Pearson The Essential Machine Learning Foundations Math Probability Statistics And Computer Science

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00000 Introduction.mp4

02:54

00001 Topics.mp4

00:33

00002 1.1 Defining Linear Algebra.mp4

06:35

00003 1.2 Solving a System of Equations Algebraically.mp4

06:42

00004 1.3 Linear Algebra in Machine Learning and Deep Learning.mp4

10:51

00005 1.4 Historical and Contemporary Applications.mp4

07:36

00006 1.5 Exercise.mp4

08:49

00007 Topics.mp4

00:37

00008 2.1 Tensors.mp4

04:35

00009 2.2 Scalars.mp4

23:03

00010 2.3 Vectors and Vector Transposition.mp4

11:15

00011 2.4 Norms and Unit Vectors.mp4

15:35

00012 2.5 Basis Orthogonal and Orthonormal Vectors.mp4

04:47

00013 2.6 Matrices.mp4

07:39

00014 2.7 Generic Tensor Notation.mp4

04:46

00015 2.8 Exercises.mp4

02:11

00016 Topics.mp4

00:21

00017 3.1 Tensor Transposition.mp4

03:37

00018 3.2 Basic Tensor Arithmetic.mp4

05:56

00019 3.3 Reduction.mp4

04:39

00020 3.4 The Dot Product.mp4

06:04

00021 3.5 Exercises.mp4

05:38

00022 Topics.mp4

00:24

00023 4.1 The Substitution Strategy.mp4

03:40

00024 4.2 Substitution Exercises.mp4

08:41

00025 4.3 The Elimination Strategy.mp4

04:10

00026 4.4 Elimination Exercises.mp4

09:49

00027 Topics.mp4

00:40

00028 5.1 Matrix-by-Vector Multiplication.mp4

12:01

00029 5.2 Matrix-by-Matrix Multiplication.mp4

09:44

00030 5.3 Symmetric and Identity Matrices.mp4

05:13

00031 5.4 Exercises.mp4

08:31

00032 5.5 Machine Learning and Deep Learning Applications.mp4

11:45

00033 Topics.mp4

00:24

00034 6.1 The Frobenius Norm.mp4

03:39

00035 6.2 Matrix Inversion.mp4

16:15

00036 6.3 Diagonal Matrices.mp4

03:41

00037 6.4 Orthogonal Matrices.mp4

04:23

00038 6.5 The Trace Operator.mp4

03:45

00039 Topics.mp4

00:26

00040 7.1 The Eigenconcept.mp4

09:00

00041 7.2 Exercises.mp4

09:29

00042 7.3 Eigenvectors in Python.mp4

28:00

00043 7.4 High-Dimensional Eigenvectors.mp4

03:54

00044 Topics.mp4

00:38

00045 8.1 The Determinant of a 2 x 2 Matrix.mp4

06:02

00046 8.2 The Determinants of Larger Matrices.mp4

07:18

00047 8.3 Exercises.mp4

03:59

00048 8.4 Determinants and Eigenvalues.mp4

08:43

00049 8.5 Eigendecomposition.mp4

14:01

00050 Topics.mp4

00:43

00051 9.1 Singular Value Decomposition.mp4

08:09

00052 9.2 Media File Compression.mp4

06:57

00053 9.3 The Moore-Penrose Pseudoinverse.mp4

08:44

00054 9.4 Regression via Pseudoinversion.mp4

12:47

00055 9.5 Principal Component Analysis.mp4

06:57

00056 9.6 Resources for Further Study of Linear Algebra.mp4

03:16

00057 Linear Algebra for Machine Learning Machine Learning Foundations - Summary.mp4

01:11

00058 Introduction.mp4

02:46

00059 Topics.mp4

00:31

00060 1.1 Differential versus Integral Calculus.mp4

16:42

00061 1.2 A Brief History.mp4

07:22

00062 1.3 Calculus of the Infinitesimals.mp4

13:04

00063 1.4 Modern Applications.mp4

10:17

00064 Topics.mp4

00:35

00065 2.1 Continuous versus Discontinuous Functions.mp4

09:20

00066 2.2 Solving via Factoring.mp4

01:37

00067 2.3 Solving via Approaching.mp4

03:17

00068 2.4 Approaching Infinity.mp4

04:59

00069 2.5 Exercises.mp4

06:02

00070 Topics.mp4

00:37

00071 3.1 Delta Method.mp4

11:13

00072 3.2 The Most Common Representation.mp4

10:28

00073 3.3 Derivative Notation.mp4

04:12

00074 3.4 Constants.mp4

01:37

00075 3.5 Power Rule.mp4

01:33

00076 3.6 Constant Product Rule.mp4

03:17

00077 3.7 Sum Rule.mp4

02:08

00078 3.8 Exercises.mp4

08:54

00079 Topics.mp4

00:28

00080 4.1 Product Rule.mp4

03:33

00081 4.2 Quotient Rule.mp4

03:43

00082 4.3 Chain Rule.mp4

05:22

00083 4.4 Exercises.mp4

09:07

00084 4.5 Power Rule on a Function Chain.mp4

03:28

00085 Topics.mp4

00:31

00086 5.1 Introduction.mp4

03:41

00087 5.2 Autodiff with PyTorch.mp4

04:33

00088 5.3 Autodiff with TensorFlow.mp4

02:35

00089 5.4 Directed Acyclic Graph of a Line Equation.mp4

15:44

00090 5.5 Fitting a Line with Machine Learning.mp4

25:00

00091 Topics.mp4

00:33

00092 6.1 Derivatives of Multivariate Functions.mp4

19:20

00093 6.2 Partial Derivative Exercises.mp4

09:14

00094 6.3 Geometrical Examples.mp4

09:23

00095 6.4 Geometrical Exercises.mp4

06:18

00096 6.5 Notation.mp4

01:38

00097 6.6 Chain Rule.mp4

06:25

00098 6.7 Chain Rule Exercises.mp4

04:25

00099 Topics.mp4

00:47

00100 7.1 Single-Point Regression.mp4

12:15

00101 7.2 Partial Derivatives of Quadratic Cost.mp4

11:00

00102 7.3 Descending the Gradient of Cost.mp4

06:24

00103 7.4 Gradient of Mean Squared Error.mp4

20:58

00104 7.5 Backpropagation.mp4

03:39

00105 7.6 Higher-Order Partial Derivatives.mp4

08:37

00106 7.7 Exercise.mp4

02:36

00107 Topics.mp4

01:00

00108 8.1 Binary Classification.mp4

06:19

00109 8.2 The Confusion Matrix and ROC Curve.mp4

12:34

00110 8.3 Indefinite Integrals.mp4

11:05

00111 8.4 Definite Integrals.mp4

06:23

00112 8.5 Numeric Integration with Python.mp4

03:33

00113 8.6 Exercises.mp4

05:41

00114 8.7 Finding the Area Under the ROC Curve.mp4

02:13

00115 8.8 Resources for Further Study of Calculus.mp4

02:14

00116 Calculus for Machine Learning - Summary.mp4

01:02

00117 Introduction.mp4

03:21

00118 Topics.mp4

00:37

00119 1.1 Orientation to the Machine Learning Foundations Series.mp4

02:48

00120 1.2 What Probability Theory Is.mp4

07:04

00121 1.3 Events and Sample Spaces.mp4

04:56

00122 1.4 Multiple Observations.mp4

05:58

00123 1.5 Factorials and Combinatorics.mp4

04:49

00124 1.6 Exercises.mp4

08:52

00125 1.7 The Law of Large Numbers and the Gambler s Fallacy.mp4

11:17

00126 1.8 Probability Distributions in Statistics.mp4

05:23

00127 1.9 Bayesian versus Frequentist Statistics.mp4

08:43

00128 1.10 Applications of Probability to Machine Learning.mp4

08:47

00129 Topics.mp4

00:35

00130 2.1 Discrete and Continuous Variables.mp4

04:51

00131 2.2 Probability Mass Functions.mp4

05:56

00132 2.3 Probability Density Functions.mp4

04:09

00133 2.4 Exercises on Probability Functions.mp4

02:30

00134 2.5 Expected Value.mp4

06:21

00135 2.6 Exercises on Expected Value.mp4

04:26

00136 Topics.mp4

00:22

00137 3.1 The Mean a Measure of Central Tendency.mp4

07:19

00138 3.2 Medians.mp4

02:56

00139 3.3 Modes.mp4

06:22

00140 3.4 Quantiles - Percentiles Quartiles and Deciles.mp4

07:44

00141 3.5 Box-and-Whisker Plots.mp4

11:56

00142 3.6 Variance a Measure of Dispersion.mp4

07:45

00143 3.7 Standard Deviation.mp4

03:21

00144 3.8 Standard Error.mp4

04:53

00145 3.9 Covariance a Measure of Relatedness.mp4

12:43

00146 3.10. Correlation.mp4

07:45

00147 Topics.mp4

00:21

00148 4.1 Joint Probability Distribution.mp4

03:56

00149 4.2 Marginal Probability.mp4

05:11

00150 4.3 Conditional Probability.mp4

06:14

00151 4.4 Exercises.mp4

05:38

00152 4.5 Chain Rule of Probabilities.mp4

03:35

00153 4.6 Independent Random Variables.mp4

03:24

00154 4.7 Conditional Independence.mp4

05:01

00155 Topics.mp4

00:42

00156 5.1 Uniform.mp4

04:14

00157 5.2 Gaussian - Normal and Standard Normal.mp4

08:41

00158 5.3 The Central Limit Theorem.mp4

15:28

00159 5.4 Log-Normal.mp4

02:29

00160 5.5 Exponential and Laplace.mp4

04:45

00161 5.6 Binomial and Multinomial.mp4

08:06

00162 5.7 Poisson.mp4

03:36

00163 5.8 Mixture Distributions.mp4

03:29

00164 5.9 Preprocessing Data for Model Input.mp4

03:21

00165 5.10 Exercises.mp4

02:48

00166 Topics.mp4

00:28

00167 6.1 What Information Theory Is.mp4

01:11

00168 6.2 Self-Information Nats and Bits.mp4

06:20

00169 6.3 Shannon and Differential Entropy.mp4

07:10

00170 6.4 Kullback-Leibler Divergence and Cross-Entropy.mp4

05:29

00171 Topics.mp4

00:35

00172 7.1 Applications of Statistics to Machine Learning.mp4

09:23

00173 7.2 Review of Essential Probability Theory.mp4

12:08

00174 7.3 z-scores and Outliers.mp4

10:06

00175 7.4 Exercises on z-scores.mp4

03:58

00176 7.5 p-values.mp4

13:32

00177 7.6 Exercises on p-values.mp4

06:38

00178 Topics.mp4

00:35

00179 8.1 Single-Sample t-tests and Degrees of Freedom.mp4

10:36

00180 8.2 Independent t-tests.mp4

12:57

00181 8.3 Paired t-tests.mp4

13:30

00182 8.4 Applications to Machine Learning.mp4

04:13

00183 8.5 Exercises.mp4

07:55

00184 8.6 Confidence Intervals.mp4

10:27

00185 8.7 ANOVA - Analysis of Variance.mp4

03:09

00186 Topics.mp4

00:31

00187 9.1 The Pearson Correlation Coefficient.mp4

06:58

00188 9.2 R-squared Coefficient of Determination.mp4

02:55

00189 9.4 Correcting for Multiple Comparisons.mp4

03:36

00190 Topics.mp4

00:45

00191 10.1 Independent versus Dependent Variables.mp4

05:23

00192 10.2 Linear Regression to Predict Continuous Values.mp4

07:16

00193 10.3 Fitting a Line to Points on a Cartesian Plane.mp4

15:45

00194 10.4 Linear Least Squares Exercise.mp4

05:09

00195 10.5 Ordinary Least Squares.mp4

13:09

00196 10.6 Categorical Dummy Features.mp4

17:45

00197 10.7 Logistic Regression to Predict Categories.mp4

13:23

00198 10.8 Open-Ended Exercises.mp4

03:58

00199 Topics.mp4

00:36

00200 11.1 Machine Learning versus Frequentist Statistics.mp4

07:03

00201 11.2 When to Use Bayesian Statistics.mp4

02:24

00202 11.3 Prior Probabilities.mp4

05:28

00203 11.4 Bayes Theorem.mp4

12:46

00204 11.5 Resources for Further Study of Probability and Statistics.mp4

02:03

00205 Probability and Statistics for Machine Learning - Summary.mp4

01:03

00206 Introduction.mp4

02:55

00207 Topics.mp4

00:27

00208 1.1 Orientation to the Machine Learning Foundations Series.mp4

03:06

00209 1.2 A Brief History of Data.mp4

06:51

00210 1.3 A Brief History of Algorithms.mp4

05:04

00211 1.4 Applications to Machine Learning.mp4

03:18

00212 Topics.mp4

00:35

00213 2.1 Introduction.mp4

08:14

00214 2.2 Constant Time.mp4

09:26

00215 2.3 Linear Time.mp4

07:57

00216 2.4 Polynomial Time.mp4

05:26

00217 2.5 Common Runtimes.mp4

06:47

00218 2.6 Best versus Worst Case.mp4

09:23

00219 Topics.mp4

00:19

00220 3.1 Lists.mp4

03:28

00221 3.2 Arrays.mp4

08:55

00222 3.3 Linked Lists.mp4

03:03

00223 3.4 Doubly-Linked Lists.mp4

01:24

00224 3.5 Stacks.mp4

04:16

00225 3.6 Queues.mp4

01:48

00226 3.7 Deques.mp4

05:19

00227 Topics.mp4

00:24

00228 4.1 Binary Search.mp4

17:40

00229 4.2 Bubble Sort.mp4

09:20

00230 4.3 Merge Sort.mp4

16:14

00231 4.4 Quick Sort.mp4

15:05

00232 Topics.mp4

00:30

00233 5.1 Maps and Dictionaries.mp4

04:32

00234 5.2 Sets.mp4

03:57

00235 5.3 Hash Functions.mp4

06:50

00236 5.4 Collisions.mp4

05:13

00237 5.5 Load Factor.mp4

02:17

00238 5.6 Hash Maps.mp4

02:50

00239 5.7 String Keys.mp4

03:17

00240 5.8 Hashing in ML.mp4

02:13

00241 Topics.mp4

00:22

00242 6.1 Introduction.mp4

03:37

00243 6.2 Decision Trees.mp4

15:58

00244 6.3 Random Forests.mp4

08:05

00245 6.4 XGBoost - Gradient-Boosted Trees.mp4

09:57

00246 6.5 Additional Concepts.mp4

02:49

00247 Topics.mp4

00:28

00248 7.1 Introduction.mp4

02:58

00249 7.2 Directed versus Undirected Graphs.mp4

01:43

00250 7.3 DAGs - Directed Acyclic Graphs.mp4

10:33

00251 7.4 Additional Concepts.mp4

01:26

00252 7.5 Bonus - Pandas DataFrames.mp4

01:50

00253 7.6 Resources for Further Study of DSA.mp4

01:41

00254 Topics.mp4

00:43

00255 8.1 Statistics versus Machine Learning.mp4

12:28

00256 8.2 Objective Functions.mp4

07:56

00257 8.3 Mean Absolute Error.mp4

02:08

00258 8.4 Mean Squared Error.mp4

08:01

00259 8.5 Minimizing Cost with Gradient Descent.mp4

06:59

00260 8.6 Gradient Descent from Scratch with PyTorch.mp4

25:58

00261 8.7 Critical Points.mp4

08:46

00262 8.8 Stochastic Gradient Descent.mp4

16:44

00263 8.9 Learning Rate Scheduling.mp4

12:47

00264 8.10 Maximizing Reward with Gradient Ascent.mp4

02:55

00265 Topics.mp4

00:36

00266 9.1 Jacobian Matrices.mp4

07:06

00267 9.2 Second-Order Optimization and Hessians.mp4

05:26

00268 9.3 Momentum.mp4

02:38

00269 9.4 Adaptive Optimizers.mp4

07:19

00270 9.5 Congratulations and Next Steps.mp4

08:26

00271 Data Structures Algorithms and Machine Learning Optimization - Summary.mp4

00:52

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