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种子名称： [FreeAllCourse.Com] Udemy - The Data Science Course 2020 Complete Data Science Bootcamp
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文件大小： 15.17 GB
收录时间： 2021-11-10 11:50
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最近下载： 2024-11-19 02:41

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[FreeAllCourse.Com] Udemy - The Data Science Course 2020 Complete Data Science Bootcamp.torrent

01 Part 1 Introduction/001 A Practical Example What You Will Learn in This Course.mp449.03MB

01 Part 1 Introduction/002 What Does the Course Cover.mp462.25MB

02 The Field of Data Science - The Various Data Science Disciplines/004 Data Science and Business Buzzwords Why are there so Many.mp481.41MB

02 The Field of Data Science - The Various Data Science Disciplines/005 What is the difference between Analysis and Analytics.mp453.55MB

02 The Field of Data Science - The Various Data Science Disciplines/006 Business Analytics Data Analytics and Data Science An Introduction.mp464.51MB

02 The Field of Data Science - The Various Data Science Disciplines/007 Continuing with BI ML and AI.mp4108.98MB

02 The Field of Data Science - The Various Data Science Disciplines/008 A Breakdown of our Data Science Infographic.mp467.74MB

03 The Field of Data Science - Connecting the Data Science Disciplines/009 Applying Traditional Data Big Data BI Traditional Data Science and ML.mp4126.87MB

04 The Field of Data Science - The Benefits of Each Discipline/010 The Reason Behind These Disciplines.mp481.18MB

05 The Field of Data Science - Popular Data Science Techniques/011 Techniques for Working with Traditional Data.mp4138.3MB

05 The Field of Data Science - Popular Data Science Techniques/012 Real Life Examples of Traditional Data.mp429.93MB

05 The Field of Data Science - Popular Data Science Techniques/013 Techniques for Working with Big Data.mp475.5MB

05 The Field of Data Science - Popular Data Science Techniques/014 Real Life Examples of Big Data.mp422.03MB

05 The Field of Data Science - Popular Data Science Techniques/015 Business Intelligence (BI) Techniques.mp489.94MB

05 The Field of Data Science - Popular Data Science Techniques/016 Real Life Examples of Business Intelligence (BI).mp429.54MB

05 The Field of Data Science - Popular Data Science Techniques/017 Techniques for Working with Traditional Methods.mp4111.65MB

05 The Field of Data Science - Popular Data Science Techniques/018 Real Life Examples of Traditional Methods.mp442.78MB

05 The Field of Data Science - Popular Data Science Techniques/019 Machine Learning (ML) Techniques.mp499.32MB

05 The Field of Data Science - Popular Data Science Techniques/020 Types of Machine Learning.mp4125.14MB

05 The Field of Data Science - Popular Data Science Techniques/021 Real Life Examples of Machine Learning (ML).mp436.81MB

06 The Field of Data Science - Popular Data Science Tools/022 Necessary Programming Languages and Software Used in Data Science.mp4103.51MB

07 The Field of Data Science - Careers in Data Science/023 Finding the Job - What to Expect and What to Look for.mp454.38MB

08 The Field of Data Science - Debunking Common Misconceptions/024 Debunking Common Misconceptions.mp472.85MB

09 Part 2 Probability/025 The Basic Probability Formula.mp485.91MB

09 Part 2 Probability/026 Computing Expected Values.mp475.68MB

09 Part 2 Probability/027 Frequency.mp461.73MB

09 Part 2 Probability/028 Events and Their Complements.mp459.15MB

10 Probability - Combinatorics/029 Fundamentals of Combinatorics.mp416.21MB

10 Probability - Combinatorics/030 Permutations and How to Use Them.mp442.72MB

10 Probability - Combinatorics/031 Simple Operations with Factorials.mp436.11MB

10 Probability - Combinatorics/032 Solving Variations with Repetition.mp434MB

10 Probability - Combinatorics/033 Solving Variations without Repetition.mp443.14MB

10 Probability - Combinatorics/034 Solving Combinations.mp457.34MB

10 Probability - Combinatorics/035 Symmetry of Combinations.mp440.3MB

10 Probability - Combinatorics/036 Solving Combinations with Separate Sample Spaces.mp433.15MB

10 Probability - Combinatorics/037 Combinatorics in Real-Life The Lottery.mp441.29MB

10 Probability - Combinatorics/038 A Recap of Combinatorics.mp438.49MB

10 Probability - Combinatorics/039 A Practical Example of Combinatorics.mp4134.31MB

11 Probability - Bayesian Inference/040 Sets and Events.mp453.46MB

11 Probability - Bayesian Inference/041 Ways Sets Can Interact.mp447.42MB

11 Probability - Bayesian Inference/042 Intersection of Sets.mp426.96MB

11 Probability - Bayesian Inference/043 Union of Sets.mp457.19MB

11 Probability - Bayesian Inference/044 Mutually Exclusive Sets.mp425.39MB

11 Probability - Bayesian Inference/045 Dependence and Independence of Sets.mp434.78MB

11 Probability - Bayesian Inference/046 The Conditional Probability Formula.mp445.86MB

11 Probability - Bayesian Inference/047 The Law of Total Probability.mp434.93MB

11 Probability - Bayesian Inference/048 The Additive Rule.mp426.97MB

11 Probability - Bayesian Inference/049 The Multiplication Law.mp449.02MB

11 Probability - Bayesian Inference/050 Bayes Law.mp449.93MB

11 Probability - Bayesian Inference/051 A Practical Example of Bayesian Inference.mp4145.12MB

12 Probability - Distributions/052 Fundamentals of Probability Distributions.mp473.4MB

12 Probability - Distributions/053 Types of Probability Distributions.mp471.06MB

12 Probability - Distributions/054 Characteristics of Discrete Distributions.mp422.7MB

12 Probability - Distributions/055 Discrete Distributions The Uniform Distribution.mp424.39MB

12 Probability - Distributions/056 Discrete Distributions The Bernoulli Distribution.mp434.13MB

12 Probability - Distributions/057 Discrete Distributions The Binomial Distribution.mp468.83MB

12 Probability - Distributions/058 Discrete Distributions The Poisson Distribution.mp455.75MB

12 Probability - Distributions/059 Characteristics of Continuous Distributions.mp484.12MB

12 Probability - Distributions/060 Continuous Distributions The Normal Distribution.mp448.24MB

12 Probability - Distributions/061 Continuous Distributions The Standard Normal Distribution.mp447.9MB

12 Probability - Distributions/062 Continuous Distributions The Students T Distribution.mp427.18MB

12 Probability - Distributions/063 Continuous Distributions The Chi-Squared Distribution.mp426.34MB

12 Probability - Distributions/064 Continuous Distributions The Exponential Distribution.mp440.23MB

12 Probability - Distributions/065 Continuous Distributions The Logistic Distribution.mp447.05MB

12 Probability - Distributions/066 A Practical Example of Probability Distributions.mp4157.82MB

13 Probability - Probability in Other Fields/067 Probability in Finance.mp499.06MB

13 Probability - Probability in Other Fields/068 Probability in Statistics.mp477.28MB

13 Probability - Probability in Other Fields/069 Probability in Data Science.mp463.49MB

14 Part 3 Statistics/070 Population and Sample.mp458.11MB

15 Statistics - Descriptive Statistics/071 Types of Data.mp472.52MB

15 Statistics - Descriptive Statistics/072 Levels of Measurement.mp454.38MB

15 Statistics - Descriptive Statistics/073 Categorical Variables - Visualization Techniques.mp436.64MB

15 Statistics - Descriptive Statistics/075 Numerical Variables - Frequency Distribution Table.mp425.85MB

15 Statistics - Descriptive Statistics/077 The Histogram.mp413.78MB

15 Statistics - Descriptive Statistics/079 Cross Tables and Scatter Plots.mp439.8MB

15 Statistics - Descriptive Statistics/081 Mean median and mode.mp437.12MB

15 Statistics - Descriptive Statistics/083 Skewness.mp419.4MB

15 Statistics - Descriptive Statistics/085 Variance.mp450.95MB

15 Statistics - Descriptive Statistics/087 Standard Deviation and Coefficient of Variation.mp445.12MB

15 Statistics - Descriptive Statistics/089 Covariance.mp427.48MB

15 Statistics - Descriptive Statistics/091 Correlation Coefficient.mp429.38MB

16 Statistics - Practical Example Descriptive Statistics/093 Practical Example Descriptive Statistics.mp4160.46MB

17 Statistics - Inferential Statistics Fundamentals/095 Introduction.mp415.5MB

17 Statistics - Inferential Statistics Fundamentals/096 What is a Distribution.mp461.59MB

17 Statistics - Inferential Statistics Fundamentals/097 The Normal Distribution.mp449.85MB

17 Statistics - Inferential Statistics Fundamentals/098 The Standard Normal Distribution.mp422.5MB

17 Statistics - Inferential Statistics Fundamentals/100 Central Limit Theorem.mp462.88MB

17 Statistics - Inferential Statistics Fundamentals/101 Standard error.mp422.77MB

17 Statistics - Inferential Statistics Fundamentals/102 Estimators and Estimates.mp447.83MB

18 Statistics - Inferential Statistics Confidence Intervals/103 What are Confidence Intervals.mp449.98MB

18 Statistics - Inferential Statistics Confidence Intervals/104 Confidence Intervals Population Variance Known Z-score.mp478.2MB

18 Statistics - Inferential Statistics Confidence Intervals/106 Confidence Interval Clarifications.mp457.03MB

18 Statistics - Inferential Statistics Confidence Intervals/107 Students T Distribution.mp435.43MB

18 Statistics - Inferential Statistics Confidence Intervals/108 Confidence Intervals Population Variance Unknown T-score.mp432.2MB

18 Statistics - Inferential Statistics Confidence Intervals/110 Margin of Error.mp447.23MB

18 Statistics - Inferential Statistics Confidence Intervals/111 Confidence intervals. Two means. Dependent samples.mp470.47MB

18 Statistics - Inferential Statistics Confidence Intervals/113 Confidence intervals. Two means. Independent Samples (Part 1).mp428.75MB

18 Statistics - Inferential Statistics Confidence Intervals/115 Confidence intervals. Two means. Independent Samples (Part 2).mp426.82MB

18 Statistics - Inferential Statistics Confidence Intervals/117 Confidence intervals. Two means. Independent Samples (Part 3).mp419.93MB

19 Statistics - Practical Example Inferential Statistics/118 Practical Example Inferential Statistics.mp4102.66MB

20 Statistics - Hypothesis Testing/120 Null vs Alternative Hypothesis.mp492.04MB

20 Statistics - Hypothesis Testing/122 Rejection Region and Significance Level.mp482.61MB

20 Statistics - Hypothesis Testing/123 Type I Error and Type II Error.mp443.93MB

20 Statistics - Hypothesis Testing/124 Test for the Mean. Population Variance Known.mp454.22MB

20 Statistics - Hypothesis Testing/126 p-value.mp455.87MB

20 Statistics - Hypothesis Testing/127 Test for the Mean. Population Variance Unknown.mp440.24MB

20 Statistics - Hypothesis Testing/129 Test for the Mean. Dependent Samples.mp450.37MB

20 Statistics - Hypothesis Testing/131 Test for the mean. Independent Samples (Part 1).mp433.94MB

20 Statistics - Hypothesis Testing/133 Test for the mean. Independent Samples (Part 2).mp436.39MB

21 Statistics - Practical Example Hypothesis Testing/135 Practical Example Hypothesis Testing.mp469.48MB

22 Part 4 Introduction to Python/137 Introduction to Programming.mp458.54MB

22 Part 4 Introduction to Python/138 Why Python.mp475.07MB

22 Part 4 Introduction to Python/139 Why Jupyter.mp444.31MB

22 Part 4 Introduction to Python/140 Installing Python and Jupyter.mp450.99MB

22 Part 4 Introduction to Python/141 Understanding Jupyters Interface - the Notebook Dashboard.mp413.79MB

22 Part 4 Introduction to Python/142 Prerequisites for Coding in the Jupyter Notebooks.mp430.58MB

22 Part 4 Introduction to Python/143 Python 2 vs Python 3.mp411.27MB

23 Python - Variables and Data Types/144 Variables.mp425.3MB

23 Python - Variables and Data Types/145 Numbers and Boolean Values in Python.mp417.06MB

23 Python - Variables and Data Types/146 Python Strings.mp450.64MB

24 Python - Basic Python Syntax/147 Using Arithmetic Operators in Python.mp418.92MB

24 Python - Basic Python Syntax/148 The Double Equality Sign.mp45.99MB

24 Python - Basic Python Syntax/149 How to Reassign Values.mp44MB

24 Python - Basic Python Syntax/150 Add Comments.mp411.26MB

24 Python - Basic Python Syntax/151 Understanding Line Continuation.mp42.35MB

24 Python - Basic Python Syntax/152 Indexing Elements.mp45.93MB

24 Python - Basic Python Syntax/153 Structuring with Indentation.mp413.14MB

25 Python - Other Python Operators/154 Comparison Operators.mp410.17MB

25 Python - Other Python Operators/155 Logical and Identity Operators.mp430.05MB

26 Python - Conditional Statements/156 The IF Statement.mp423.23MB

26 Python - Conditional Statements/157 The ELSE Statement.mp423.28MB

26 Python - Conditional Statements/158 The ELIF Statement.mp453.33MB

26 Python - Conditional Statements/159 A Note on Boolean Values.mp419.99MB

27 Python - Python Functions/160 Defining a Function in Python.mp414.75MB

27 Python - Python Functions/161 How to Create a Function with a Parameter.mp438.1MB

27 Python - Python Functions/162 Defining a Function in Python - Part II.mp425.24MB

27 Python - Python Functions/163 How to Use a Function within a Function.mp48.13MB

27 Python - Python Functions/164 Conditional Statements and Functions.mp415.68MB

27 Python - Python Functions/165 Functions Containing a Few Arguments.mp414.71MB

27 Python - Python Functions/166 Built-in Functions in Python.mp422.01MB

28 Python - Sequences/167 Lists.mp437.79MB

28 Python - Sequences/168 Using Methods.mp437.59MB

28 Python - Sequences/169 List Slicing.mp430.76MB

28 Python - Sequences/170 Tuples.mp429.49MB

28 Python - Sequences/171 Dictionaries.mp441.68MB

29 Python - Iterations/172 For Loops.mp423.59MB

29 Python - Iterations/173 While Loops and Incrementing.mp428.43MB

29 Python - Iterations/174 Lists with the range() Function.mp425.79MB

29 Python - Iterations/175 Conditional Statements and Loops.mp427.76MB

29 Python - Iterations/176 Conditional Statements Functions and Loops.mp49.48MB

29 Python - Iterations/177 How to Iterate over Dictionaries.mp429.65MB

30 Python - Advanced Python Tools/178 Object Oriented Programming.mp433.59MB

30 Python - Advanced Python Tools/179 Modules and Packages.mp48.5MB

30 Python - Advanced Python Tools/180 What is the Standard Library.mp418.03MB

30 Python - Advanced Python Tools/181 Importing Modules in Python.mp419.93MB

31 Part 5 Advanced Statistical Methods in Python/182 Introduction to Regression Analysis.mp417.32MB

32 Advanced Statistical Methods - Linear Regression with StatsModels/183 The Linear Regression Model.mp457.37MB

32 Advanced Statistical Methods - Linear Regression with StatsModels/184 Correlation vs Regression.mp414.73MB

32 Advanced Statistical Methods - Linear Regression with StatsModels/185 Geometrical Representation of the Linear Regression Model.mp45.12MB

32 Advanced Statistical Methods - Linear Regression with StatsModels/186 Python Packages Installation.mp440.58MB

32 Advanced Statistical Methods - Linear Regression with StatsModels/187 First Regression in Python.mp444.56MB

32 Advanced Statistical Methods - Linear Regression with StatsModels/189 Using Seaborn for Graphs.mp412.24MB

32 Advanced Statistical Methods - Linear Regression with StatsModels/190 How to Interpret the Regression Table.mp444.64MB

32 Advanced Statistical Methods - Linear Regression with StatsModels/191 Decomposition of Variability.mp449.66MB

32 Advanced Statistical Methods - Linear Regression with StatsModels/192 What is the OLS.mp428.31MB

32 Advanced Statistical Methods - Linear Regression with StatsModels/193 R-Squared.mp441.03MB

33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/194 Multiple Linear Regression.mp421.52MB

33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/195 Adjusted R-Squared.mp454.83MB

33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/197 Test for Significance of the Model (F-Test).mp416.42MB

33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/198 OLS Assumptions.mp421.85MB

33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/199 A1 Linearity.mp412.6MB

33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/200 A2 No Endogeneity.mp435.67MB

33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/201 A3 Normality and Homoscedasticity.mp442.7MB

33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/202 A4 No Autocorrelation.mp431.51MB

33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/203 A5 No Multicollinearity.mp428.7MB

33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/204 Dealing with Categorical Data - Dummy Variables.mp455.66MB

33 Advanced Statistical Methods - Multiple Linear Regression with StatsModels/206 Making Predictions with the Linear Regression.mp424.69MB

34 Advanced Statistical Methods - Linear Regression with sklearn/207 What is sklearn and How is it Different from Other Packages.mp427.25MB

34 Advanced Statistical Methods - Linear Regression with sklearn/208 How are Going to Approach this Section.mp419.41MB

34 Advanced Statistical Methods - Linear Regression with sklearn/209 Simple Linear Regression with sklearn.mp434.77MB

34 Advanced Statistical Methods - Linear Regression with sklearn/210 Simple Linear Regression with sklearn - A StatsModels-like Summary Table.mp432MB

34 Advanced Statistical Methods - Linear Regression with sklearn/213 Multiple Linear Regression with sklearn.mp420.07MB

34 Advanced Statistical Methods - Linear Regression with sklearn/214 Calculating the Adjusted R-Squared in sklearn.mp430.88MB

34 Advanced Statistical Methods - Linear Regression with sklearn/216 Feature Selection (F-regression).mp429.51MB

34 Advanced Statistical Methods - Linear Regression with sklearn/218 Creating a Summary Table with P-values.mp412.3MB

34 Advanced Statistical Methods - Linear Regression with sklearn/220 Feature Scaling (Standardization).mp439.08MB

34 Advanced Statistical Methods - Linear Regression with sklearn/221 Feature Selection through Standardization of Weights.mp434.89MB

34 Advanced Statistical Methods - Linear Regression with sklearn/222 Predicting with the Standardized Coefficients.mp425.96MB

34 Advanced Statistical Methods - Linear Regression with sklearn/224 Underfitting and Overfitting.mp416.95MB

34 Advanced Statistical Methods - Linear Regression with sklearn/225 Train - Test Split Explained.mp449.17MB

35 Advanced Statistical Methods - Practical Example Linear Regression/226 Practical Example Linear Regression (Part 1).mp497.08MB

35 Advanced Statistical Methods - Practical Example Linear Regression/227 Practical Example Linear Regression (Part 2).mp446MB

35 Advanced Statistical Methods - Practical Example Linear Regression/229 Practical Example Linear Regression (Part 3).mp423.69MB

35 Advanced Statistical Methods - Practical Example Linear Regression/231 Practical Example Linear Regression (Part 4).mp456.04MB

35 Advanced Statistical Methods - Practical Example Linear Regression/233 Practical Example Linear Regression (Part 5).mp457.88MB

36 Advanced Statistical Methods - Logistic Regression/235 Introduction to Logistic Regression.mp427.06MB

36 Advanced Statistical Methods - Logistic Regression/236 A Simple Example in Python.mp434.69MB

36 Advanced Statistical Methods - Logistic Regression/237 Logistic vs Logit Function.mp486.49MB

36 Advanced Statistical Methods - Logistic Regression/238 Building a Logistic Regression.mp417.1MB

36 Advanced Statistical Methods - Logistic Regression/240 An Invaluable Coding Tip.mp423.05MB

36 Advanced Statistical Methods - Logistic Regression/241 Understanding Logistic Regression Tables.mp430.55MB

36 Advanced Statistical Methods - Logistic Regression/243 What do the Odds Actually Mean.mp432.28MB

36 Advanced Statistical Methods - Logistic Regression/244 Binary Predictors in a Logistic Regression.mp438.43MB

36 Advanced Statistical Methods - Logistic Regression/246 Calculating the Accuracy of the Model.mp432.85MB

36 Advanced Statistical Methods - Logistic Regression/248 Underfitting and Overfitting.mp422.29MB

36 Advanced Statistical Methods - Logistic Regression/249 Testing the Model.mp432.27MB

37 Advanced Statistical Methods - Cluster Analysis/251 Introduction to Cluster Analysis.mp453.42MB

37 Advanced Statistical Methods - Cluster Analysis/252 Some Examples of Clusters.mp471.53MB

37 Advanced Statistical Methods - Cluster Analysis/253 Difference between Classification and Clustering.mp436.15MB

37 Advanced Statistical Methods - Cluster Analysis/254 Math Prerequisites.mp414.55MB

38 Advanced Statistical Methods - K-Means Clustering/255 K-Means Clustering.mp427.28MB

38 Advanced Statistical Methods - K-Means Clustering/256 A Simple Example of Clustering.mp451.82MB

38 Advanced Statistical Methods - K-Means Clustering/258 Clustering Categorical Data.mp421.23MB

38 Advanced Statistical Methods - K-Means Clustering/260 How to Choose the Number of Clusters.mp444.13MB

38 Advanced Statistical Methods - K-Means Clustering/262 Pros and Cons of K-Means Clustering.mp437.7MB

38 Advanced Statistical Methods - K-Means Clustering/263 To Standardize or not to Standardize.mp430.1MB

38 Advanced Statistical Methods - K-Means Clustering/264 Relationship between Clustering and Regression.mp49.93MB

38 Advanced Statistical Methods - K-Means Clustering/265 Market Segmentation with Cluster Analysis (Part 1).mp443.01MB

38 Advanced Statistical Methods - K-Means Clustering/266 Market Segmentation with Cluster Analysis (Part 2).mp456.11MB

38 Advanced Statistical Methods - K-Means Clustering/267 How is Clustering Useful.mp474.45MB

39 Advanced Statistical Methods - Other Types of Clustering/270 Types of Clustering.mp444.57MB

39 Advanced Statistical Methods - Other Types of Clustering/271 Dendrogram.mp429.06MB

39 Advanced Statistical Methods - Other Types of Clustering/272 Heatmaps.mp429.62MB

40 Part 6 Mathematics/273 What is a Matrix.mp433.59MB

40 Part 6 Mathematics/274 Scalars and Vectors.mp433.85MB

40 Part 6 Mathematics/275 Linear Algebra and Geometry.mp449.79MB

40 Part 6 Mathematics/276 Arrays in Python - A Convenient Way To Represent Matrices.mp426.67MB

40 Part 6 Mathematics/277 What is a Tensor.mp422.52MB

40 Part 6 Mathematics/278 Addition and Subtraction of Matrices.mp432.61MB

40 Part 6 Mathematics/279 Errors when Adding Matrices.mp411.17MB

40 Part 6 Mathematics/280 Transpose of a Matrix.mp438.07MB

40 Part 6 Mathematics/281 Dot Product.mp423.99MB

40 Part 6 Mathematics/282 Dot Product of Matrices.mp449.43MB

40 Part 6 Mathematics/283 Why is Linear Algebra Useful.mp4144.33MB

41 Part 7 Deep Learning/284 What to Expect from this Part.mp431.1MB

42 Deep Learning - Introduction to Neural Networks/285 Introduction to Neural Networks.mp442.92MB

42 Deep Learning - Introduction to Neural Networks/286 Training the Model.mp428.71MB

42 Deep Learning - Introduction to Neural Networks/287 Types of Machine Learning.mp445.1MB

42 Deep Learning - Introduction to Neural Networks/288 The Linear Model (Linear Algebraic Version).mp428.44MB

42 Deep Learning - Introduction to Neural Networks/289 The Linear Model with Multiple Inputs.mp425.11MB

42 Deep Learning - Introduction to Neural Networks/290 The Linear model with Multiple Inputs and Multiple Outputs.mp438.31MB

42 Deep Learning - Introduction to Neural Networks/291 Graphical Representation of Simple Neural Networks.mp422.64MB

42 Deep Learning - Introduction to Neural Networks/292 What is the Objective Function.mp417.91MB

42 Deep Learning - Introduction to Neural Networks/293 Common Objective Functions L2-norm Loss.mp423.27MB

42 Deep Learning - Introduction to Neural Networks/294 Common Objective Functions Cross-Entropy Loss.mp437.24MB

42 Deep Learning - Introduction to Neural Networks/295 Optimization Algorithm 1-Parameter Gradient Descent.mp455.62MB

42 Deep Learning - Introduction to Neural Networks/296 Optimization Algorithm n-Parameter Gradient Descent.mp439.42MB

43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/297 Basic NN Example (Part 1).mp420.59MB

43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/298 Basic NN Example (Part 2).mp434.94MB

43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/299 Basic NN Example (Part 3).mp424.4MB

43 Deep Learning - How to Build a Neural Network from Scratch with NumPy/300 Basic NN Example (Part 4).mp461.13MB

44 Deep Learning - TensorFlow 2.0 Introduction/302 How to Install TensorFlow 2.0.mp438.76MB

44 Deep Learning - TensorFlow 2.0 Introduction/303 TensorFlow Outline and Comparison with Other Libraries.mp433.51MB

44 Deep Learning - TensorFlow 2.0 Introduction/304 TensorFlow 1 vs TensorFlow 2.mp421.99MB

44 Deep Learning - TensorFlow 2.0 Introduction/305 A Note on TensorFlow 2 Syntax.mp46.75MB

44 Deep Learning - TensorFlow 2.0 Introduction/306 Types of File Formats Supporting TensorFlow.mp416.4MB

44 Deep Learning - TensorFlow 2.0 Introduction/307 Outlining the Model with TensorFlow 2.mp434.69MB

44 Deep Learning - TensorFlow 2.0 Introduction/308 Interpreting the Result and Extracting the Weights and Bias.mp430.27MB

44 Deep Learning - TensorFlow 2.0 Introduction/309 Customizing a TensorFlow 2 Model.mp422.91MB

45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/311 What is a Layer.mp412.5MB

45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/312 What is a Deep Net.mp429.53MB

45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/313 Digging into a Deep Net.mp459.36MB

45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/314 Non-Linearities and their Purpose.mp427.68MB

45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/315 Activation Functions.mp425.09MB

45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/316 Activation Functions Softmax Activation.mp425.92MB

45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/317 Backpropagation.mp434.95MB

45 Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/318 Backpropagation Picture.mp419.5MB

46 Deep Learning - Overfitting/320 What is Overfitting.mp431.08MB

46 Deep Learning - Overfitting/321 Underfitting and Overfitting for Classification.mp425.07MB

46 Deep Learning - Overfitting/322 What is Validation.mp432.71MB

46 Deep Learning - Overfitting/323 Training Validation and Test Datasets.mp425.19MB

46 Deep Learning - Overfitting/324 N-Fold Cross Validation.mp420.7MB

46 Deep Learning - Overfitting/325 Early Stopping or When to Stop Training.mp424.17MB

47 Deep Learning - Initialization/326 What is Initialization.mp421.76MB

47 Deep Learning - Initialization/327 Types of Simple Initializations.mp414.31MB

47 Deep Learning - Initialization/328 State-of-the-Art Method - (Xavier) Glorot Initialization.mp417.14MB

48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/329 Stochastic Gradient Descent.mp428.68MB

48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/330 Problems with Gradient Descent.mp411.01MB

48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/331 Momentum.mp416.43MB

48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/332 Learning Rate Schedules or How to Choose the Optimal Learning Rate.mp429.08MB

48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/333 Learning Rate Schedules Visualized.mp49.11MB

48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/334 Adaptive Learning Rate Schedules (AdaGrad and RMSprop ).mp426.35MB

48 Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/335 Adam (Adaptive Moment Estimation).mp422.35MB

49 Deep Learning - Preprocessing/336 Preprocessing Introduction.mp427.78MB

49 Deep Learning - Preprocessing/337 Types of Basic Preprocessing.mp411.84MB

49 Deep Learning - Preprocessing/338 Standardization.mp450.98MB

49 Deep Learning - Preprocessing/339 Preprocessing Categorical Data.mp418.6MB

49 Deep Learning - Preprocessing/340 Binary and One-Hot Encoding.mp428.94MB

50 Deep Learning - Classifying on the MNIST Dataset/341 MNIST The Dataset.mp413.38MB

50 Deep Learning - Classifying on the MNIST Dataset/342 MNIST How to Tackle the MNIST.mp418.66MB

50 Deep Learning - Classifying on the MNIST Dataset/343 MNIST Importing the Relevant Packages and Loading the Data.mp416.32MB

50 Deep Learning - Classifying on the MNIST Dataset/344 MNIST Preprocess the Data - Create a Validation Set and Scale It.mp429.04MB

50 Deep Learning - Classifying on the MNIST Dataset/346 MNIST Preprocess the Data - Shuffle and Batch.mp441.52MB

50 Deep Learning - Classifying on the MNIST Dataset/348 MNIST Outline the Model.mp428.23MB

50 Deep Learning - Classifying on the MNIST Dataset/349 MNIST Select the Loss and the Optimizer.mp413.9MB

50 Deep Learning - Classifying on the MNIST Dataset/350 MNIST Learning.mp440.96MB

50 Deep Learning - Classifying on the MNIST Dataset/352 MNIST Testing the Model.mp429.52MB

51 Deep Learning - Business Case Example/353 Business Case Exploring the Dataset and Identifying Predictors.mp466.27MB

51 Deep Learning - Business Case Example/354 Business Case Outlining the Solution.mp47.3MB

51 Deep Learning - Business Case Example/355 Business Case Balancing the Dataset.mp430.43MB

51 Deep Learning - Business Case Example/356 Business Case Preprocessing the Data.mp484.33MB

51 Deep Learning - Business Case Example/358 Business Case Load the Preprocessed Data.mp417.57MB

51 Deep Learning - Business Case Example/360 Business Case Learning and Interpreting the Result.mp431.18MB

51 Deep Learning - Business Case Example/361 Business Case Setting an Early Stopping Mechanism.mp449.81MB

51 Deep Learning - Business Case Example/363 Business Case Testing the Model.mp410.79MB

52 Deep Learning - Conclusion/365 Summary on What Youve Learned.mp439.75MB

52 Deep Learning - Conclusion/366 Whats Further out there in terms of Machine Learning.mp420.12MB

52 Deep Learning - Conclusion/368 An overview of CNNs.mp458.79MB

52 Deep Learning - Conclusion/369 An Overview of RNNs.mp425.26MB

52 Deep Learning - Conclusion/370 An Overview of non-NN Approaches.mp444.77MB

53 Appendix Deep Learning - TensorFlow 1 Introduction/372 How to Install TensorFlow 1.mp411.35MB

53 Appendix Deep Learning - TensorFlow 1 Introduction/374 TensorFlow Intro.mp447.69MB

53 Appendix Deep Learning - TensorFlow 1 Introduction/375 Actual Introduction to TensorFlow.mp417.41MB

53 Appendix Deep Learning - TensorFlow 1 Introduction/376 Types of File Formats supporting Tensors.mp420.34MB

53 Appendix Deep Learning - TensorFlow 1 Introduction/377 Basic NN Example with TF Inputs Outputs Targets Weights Biases.mp438.49MB

53 Appendix Deep Learning - TensorFlow 1 Introduction/378 Basic NN Example with TF Loss Function and Gradient Descent.mp432.51MB

53 Appendix Deep Learning - TensorFlow 1 Introduction/379 Basic NN Example with TF Model Output.mp437.39MB

54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/381 MNIST What is the MNIST Dataset.mp417.82MB

54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/382 MNIST How to Tackle the MNIST.mp422.58MB

54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/383 MNIST Relevant Packages.mp418.9MB

54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/384 MNIST Model Outline.mp456.38MB

54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/385 MNIST Loss and Optimization Algorithm.mp425.86MB

54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/386 Calculating the Accuracy of the Model.mp443.9MB

54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/387 MNIST Batching and Early Stopping.mp412.85MB

54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/388 MNIST Learning.mp446.68MB

54 Appendix Deep Learning - TensorFlow 1 Classifying on the MNIST Dataset/389 MNIST Results and Testing.mp462.77MB

55 Appendix Deep Learning - TensorFlow 1 Business Case/392 Business Case Getting Acquainted with the Dataset.mp487.65MB

55 Appendix Deep Learning - TensorFlow 1 Business Case/393 Business Case Outlining the Solution.mp412.21MB

55 Appendix Deep Learning - TensorFlow 1 Business Case/394 The Importance of Working with a Balanced Dataset.mp439.41MB

55 Appendix Deep Learning - TensorFlow 1 Business Case/395 Business Case Preprocessing.mp4103.41MB

55 Appendix Deep Learning - TensorFlow 1 Business Case/397 Creating a Data Provider.mp476.34MB

55 Appendix Deep Learning - TensorFlow 1 Business Case/398 Business Case Model Outline.mp453.12MB

55 Appendix Deep Learning - TensorFlow 1 Business Case/399 Business Case Optimization.mp441.52MB

55 Appendix Deep Learning - TensorFlow 1 Business Case/400 Business Case Interpretation.mp425.74MB

55 Appendix Deep Learning - TensorFlow 1 Business Case/401 Business Case Testing the Model.mp411.2MB

55 Appendix Deep Learning - TensorFlow 1 Business Case/402 Business Case A Comment on the Homework.mp436.38MB

56 Software Integration/404 What are Data Servers Clients Requests and Responses.mp469.03MB

56 Software Integration/405 What are Data Connectivity APIs and Endpoints.mp4104.08MB

56 Software Integration/406 Taking a Closer Look at APIs.mp4115.59MB

56 Software Integration/407 Communication between Software Products through Text Files.mp460.34MB

56 Software Integration/408 Software Integration - Explained.mp463.69MB

57 Case Study - Whats Next in the Course/409 Game Plan for this Python SQL and Tableau Business Exercise.mp452.3MB

57 Case Study - Whats Next in the Course/410 The Business Task.mp439.15MB

57 Case Study - Whats Next in the Course/411 Introducing the Data Set.mp440.86MB

58 Case Study - Preprocessing the Absenteeism_data/413 Importing the Absenteeism Data in Python.mp423.15MB

58 Case Study - Preprocessing the Absenteeism_data/414 Checking the Content of the Data Set.mp461.9MB

58 Case Study - Preprocessing the Absenteeism_data/415 Introduction to Terms with Multiple Meanings.mp427.85MB

58 Case Study - Preprocessing the Absenteeism_data/417 Using a Statistical Approach towards the Solution to the Exercise.mp420.18MB

58 Case Study - Preprocessing the Absenteeism_data/418 Dropping a Column from a DataFrame in Python.mp461.76MB

58 Case Study - Preprocessing the Absenteeism_data/421 Analyzing the Reasons for Absence.mp440.57MB

58 Case Study - Preprocessing the Absenteeism_data/422 Obtaining Dummies from a Single Feature.mp481.11MB

58 Case Study - Preprocessing the Absenteeism_data/426 More on Dummy Variables A Statistical Perspective.mp413.74MB

58 Case Study - Preprocessing the Absenteeism_data/427 Classifying the Various Reasons for Absence.mp474.6MB

58 Case Study - Preprocessing the Absenteeism_data/428 Using .concat() in Python.mp438.73MB

58 Case Study - Preprocessing the Absenteeism_data/431 Reordering Columns in a Pandas DataFrame in Python.mp414.01MB

58 Case Study - Preprocessing the Absenteeism_data/434 Creating Checkpoints while Coding in Jupyter.mp425.67MB

58 Case Study - Preprocessing the Absenteeism_data/437 Analyzing the Dates from the Initial Data Set.mp457.28MB

58 Case Study - Preprocessing the Absenteeism_data/438 Extracting the Month Value from the Date Column.mp447.79MB

58 Case Study - Preprocessing the Absenteeism_data/439 Extracting the Day of the Week from the Date Column.mp427.96MB

58 Case Study - Preprocessing the Absenteeism_data/441 Analyzing Several Straightforward Columns for this Exercise.mp429.51MB

58 Case Study - Preprocessing the Absenteeism_data/442 Working on Education Children and Pets.mp439.59MB

58 Case Study - Preprocessing the Absenteeism_data/443 Final Remarks of this Section.mp421.63MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/445 Exploring the Problem with a Machine Learning Mindset.mp427.54MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/446 Creating the Targets for the Logistic Regression.mp445.79MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/447 Selecting the Inputs for the Logistic Regression.mp416.75MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/448 Standardizing the Data.mp420.59MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/449 Splitting the Data for Training and Testing.mp452.76MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/450 Fitting the Model and Assessing its Accuracy.mp441.62MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/451 Creating a Summary Table with the Coefficients and Intercept.mp438.87MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/452 Interpreting the Coefficients for Our Problem.mp452.37MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/453 Standardizing only the Numerical Variables (Creating a Custom Scaler).mp441.19MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/454 Interpreting the Coefficients of the Logistic Regression.mp440.4MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/455 Backward Elimination or How to Simplify Your Model.mp439.56MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/456 Testing the Model We Created.mp449.06MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/457 Saving the Model and Preparing it for Deployment.mp437.45MB

59 Case Study - Applying Machine Learning to Create the absenteeism_module/460 Preparing the Deployment of the Model through a Module.mp444.48MB

60 Case Study - Loading the absenteeism_module/462 Deploying the absenteeism_module - Part I.mp425.48MB

60 Case Study - Loading the absenteeism_module/463 Deploying the absenteeism_module - Part II.mp454.25MB

61 Case Study - Analyzing the Predicted Outputs in Tableau/466 Analyzing Age vs Probability in Tableau.mp456.55MB

61 Case Study - Analyzing the Predicted Outputs in Tableau/468 Analyzing Reasons vs Probability in Tableau.mp459.33MB

61 Case Study - Analyzing the Predicted Outputs in Tableau/470 Analyzing Transportation Expense vs Probability in Tableau.mp440.63MB