the Linear Algebra concepts or topics.There’re tons of topics in this course. To begin the course:We have a discussion on what is Linear Algebra and Why we need Linear Algebra Then we move on to Getting Started with Python, where you will learn all about how to setup the Python environment, so that it’s easy for you to have a hands-on experience.Then we get to the essence of this course;Vectors & Operations on Vectors Matrices & Operations on Matrices Determinant and Inverse Solving Systems of Linear Equations Norms & Basis Vectors Linear Independence Matrix Factorization Orthogonality Eigenvalues and Eigenvectors Singular Value Decomposition (SVD)Again, in each of these sections you will find Python code demos and solved problems apart from the theoretical concepts of Linear Algebra.You will also learn how to use the Python's numpy library which contains numerous functions for matrix computations and solving Linear Algebric problems.So, let’s get started….

This course is ideal for you if you want to gain knowledge in statistical methods required for Data Science and machine learning!Learning Statistics is an essential part of becoming a professional data scientist. Most data science learners study python for data science and ignore or postpone studying statistics. One reason for that is the lack of resources and courses that teach statistics for data science and machine learning.Statistics is a huge field of science, but the good news for data science learners is that not all statistics are required for data science and machine learning. However, this fact makes it more difficult for learners to study statistics because they are not sure where to start and what are the most relevant topics of statistics for data science.This course comes to close this gap.This course is designed for both beginners with no background in statistics for data science or for those looking to extend their knowledge in the field of statistics for data science.I have organized this course to be used as a video library for you so that you can use it in the future as a reference. Every lecture in this comprehensive course covers a single topic.In this comprehensive course, I will guide you to learn the most common and essential methods of statistics for data analysis and data modeling.My course is equivalent to a college-level course in statistics for data science and machine learning that usually cost thousands of dollars. Here, I give you the opportunity to learn all that information at a fraction of the cost! With 77 HD video lectures, many exercises, and two projects with solutions.All materials presented in this course are provided in detailed downloadable notebooks for every lecture.Most students focus on learning python codes for data science, however, this is not enough to be a proficient data scientis

Short Summary about the need and importance of the Course Linear Algebra is the backbone of Data Science, Machine Learning (ML), and Artificial Intelligence (AI). Understanding its core concepts is essential to grasp the functionality of ML algorithms. However, most courses make this process overwhelming by focusing on complex calculations rather than the practical application you need to understand the working of Machine Learning Algorithms. How our course is different ?We’ve designed this Linear Algebra course specifically for aspiring Data Scientists and Machine Learning enthusiasts who want to dive into the essentials without wasting time. In just around 7.5 hours, you’ll master the key concepts required for Machine Learning, with a clear focus on how these concepts apply directly to real-world Machine Learning algorithms. This Course will teach you the geometric intuition and essential computations so that you can think like a Machine Learning Expert.Please find the Complete Syllabus for the Course below Mathematics for Machine Learning: 1. Introduction to linear Algebra Difference between Algebra and Linear Algebra, Definition of Linear Algebra, Linear Equation and System of linear equations with an Example, Attributes and properties of system of linear equation.Mathematics for Machine Learning: 2. Geometric representation of an expression Geometric visualization of an algebraic expression with an example, Gradient of a straight line, Generalization of an expression geometrically on an N dimensional plane.Mathematics for Machine Learning: 3. Importance of a System of linear Equation Definition and Goal of System of Linear Equations, General form of system of Linear Equations, representing a dataset in terms of System of linear equations, Applications of system of linear equations in solving a classification and a regression problem with an e

Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Probability is one of the most important math prerequisites for data science and machine learning. It's required to understand essentially everything we do, from the latest LL Ms like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call "probability part 2").Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Principal Components Analysis, and Neural Networks, all make use of probability.In short, probability cannot be avoided!If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know probability.This course will cover everything that you'd learn (and maybe a bit more) in an undergraduate-level probability class. This includes random variables and random vectors, discrete and continuous probability distributions, functions of random variables, multivariate distributions, expectation, generating functions, the law of large numbers, and the central limit theorem.Most important theorems will be derived from scratch. Don't worry, as long as you meet the prerequisites, they won't be difficult to understand. This will ensure you have the strongest foundation possible in this subject. No more memorizing "rules" only to apply them incorrectly / inappropriately in the future! This course will provide you with