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| Linear Algebra (Matrices): Learn about matrix properties, multiplying matrices, LU decomposition, and determinants. This is needed for data analysis, processing, and techniques like PCA (Principal Component Analysis) [08:54]. | |
| Probability and Statistics: Learn about random variables, probability distributions, expectation value, variance, covariance, correlation, and Bayes' Rule. This is essential for understanding your data and model results. | |
| Numerical Computation: Learn about Gradient Descent, which is used to find a local minimum. The speaker suggests writing code for gradient descent. | |
| Calculus Basics: Learn the Chain Rule, which is at the heart of backpropagation. | |
| Theory of Machine Learning: Learn key terminologies and concepts like regression, train/test/validation sets, labels/targets, weights, generalization error, regularization, hyperparameter tuning (using cross-validation), and bias-variance tradeoff. | |
| https://www.deeplearningbook.org/exercises.html | |
| ----- | |
| > High School Math (A-Level) : | |
| 1 - Arithmetic, 2 - Algebra, 3 - Geometry & (Analytic Geometry), 4 - Trigonometry - ( Equations, Inequalities, Functions, Graphs, etc ). | |
| > College Math : | |
| 5 - Discrete Math - ( Logic, Sets, Number Theory, Proofs, Sequences, Relations, Counting, Graphs, etc ). | |
| 6 - Linear Algebra - ( Linear Systems, Vector Spaces, Linear Maps, Matrices, Determinants, etc ). | |
| 7 - Single-Variable Calculus I & II - ( Limits, Differentials, Integrals, Infinite Series, Differential Equations, etc ). | |
| 8 - Multi-Variable Calculus III - ( Vector Calculus, Partial Differentiations, Multi Integrals, Partial Differential Equations, etc ). | |
| 9 - Probability & Statistics - (Random Variables, Distributions) & (Inference, Regression). | |
| 10 - Numerical Analysis, Optimization & Mathematical Models. | |
| Also use Workbooks to do lots of exercises, example from: | |
| - Schaum's outlines series. | |
| - Essential skills Chris McMullen series. | |
| - Student Solutions, and websites for each textbook. | |
| And others. | |
| Math Textbooks: | |
| > High School Math (A-Level) : | |
| 1 - Arithmetic, 2 - Algebra, 3 - Geometry & (Analytic Geometry), 4 - Trigonometry. | |
| - Beginning & Intermediate Algebra 6e - Karr 2011 | |
| - Elementary & Intermediate Algebra 5e - Tussy 2013 | |
| - A Graphical approach to Precalculus with Limits 7e - Hornsby, Lial 2018 | |
| - Precalculus 5e - Robert Blitzer 2014 | |
| > College Math : | |
| 5 - Discrete Math. | |
| - Discrete Math with Applications 5e (Metric) - Sussana Epp 2019 | |
| - Discrete Mathematics and it’s Applications 8e - Kenneth Rosen 2018 | |
| - Elements of Discrete Mathematics - Richard Hammack 2017 (free) | |
| - Discrete Mathematical Structures for CS and it’s Applications 7e - Judith Gersting 2014 | |
| 6 - Linear Algebra. | |
| - Elementary Linear Algebra w Applications EMEA 12e - Anton, Rorres 2020 | |
| - Linear Algebra - Cherney, Denton 2013 (free) | |
| - Introduction to Linear Algebra 6e - Gilbert Strang 2023 | |
| 7 + 8 - Single/Multi-Variable Calculus I, II & III. | |
| - Calculus 9e - Larson, Edwards 2010 | |
| - Calculus 8e - James Stewart 2016 | |
| - Calculus 9e - Adams, Essex 2018 | |
| - Thomas Calculus Transcendentals - Hass 2022 | |
| - Differential Equations with Boundary-Value Problems 7e - Zill, Cullen 2009 | |
| - Fundamentals of Differential Equations & boundary value problems 7e - Nagle 2017 | |
| - Partial Differential Equations, Boundary Value Problems 2e - Asmar 2005 | |
| 9 - Probability & Statistics. | |
| - Introduction Probability & Statistics (Metric) 15e - Mendenhall 2020 -> (basic concepts) | |
| - Probability & Statistics for Engineering and the Sciences 9e - Devore 2016 | |
| - Probability & Statistics for Computer Scientists 3e (R, Matlab) - Michael Baron 2019 | |
| - Mathematical Statistics with Applications 7e - Wackerly, Mendenhall 2008 | |
| 10 - Numerical Analysis, Optimization, & Mathematical Models. | |
| (Math workbooks) | |
| - Engineering Mathematics + Advanced Engineering Mathematics - Stroud, Booth 2020 | |
| - Advanced Engineering Mathematics 10e - Kreyszig 2018 | |
| Conclusion : | |
| Its might be enough to choose 1 or 2 textbooks and 1 workbook for each Math topic. | |
| Example for 1 - Arithmetic, 2 - Algebra, 3 - Geometry & (Analytic Geometry), 4 - Trigonometry: | |
| - Beginning & Intermediate Algebra 6e - Karr 2011 | |
| - A Graphical approach to Precalculus with Limits 7e - Hornsby, Lial 2018 | |
| - Trigonometry: Essential Skills - Chris McMullen 2015 | |
| - Schaum's outline of College Algebra 5e - Spiegel, Moyer 2018 | |
| Example for 6 - Linear Algebra: | |
| - Elementary Linear Algebra w Applications EMEA 12e - Anton, Rorres 2020 | |
| - Schaum's outline of Linear Algebra 6e - Lipschutz, Lipson 2018 | |
| Example for 9 - Probability & Statistics: (it is advisable to do the (basic concepts) textbook first). | |
| - Introduction Probability & Statistics (Metric) 15e - Mendenhall 2020 -> (basic concepts) | |
| - Probability & Statistics for Computer Scientists 3e (R, Matlab) - Micheal Baron 2019 | |
| - Schaum's outlines of Probability & Statistics 4e - Spiegel, Schiller 2012 | |
| Also some Youtube and other online Math tutorials, for visual Math clarity and lectures to ease understanding. | |
| 1 Visuals & Pro channels: | |
| - 3Blue1Brown, Numberphile, Mathologer, PBSInfiniteSeries, KhanAcademy, DennisDavis, freeCodeCamp, etc. | |
| 2 Instructional Lectures: | |
| - ProfessorLeonard, EddieWoo, MathDoctorRob, ProfRobBob, ThinkwellVids, MywhyU, etc | |
| 3 Tutoring & Homework: | |
| - PatrickJMT, HoustonMathPrep, Mario'sMathTutoring, BrianMcLogan, Vavmath, NancyPi, KristaKing, MySecretMathTutor, MichealPenn, TheOrganicChemistryTutor, YayMath, Math & Science, JohnHush, TheMathSorcerer, etc. | |
| 4 Problem Solving: | |
| - blackpenredpen, ArtOfProblemSolving, MindYourDecisions, etc. | |
| 5 Micellaneous: | |
| - standupmaths, ThinkTwice, FlammableMaths, vcubingy, TippingPointMath, etc. | |
| Also Which Math to study ? | |
| Pure Math vs Discrete Math vs Applied Math ? : | |
| > Pure Math: focuses on the (Analysis) of the abstract and the theoretical concepts using proof theorems and research, to make for new Math discoveries. | |
| It is generally more difficult than Applied Math. | |
| And It is mainly for students pursuing Mathematics Degrees. | |
| With topics Like: | |
| - Set Theory, Abstract Algebra, Number Theory, Analysis, Topology, etc. | |
| > Discrete Math: focuses on the (Concepts & Structures) used to verify by proofs, discrete objects and their relationships in Digital Electronics/Computer Engineering, Computer Science, Software Programming/Engineering, etc. | |
| Like: | |
| - Logic, Graphs, Sets, Functions, Relations, Permutations and Finite-State Machines. | |
| > Applied Math: focuses on (Applying) computational Mathematical Modelling methods, also with Numerical Analysis & Methods, and Optimization, to solve real-world problems in many fields, example in the Applied Sciences, Engineering, and Economics etc. | |
| Like: | |
| - Physics, Chemistry, Biology, Statistics, Mechanical, Civil, Electrical, Medicine, Finance etc | |
| ----- | |
| 1. Mathematics "All of it" Youtube playlist by Prof. Dave Explains | |
| 2. The Business Case for AI by Kavita Ganesan | |
| 3. Mathematics-Basics to Advanced for Data Science And GenAI Udemy Course | |
| 4. Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2025] Udemy Course | |
| 5. Hands-On | |
| Machine Learning with Scikit-Learn, | |
| Keras & TensorFlow by Aurélien Géron |
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