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Online you can learn it for free with excellent, high-quality courses at all levels.

Here are:

Algebra: Elementary to Advanced

The course is in English and is a beginner level course. This is course 1 of 3 in the Algebra: Elementary to Advanced Specialization. If you are seeking a good base in mathematical concepts from which to take advanced courses using concepts from precalculus, Calculus, Probability, and Statistics then this is where you need to begin. You will consolidate your computational methods, review Algebraic formulas and properties and apply these concepts to actual everyday situations. The course topics include many of the basic Algebraic concepts which can be seen in the skills set below. Module 1 covers The Structure of Numbers; Module 2 covers Linear Equations; Module 3 covers Solving Inequalities; Module 4 delves into Systems of Equations; and finally in Week 5 is an exam on Equations with Inequalities and Real Numbers. **Skills Acquired:** Equalities, Inequalities, Polynomials, Radicals, Exponents, and Quadratic Equations. **Duration**: Approx 10 hours over 5 weeks. **Rating:** 4.9/95%

After much research on all online Math Courses on offer, we have found the top 22 Math courses to make it easier for you to choose

This is an intermediate course in English with subtitles in 8 different languages. Discreet mathematics while fascinating in itself , is the mathematical base of computer and information science. You will learn about a broad range of mathematical concepts such as sets, relations, functions, graphs, that are very common to computer science and you will reach a level of understanding concerning formal statements and their proofs, arriving at rigorous proofs yourself and interesting results. However, the mathematics is not very formal so for each concept you will be shown 1 interesting result with a full proof, but not with too much formal notation. This course does not cover modular arithmetic, algebra, and logic. The syllabus covers: Introduction- Basic Objects in Discreet Maths, Partial Orders, Enumerative Combinatorics, The Binomial Coefficient, Asymptotics, and the O-Notation, Introduction to Graph Theory, Connectivity, Trees, Cycles, Eulerian and Hamiltonian Cycles, Spanning Trees, Maximum Flow and Minimum Cut, and finally Matchings in Bipartite Graphs.

**Skills Acquired:** Mathematics of Computer Science, Sets, Functions, Relations, Graphs, and P:roofs

**Duration**: Approx 42 hours over 11 weeks.**Rating:** 3.3/83%

Mathematics for Machine Learning: PCA

This is a intermediate level course in English with subtitles in 8 different languages. It is the 3^{rd} course in Mathematics for Machine Learning Specializations. The mathematical basis to derive Principal Component Analysis (PCA), which is a Dimensionality Reduction Technique will be introduced. Some basic statistics like mean values, variances, distance and angle between vectors will be calculated utilizing inner products and you will derive orthogonal projections of data onto lower dimensional subspace. Utilizing these tools you will derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. Buy the conclusion of the course you will know and understand these important mathematical concepts and be able to implement PCA on your own. For guidance a set of Jupyter notebooks can be found to further explore properties of the techniques, which will get you back on track. The requirements for this course are: Ability to think abstractly, a background of linear algebra (Matrix and Vector algebra, linear independence, basis), an understanding of multivariate calculus (eg Partial derivative, and basic optimization), and finally a basic knowledge of Python programming and Numpy. The modules comprise of: Statistics of Datasets, Inner Products, Orthogonal Projections and Principal Components Analysis.

**Skills Acquired: **Dimensionality reduction, Python Programming and Linear Algebra**Duration:** Approx 20 hours over 4 weeks. **Rating:** 4.0/82%

This is an intermediate level course in English with subtitles in 4 different languages. Mathematical

Matrix methods underlying most methods of machine learning and data analysis of tables of data. You will learn elementary Matrix Methods, including Matrix-Matrix Multiplication, how to solve linear equations, orthogonality, and best least squares approximation. You will uncover Singular Value Decomposition that is fundamental to dimensional reduction, Principal Component Analysis and Noise reduction. You will engage with optional Python examples which will be used to illustrate the concept and this will allow you to experiment with the algorithms. This syllabus is taught by means of video and prescribed readings as well as quizzes and covers the following topics: Matrices as Mathematical Objects, Matrix Multiplication and other Operations, Systems of Linear Equations, Linear Least Squares and finally, Singular Value Decomposition.

**Skills Acquired: **Matrices, Matrix Operations, Linear Equations, and Singular Value Decomposition. **Duration:** Approx 7 hours over 5 weeks. **Rating:** 4.1

This course is a beginner level in English. If you are a student preparing to enter college life but need to take the Math Placement Exam then this course will prepare you adequately. Further, all students taking the college board Accuplacer Exam can also utilize the information in this course. Further, this is information in the course specifically tailored to Texas students because of initiatives set by the Texas Higher education Coordinating Board. The modules consist of a number of readings and Week 1 is an introduction to the course and comprises 3 lessons regarding number sense, a quiz at the end of each lesson reinforces the concepts. The second week covers Elementary Algebra and consists of 4 lessons on Elementary Algebra including the solving of linear equations and inequalities, math models, graphing and systems of equations. The third week looks at Intermediate Algebra which comprises of 5 lessons and this is the most extensive module so please set a reasonable time frame to grasp the concepts. The final week covers Geometry and Statistics and contains 2 lessons which concludes the course. **Skills Acquired:** Algebra, Numbers, Geometry and Statistics. **Duration:** Approx 35 hours over 4 weeks. **Rating:** 4.2

This is a beginner level course in English. Through you will acquire knowledge of statistics as applied in business situations. Occasionally students enrolling themselves at a University to read a Master of Business Administration (MBA) make come from a position of not having had any training or very little training in mathematics and statistics. This course will then give you the tools to understand how basic statistics are calculated for navigating and utilizing the formulas included within Excel and you will also be able to apply these formulas in a business setting or situation. Week 1 introduces you to the course, Week 2 considers Descriptive Statistics which comprises of means, medians, and modes, standard deviations and some other basic concepts, Week 3 covers data and data visualization: Excel tools and techniques - Excel tools and techniques for data visualization will be discussed and in week 4 you will look at equations and inverting equations which is a kind of algebra refresher. Week 5 covers Common Business Concepts and Mathematical Applications in business setting. In week 6 you will see and learn Calculus and Marginal Analysis in a Business Context. Finally, in Week 7 you will study Regression Analysis. **Skills Acquired: **Business, Microsoft Excel, Statistical Analysis, and General Statistics. **Duration:** Approx 13 hours over 7 weeks. **Rating:** 4.4

This course is a beginner level n English with subtitles in 9 different languages. This course will teach you the maths you will require to be a success in data science and was designed for those who have basic maths skills but who have not studied algebra or precalculus. This course teaches the mathematics that data science is built upon and introduces previously unexplored ideas and math symbols individually. At the conclusion of this course you would have mastered the vocabulary, notation, concepts, and algebra rules that any data scientist should know before studying more advanced courses. Some of the topics covered include: Set Theory and Venn diagrams; Properties of the real number line; Interval notation and algebra with inequalities; Uses for summation and Sigma notation; math on the Cartesian (x,y) plane; graphing; concept of instantaneous rate of change and tangent lines to a curve; Exponents, Logarithms, and Natural log function; And finally, Probability theory, including Bayes’ theorem. This course is considered a prerequisite for people interested in studying the course “Mastering Data Analysis in Excel” , which is part of the Excel to MySQL Data Science Specialization.

This course is in English and has subtitles in 4 different languages. This course focuses on the conceptual understanding and application of Single Variable Calculus which explains things such as planetary orbits to things like the optimal size of a city. This course is perfect for students starting to study in the engineering, physical, and social sciences. The course includes an introduction to Taylor series and approximations, discreet and continuous forms of Calculus, with an emphasize on concepts rather than computation. The modules cover a Calculus for sequences in Week 1, Week 2 introduces Numerical Methods, in Week 3 you will study Series and Convergence Tests, Week 4 covers the Power and Taylor series, and finally in Week 5 Concludes with Single Variable Calculus. **Skills Acquired: **Taylor series, and Single Variable Calculus. **Duration:** Approx 14 hours over 5 weeks

**Rating:** 4.6

Mathematices for Machine Learning Specialization

This course is a beginner level in English with subtitles in 9 different languages. For many high level machine learning and data science courses you will need to refresh your mathematics basics which you may have already studied but which was taught in a different context so it is difficult to relate to how it is used in the field of Computer Science. This specialization will rebuild your mathematics learnings in that it relates what you already know to Machine Learning and Data Science. The 1^{st} course looks at Linear Algebra and how it relates to data after which you will reconsider Vectors and Matrices and how to work with them. The 2^{nd} course deals with Multivariate Calculus which builds on the 1^{st} course and you will see how to optimize fitting functions to get good fits to data. It begins from introductory Calculus and then uses your learning from course 1 to look at data fitting. The 3^{rd} course which is Dimensionality Reduction with Principal Component Analysis (PCA), uses the Mathematics from the 1^{st} two courses to compress Hi-Dimensional data. This course will also require knowledge of Python and Numpy. By the end of this specialization you would have the mathematical knowledge at your finger tips to take more advanced courses in machine learning. Through the assignments set you will use your new found skills to produce mini projects with Python on interactive notebooks to help you to apply the knowledge to actual problems. As an example, utilizing Linear algebra to calculate the page rank of a small simulated internet, training your own neural network applying Multivariate Calculus, and perform a non-Linear least squares regression to fit a model to a data set. As this is a specialization you can start with any course you prefer and then continue as needed.

**Skills Acquired:** Eigenvalues and Eigenvectors, Principal Component Analysis (PCA), Multivariable, Calculus, Linear Algebra, Basis (Linear Algebra), Transformation Matrix, Linear Regression, Vector Calculus, Gradient Descend, Dimensionality Reduction, and Python Programming

**Duration:** Approx 4 months at a pace of 4 hours per week - 4 courses. **Rating:** 4.6

This course is in English and has subtitles in 4 different languages. It can be learnt at your own schedule or pace. This course will assist you in analyzing the implications of Constructivism for learning and teaching in science, mathematics and technology as a focus, The course consists of readings, discussion and assignments where you will examine constructivist views of learning, research on student’s ideas and idea- based interactions, instructional approaches taking student ideas into account, and the challenges faced in the implementation of Constructivist perspectives in instruction. The medium of instruction is video with readings and quizzes. In Week 1 - is the course orientation which will give you the technical skill required for the course, Week 2 - leads to module 1 which covers An Introduction to Constructivism, Week 3 - delves into module 2 Student Ideas., Week 4 is module 3 which covers helping students develop their ideas, where constructivist learning environments and their aspects are explored, Week 5 - covers the 4^{th} module which is Implementing Constructivist learning environments. **Skills Acquired:** Constructivism Perspectives. **Duration:** Approx 10 hours over 5 weeks. **Rating:** 4.7

Mathematics for Machine Learning: Multivariate Calculus

This course is a beginner level in English with subtitles in 9 different languages. In this course you will be introduced to Multivariate Calculus which is needed to build machine learning techniques. You will initially refresh your knowledge on the formulation of a slope before converting this to the formal definition of the gradient of a function. You will then learn as a set of tools which you will build for making Calculus faster and easier. Thereafter, Vector calculation that point uphill on multidimensional services will be taught and put into action using interactive game. You will explore how to use Calculus to build approximations to functions and to quantify how accurate we can expect those approximations to be. The course discusses where Calculus arises in the training of neural networks before you are shown how it is applied in Linear Aggression models. This course offers an intuitive understanding of Calculus and the language you would require to research concept yourself when you get stuck. IN Week 1 you will cover What is Calculus?, Week 2 explores Multivariate Calculus, Week 3 delves into Multivariate Chain Rule and its applications while Week 4 looks at the Taylor series and Linearisation, Week 5 is Intro to Optimization, and finally Week 6 goes into Regression.

**Skills Acquired:** Linear Aggression, Vector Calculus, Multivariable Calculus, and Gradient Descent

**Duration:** Approx 18 hours over 6 weeks. **Rating:** 4.7/91%

Mathematics fro Machine Learning: Linear Algebra

The course is beginner level in English and has subtitles in 8 different languages. This course looks at what Linear Algebra is and how it relates to Vectors and Matrices. Next you will explore Vectors, Matrices, how to work with them, and the problem of Eigenvalues and Eigenvectors, and how to use them to solve problems. Lastly, you will see how to use these to do incredible things with data sets such as rotating an image of a face and how to extract Eigenvectors to see how a Pagerank algorithm works. As you are aiming at data driven applications, some of these ideas will be implemented in code and by the end of the course you will be able write code blocks and encounter Jupyter notebooks in Python, these will be short, focused on concept, and will guide you if you have not coded before. Byt the end of the course you will understand Vectors and Matrices to bridge the cap into Linear algebra problems and the application of these concepts to machine learning. The syllabus in Week 1 - Introduces Linear Algebra and Mathematics for Machine Learning, Week 2 - explores Vectors objects that move around in space, Week 3 - covers Matrices in linear Algebra: Objects that operate on Vectors, Week 4 - delves into Matrices make Linear mappings and finally in Week 5 - gets into eigenvalues and Eigenvectors: Application to data problems. The method of instruction is through the medium of videos, readings and quizzes. **Skills Acquired:** Eigenvalues and Eigenvectors, Basis (Linear Algebra), Transformation Matrix, and Linear Algebra. **Duration:** Approx 19 hours over 5 weeks. **Rating:** 4.7/92%

Mathematics for Engineers: The Capstone Course

This course is an intermediate level in English. This course presupposes knowledge of Matrix Algebra, Differential Equations, Vector Calculus, Numerical Methods and the MATLAB programming language.

This is the final project for students completing the Mathematics for Engineers specialization. You will first however learn some basic Computational Fluid dynamics concepts and then apply these to compute the fluid flow around a cylinder. Access will be granted to all enrolled students to MATLAB online and the MATLAB grader. Prior to enrolling you should already have knowledge of or have taken courses in differential Equations, Vector Calculus, and Numerical Methods and be able to program in MATLAB. There are 22 short video lectures and a full set of lecture notes which you can download from http://www.math.ust.hk/~machas/flow-around/a-cylinder.pdf

Each lecture concludes with a problem to solve and at the end of the 2^{nd} and 3^{rd} weeks there is a MATLAB programming assignment. The syllabus includes Governing Equations in Week 1, in Week 2 - covers Steady Flows and Week 3 - will explore unsteady flows. **Skills Acquired:** Computational Fluid Dynamics, and Scientific Computing. **Duration**: Approx 1o hours over 3 weeks. **Rating:** 4.8

This course is a beginner level in English with subtitles in 8 different languages. Those who need information and understanding regarding differential equations for practical use in their own fields will derive great benefit from this course. You will learn basic terminologies, concepts, methods of solving various types of differential equations and a basic but necessary knowledge of the theory and related applications. You should have a basic understanding of Calculus and elementary theory of matrices like determinants, Cramers’s Rule for solving linear systems of equations, eigenvalues and eigenvectors.** Skills Acquired:** First Order Differential Equations, Modelling, and Linear Second Order Equations .** Duration: **Approx 15 hours over 9 weeks.** Rating:** 4.7/95%

The course is in English with subtitles in 9 different languages. Calculus explains everything from periodicity of a heartbeat to the optimal size of a city, and this course covers single variable Calculus with the emphasis on conceptual understanding and their application. If you are studying in the engineering physical or social sciences this course will appeal to you. In this first part of 5 you will expand your understanding of Taylor series, review limits, understand the “Why” behind l’Hopital’s rule, and you will learn a new language to describe the growth and the decay of functions: The Big O.

The syllabus, after the introduction covers: In week 2 - A Review of Functions (the basics of pre calculus), Week 3 - explores the Taylor series, and finally Week 4 - covers Limits and Asymptotics. **Skills Acquired: **Series Expansions, and Calculus. **Duration:** Approx 14 hours over 4 weeks

**Rating:** 4.7/96%

This course is a beginner level in English. No mathematical background is required to register for this course but will help students looking to enhance their algebra and geometry where future course will require either precalculus or calculus as a prerequisite. Quantitative skills and reasoning train you to think logically, to reason with data, and to make informed decisions. In stead of a Capstone project there are smaller applied and algebra problems throughout the modules and you will be provided with both practice problems and worked solutions to prepare you for success. Course 1 covers Algebra: Elementary to Advanced - Equations and Inequalities, Course 2 is Algebra: Elementary to Advanced - Functions and Applications and Course 3 is Algebra: Elementary to Advanced - Polynomials and Routes. **Skills Acquired: **Algebra, Equations, Functions, Polynomials, and Routes. **Duration:** Approx 4 months at a suggested pace of 4 hours per week - 3 courses. **Rating:** 4.8

Calculus Single Variable Part 2

This course is in English with subtitles in 5 different languages. Calculus explains everything from periodicity of a heartbeat to the optimal size of a city, and this course covers single variable Calculus with the emphasis on conceptual understanding and their application. If you are studying in the engineering physical or social sciences this course will appeal to you. This 2^{nd} part of 5 covers Derivatives, Differentiation, Rules, Linearisation, Higher Derivatives, Optimization, Differentials, and Differentiation Operators. In Week 1 you will learn A New Look at Differentiation, Week 2 is Putting Derivatives to work and Week 3 looks at Differentials and Operators. **Skills Acquired:** Differential Mathematics, Newton’s Method, Linear Approximation, Differential Calculus, and Derivative . **Duration:** Approx 10 hours over 3 weeks. **Rating:** 4.8/95%

Introduction to Mathematical Thinking

This course is an intermediate level in English with subtitles in 9 different languages. The way that mathematicians think has been developed over many millennia and is a powerful cognitive process which is not the same thing as doing mathematics. Typically presented mathematics in the school system falls way short and focuses on learning procedures to solve stereo typed problems. Professional mathematicians think in a specific way to solve problems arising from science, the world, or just from mathematics itself. A key contrast between success in school maths as opposed to mathematical thinking is that mathematical thinking is thinking outside-the-box. This course will develop the way of thinking. The 9 week course covers: Mathematics and topics that look easier than they are, so take your time and study the material thoroughly. Expect to spend more time going through the lectures sufficiently well to comprehend the material. **Skills Acquired:** Number Theory, Real Analysis, Mathematical Logic, and Language. **Duration: **Approx 39 hours over 9 weeks. **Rating:** 4.8/97%

Introduction to Complex Analysis

This course is an intermediate level in English and has subtitles in 8 different languages. Complex analysis is the theory of complex functions of a complex variable, and this course will introduce you to complex analysis. You w ll be introduced initially to the complex plane together with the algebra and geometry of complex numbers and then through differentiation integration, complex dynamics, power series representation, and Laurent series into current areas. The modules each comprise of 5 video lecturers with quizzes, followed by a homework assignment. For the homework assignments you may possibly require extra time to ingest and practice the concepts which are discussed in the lectures. Most of the learning will happen completing the assignments which are not intended to be taken lightly or completed quickly so the expectation is that the course will take 6-12 hours per module depending upon your previous experience. The syllabus covers: Introduction to Complex Numbers; Complex functions and iteration; Analytic Functions; Conformal Mappings; Complex integration; Power Series; Laurent Series and Residue Theorem and a final exam.

**Skills Acquired:** Conformal Mapping, Laurent Series, Power Series, Complex Analysis, and Complex Numbers. **Duration: **27 hours over 8 weeks. **Rating:** 4.8/97%

This course in an intermediate level in English with subtitles in 8 different languages. The focus of this course is to address the most important pillars for application for mathematics and science engineering and commerce. It will explore the ideas and historical motivation for calculus while balancing between theory and application, so leading to mastery in foundational mathematics. You

Will become familiar with precalculus including equation manipulation and elementary functions, become fluent with tangents and limits and derivative. You will build and practice differential calculus with applications and with integral calculus. The course covers: Week 1 - Precalculus; Week 2 - Functions (Useful and Important Repertoire); Week 3 - Introduction to Differential Calculus; Week 4 - Properties and Application of the derivative); Week 5 - Introducing the Integral Calculus

.**Skills Acquired:** Logic, Mathematics, and Calculus. **Duration: **Approx 59 hours over 5 weeks

**Rating:** 4.8/98%

This course is in English and is a beginner level. This course will teach you concepts of AI such as machine learning, deep learning, and Vectors which is all related to Linear algebra. You will learn the use of Linear algebra in the AI algorithm and on completing the course you will fully understand AI algorithms and the basics of Linear algebra in the AI applications. The syllabus covers: Introduction to AI in Week 1, Week 2 looks at the Introduction of Linear Algebra, Week 3 covers Low Operation and Linear combination, Week 4 studies Linearly Independent and Inverse Matrix, Week 5 explores Determinant of Square Matrix and Eigenvalue Problem and finally in Week 6 you will learn Diagonalization Problem and AI Application. **Skills Acquired:** Linear Algebra, Inverse Matrix,Eigenvalue, and AI. **Duration:** Approx 7 hours over 6 weeks. **Rating:** 4.9

This course is a a beginner level in English with subtitles in 8 different languages. This course teaches everything you need to know about Matrices and covers the Linear algebra an engineer should know.

This course should be taken after completing a university level single variable calculus course. Derivatives and integrals are not covered in the course but it is expected that you have some knowledge of them. The course contains meany short lecture videos followed by problems to solve which will reinforce the subjects covered in the lecture videos. After each topic there will be a short quiz and solutions can be found in the lecture notes which can be downloaded at http://www.math.ust.hk/~machas/matrix-algebra-for-engineers.pdf

Week 1 covers Matrices, Week2 covering Systems of Linear Equations, Week 3 is covering Vector Spaces and finally in Week 4 you will cover Eigenvalues and Eigenvectors. **Skills Acquired: **Matrices, Victor Spaces, Eigenvalues, and Eigenvector. **Duration: **Approx 20 hours over 4 weeks. **Rating:** 4.9/97%

Algebra: Elementary to Advanced

The course is in English and is a beginner level course. This is course 1 of 3 in the Algebra: Elementary to Advanced Specialization. If you are seeking a good base in mathematical concepts from which to take advanced courses using concepts from precalculus, Calculus, Probability, and Statistics then this is where you need to begin. You will consolidate your computational methods, review Algebraic formulas and properties and apply these concepts to actual everyday situations. The course topics include many of the basic Algebraic concepts which can be seen in the skills set below. Module 1 covers The Structure of Numbers; Module 2 covers Linear Equations; Module 3 covers Solving Inequalities; Module 4 delves into Systems of Equations; and finally in Week 5 is an exam on Equations with Inequalities and Real Numbers. **Skills Acquired:** Equalities, Inequalities, Polynomials, Radicals, Exponents, and Quadratic Equations. **Duration**: Approx 10 hours over 5 weeks. **Rating:** 4.9/95%

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