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所在平台: Udemy |
课程主页: https://www.udemy.com/course/probability-statistics-mathematics/
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**课程名称：** 完备机器学习数学、统计与概率课程 **课程概述：** 本课程是一门极其全面的数学、概率和统计学课程，专为机器学习、商业分析、数据科学和数据分析领域学习者设计。课程内容涵盖了从集合论、排列组合，到概率论、统计推断、线性代数和微积分等关键知识点。通过大量的挑战和详细的解决方案，帮助学习者深入理解这些现代科学的基础，从而解决实际业务和科学预测问题。 **核心学习内容包括：** * **集合论：** 集合、子集、集合关系、集合运算、集合定律、维恩图等。 * **排列组合与概率：** 阶乘、排列、组合、理论概率、经验概率、概率加法法则、乘法法则、独立与非独立事件。 * **随机变量与统计：** 离散与连续变量、Z-Score、频率分布、均值（含加权均值）、中位数、众数、方差、标准差。 * **变量类型与相关性：** 依变量、自变量、控制变量、有序变量等；相关性分析（Pearson、Spearman）、回归与共线性。 * **回归误差度量：** MSE、MAE、RMSE、R-Squared、Adjusted R-Squared。 * **条件概率与分布：** 条件概率、贝叶斯定理；二项分布、泊松分布、正态分布、T分布。 * **数据分布特征：** 偏度和峰度。 * **概率决策树。** * **线性代数：** 矩阵（加减乘法、转置、方阵、特殊矩阵）、行列式、伴随矩阵、逆矩阵、克莱默法则、特征值与特征向量。 * **距离度量：** 欧氏距离与曼哈顿距离。 * **微积分：** 导数（原理、公式、链式法则、乘积法则、高阶导数）、积分（不定积分、定积分）、曲线下面积计算。 **课程特色：** * **内容全面：** 覆盖机器学习所需的所有核心数学和统计概念。 * **实践导向：** 强调理论与实践结合，提供大量练习题和解决方案。 * **支持服务：** 提供问答环节和直接消息功能，方便学习者沟通交流。 * **证书认可：** 完成课程可获得completion certificate，可在LinkedIn等平台展示。 * **无风险学习：** 提供30天无条件退款保证。 **目标受众：** * 机器学习新手。 * 希望提升职业技能的各领域人士。 * 数据科学、数据分析、金融、保险等行业专业人士。 **总结：** 本课程是掌握商业分析、数据科学、人工智能、机器学习和深度学习核心数学、概率与统计知识的理想选择。无论您是初学者还是希望深入学习的专业人士，都能从中获益。
Start learning Mathematics, Probability & Statistics for Machine Learning TODAY!Hi,You are welcome to this course: Complete Math, Probability & Statistics for Machine learning. This is a highly comprehensive Mathematics, Statistics, and Probability course, you learn everything from Set theory, Combinatorics, Probability, statistics, and linear algebra to Calculus with tons of challenges and solutions for Business Analytics, Data Science, Data Analytics, and Machine Learning. Mathematics, Probability & Statistics are the bedrock of modern science such as machine learning, predictive risk management, inferential statistics, and business decisions. Understanding the depth of these will empower you to solve numerous day-to-day business and scientific prediction problems and analytical problems. This course includes but is not limited to:"SetsUniversal SetProper and Improper SubsetSuper Set and Singleton SetNull or Empty SetPower SetEqual and Equivalent SetSet Builder NotationsCardinality of SetSet OperationsLaws of SetsFinite and Infinite SetNumber SetsVenn DiagramUnion, Intersection, and Complement of SetFactorialPermutationsCombinationsTheoretical ProbabilityEmpirical ProbabilityAddition Rules of ProbabilityMutual and Non-mutual ExclusiveMultiplication Rules of ProbabilityDependent and Independent EventsRandom VariableDiscrete and Continuous VariableZ-ScoreFrequency and TallyPopulation and SampleRaw Data and ArrayMeanIntroductionWeighted MeanProperties of MeanBasic Properties of MeanMean Frequency DistributionMedianMedian Frequency DistributionModeMeasurement of SpreadMeasures of Spread (Variation / Dispersion)RangeMean DeviationMean Deviation for Frequency DistributionVariance & Standard DeviationUnderstanding Variance and Standard DeviationBasic Properties of Variance and Standard DeviationVariable Dependent- Independent - Moderating - Ordinal...VariableTypes of VariableDependent, Independent, Control Moderating and Mediating VariablesCorrelationRegression & CollinearityCollinearityPearson and Spearman Correlation MethodsUnderstanding Pearson and Spearman correlationSpearman FormulaPearson FormulaRegression Error MetricsUnderstanding Regression Error MetricsMean Squared ErrorMean Absolute ErrorRoot Mean Squared ErrorR-Squared or Coefficient of DeterminationAdjusted R-SquaredSummary on Regression Error MetricsConditional ProbabilityBayes TheoremBinomial DistributionPoisson DistributionNormal DistributionSkewness and KurtisosT - DistributionDecision Tree of ProbabilityLinear Algebra - MatricesIndices and LogarithmsIntroduction to MatrixAddition and Subtraction - MatricesMultiplication - MatriceSquare of MatrixTranspose of MatrixSpecial MatrixDeterminant of MatrixDeterminant of Singular Matrix - ExampleCofactorMinorPlace SignAdjoint of a Square MatrixInverse of MatrixThe inverse of Matrix - ExampleMatrix for Simultaneous Equation - Exercise & Solution 10Cramer's RuleCramer's Rule ExampleEigenvalues and EigenvectorsEuclidean Distance and Manhattan DistanceDifferentiationImportance of Calculus for Machine LearningThe gradient of a Straight LineThe gradient of a Curve to Understanding DifferentiationDerivatives By First PrincipleDerived Definition Form of First PrincipleGeneral FormulaSecond DerivativesUnderstanding Second DerivativesSpecial DerivativesUnderstanding Special DerivativesDifferentiation Using Chain RuleUnderstanding Chain RuleDifferentiation Using Product RuleUnderstanding Product RuleDifferentiation Using Chain and Product RulesCalculus - Indefinite Integrals ICalculus - Indefinite Integrals IICalculus - Definite Integrals ICalculus - Definite Integrals IICalculus - Area Under Curve - Using IntegrationYou will also have access to the Q & A section where you contact post questions. You can also send me a direct message.Upon the completion of this course, you'll receive a certificate of completion which you can post on your LinkedIn account for our colleagues and potential employers to view! All these come with a 30-day money-back guarantee. so you can try out the course risk-free!Who is this course for:Those starting from scratch in Machine LearningThose who wish to take their career to the next levelProfessional in the field of Data ScienceProfessionals in the banking industryProfessionals in the insurance industryMaster the core Mathematics, Probability & Statistics for Business Analytics, Data Science, AI, Machine & Deep Learning!
