Udemy - The Data Science Course 2018 Complete Data Science Bootcamp

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  • 11. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4 159.46 MB
    33. Part 5 Mathematics/16. Why is Linear Algebra Useful.mp4 144.34 MB
    5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4 138.3 MB
    3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 126.87 MB
    5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4 125.15 MB
    5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4 123.51 MB
    15. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4 113.16 MB
    2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4 108.98 MB
    6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4 103.52 MB
    44. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4 103.41 MB
    14. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4 102.67 MB
    5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4 99.32 MB
    15. Statistics - Hypothesis Testing/1. The Null vs Alternative Hypothesis.mp4 92.12 MB
    5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4 89.94 MB
    44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp4 87.66 MB
    29. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4 86.49 MB
    2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp4 81.41 MB
    4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp4 81.19 MB
    13. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp4 78.2 MB
    44. Deep Learning - Business Case Example/6. Creating a Data Provider.mp4 76.34 MB
    5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4 75.51 MB
    17. Part 3 Introduction to Python/3. Why Python.mp4 75.08 MB
    31. Advanced Statistical Methods - K-Means Clustering/10. How is Clustering Useful.mp4 74.45 MB
    8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4 72.85 MB
    10. Statistics - Descriptive Statistics/1. Types of Data.mp4 72.52 MB
    30. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4 71.54 MB
    13. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Dependent samples.mp4 70.47 MB
    16. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp4 69.48 MB
    2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp4 67.74 MB
    2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 64.51 MB
    12. Statistics - Inferential Statistics Fundamentals/8. Central Limit Theorem.mp4 62.88 MB
    43. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp4 62.77 MB
    1. Part 1 Introduction/2. What Does the Course Cover.mp4 62.25 MB
    12. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4 61.59 MB
    36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4 61.14 MB
    38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp4 59.36 MB
    13. Statistics - Inferential Statistics Confidence Intervals/9. Margin of Error.mp4 59.09 MB
    45. Deep Learning - Conclusion/3. An overview of CNNs.mp4 58.79 MB
    17. Part 3 Introduction to Python/1. Introduction to Programming.mp4 58.55 MB
    9. Part 2 Statistics/1. Population and Sample.mp4 58.11 MB
    27. Advanced Statistical Methods - Linear regression/1. The Linear Regression Model.mp4 57.37 MB
    43. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp4 56.38 MB
    31. Advanced Statistical Methods - K-Means Clustering/9. Market Segmentation with Cluster Analysis (Part 2).mp4 56.11 MB
    15. Statistics - Hypothesis Testing/10. p-value.mp4 55.87 MB
    28. Advanced Statistical Methods - Multiple Linear Regression/17. Dealing with Categorical Data - Dummy Variables.mp4 55.66 MB
    35. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 55.62 MB
    28. Advanced Statistical Methods - Multiple Linear Regression/2. Adjusted R-Squared.mp4 54.83 MB
    17. Part 3 Introduction to Python/7. Installing Python and Jupyter.mp4 54.41 MB
    10. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4 54.39 MB
    7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp4 54.38 MB
    15. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp4 54.22 MB
    2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp4 53.56 MB
    30. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp4 53.42 MB
    44. Deep Learning - Business Case Example/7. Business Case Model Outline.mp4 53.13 MB
    31. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp4 51.82 MB
    42. Deep Learning - Preprocessing/3. Standardization.mp4 50.98 MB
    10. Statistics - Descriptive Statistics/17. Variance.mp4 50.95 MB
    15. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp4 50.39 MB
    13. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp4 49.98 MB
    12. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp4 49.85 MB
    33. Part 5 Mathematics/5. Linear Algebra and Geometry.mp4 49.79 MB
    27. Advanced Statistical Methods - Linear regression/11. Decomposition of Variability.mp4 49.66 MB
    33. Part 5 Mathematics/15. Dot Product of Matrices.mp4 49.43 MB
    1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp4 49.03 MB
    12. Statistics - Inferential Statistics Fundamentals/11. Estimators and Estimates.mp4 47.83 MB
    37. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.mp4 47.69 MB
    43. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.mp4 46.69 MB
    10. Statistics - Descriptive Statistics/19. Standard Deviation and Coefficient of Variation.mp4 45.13 MB
    35. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp4 45.11 MB
    45. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp4 44.77 MB
    27. Advanced Statistical Methods - Linear regression/10. How to Interpret the Regression Table.mp4 44.64 MB
    32. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp4 44.58 MB
    27. Advanced Statistical Methods - Linear regression/7. First Regression in Python.mp4 44.57 MB
    17. Part 3 Introduction to Python/5. Why Jupyter.mp4 44.31 MB
    31. Advanced Statistical Methods - K-Means Clustering/4. How to Choose the Number of Clusters.mp4 44.14 MB
    15. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp4 43.93 MB
    43. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp4 43.9 MB
    31. Advanced Statistical Methods - K-Means Clustering/8. Market Segmentation with Cluster Analysis (Part 1).mp4 43.01 MB
    35. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp4 42.92 MB
    5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp4 42.78 MB
    28. Advanced Statistical Methods - Multiple Linear Regression/12. A3 Normality and Homoscedasticity.mp4 42.7 MB
    44. Deep Learning - Business Case Example/8. Business Case Optimization.mp4 41.52 MB
    27. Advanced Statistical Methods - Linear regression/14. R-Squared.mp4 41.03 MB
    27. Advanced Statistical Methods - Linear regression/6. Python Packages Installation.mp4 40.59 MB
    15. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp4 40.21 MB
    10. Statistics - Descriptive Statistics/11. Cross Table and Scatter Plot.mp4 39.81 MB
    45. Deep Learning - Conclusion/1. Summary of What You Learned.mp4 39.76 MB
    35. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp4 39.42 MB
    44. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.mp4 39.41 MB
    37. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 38.49 MB
    10. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4 38.46 MB
    29. Advanced Statistical Methods - Logistic Regression/8. Binary Predictors in a Logistic Regression.mp4 38.43 MB
    35. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 38.31 MB
    33. Part 5 Mathematics/13. Transpose of a Matrix.mp4 38.07 MB
    31. Advanced Statistical Methods - K-Means Clustering/5. Pros and Cons of K-Means Clustering.mp4 37.71 MB
    37. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.mp4 37.39 MB
    35. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp4 37.24 MB
    10. Statistics - Descriptive Statistics/13. Mean, median and mode.mp4 37.07 MB
    5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp4 36.81 MB
    44. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp4 36.38 MB
    15. Statistics - Hypothesis Testing/17. Test for the mean. Independent samples (Part 2).mp4 36.37 MB
    30. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp4 36.16 MB
    28. Advanced Statistical Methods - Multiple Linear Regression/10. A2 No Endogeneity.mp4 35.67 MB
    13. Statistics - Inferential Statistics Confidence Intervals/5. Student's T Distribution.mp4 35.43 MB
    38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp4 34.95 MB
    36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4 34.94 MB
    29. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp4 34.7 MB
    33. Part 5 Mathematics/3. Scalars and Vectors.mp4 33.85 MB
    25. Python - Advanced Python Tools/1. Object Oriented Programming.mp4 33.59 MB
    33. Part 5 Mathematics/1. What is a matrix.mp4 33.59 MB
    21. Python - Conditional Statements/4. The ELIF Statement.mp4 33.15 MB
    29. Advanced Statistical Methods - Logistic Regression/9. Calculating the Accuracy of the Model.mp4 32.85 MB
    39. Deep Learning - Overfitting/3. What is Validation.mp4 32.71 MB
    33. Part 5 Mathematics/10. Addition and Subtraction of Matrices.mp4 32.62 MB
    37. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.mp4 32.51 MB
    29. Advanced Statistical Methods - Logistic Regression/7. What do the Odds Actually Mean.mp4 32.29 MB
    29. Advanced Statistical Methods - Logistic Regression/11. Testing the Model.mp4 32.27 MB
    13. Statistics - Inferential Statistics Confidence Intervals/7. Confidence Intervals; Population Variance Unknown; t-score.mp4 32.21 MB
    28. Advanced Statistical Methods - Multiple Linear Regression/13. A4 No Autocorrelation.mp4 31.52 MB
    34. Part 6 Deep Learning/1. What to Expect from this Part.mp4 31.1 MB
    39. Deep Learning - Overfitting/1. What is Overfitting.mp4 31.08 MB
    23. Python - Sequences/5. List Slicing.mp4 30.77 MB
    18. Python - Variables and Data Types/5. Python Strings.mp4 30.76 MB
    17. Part 3 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp4 30.58 MB
    29. Advanced Statistical Methods - Logistic Regression/6. Understanding Logistic Regression Tables.mp4 30.55 MB
    31. Advanced Statistical Methods - K-Means Clustering/6. To Standardize or to not Standardize.mp4 30.11 MB
    20. Python - Other Python Operators/3. Logical and Identity Operators.mp4 30.05 MB
    15. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp4 29.96 MB
    5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp4 29.94 MB
    32. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4 29.62 MB
    10. Statistics - Descriptive Statistics/23. Correlation Coefficient.mp4 29.57 MB
    5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4 29.54 MB
    38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp4 29.53 MB
    41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.mp4 29.09 MB
    32. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp4 29.06 MB
    42. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp4 28.95 MB
    13. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Independent samples (Part 1).mp4 28.75 MB
    28. Advanced Statistical Methods - Multiple Linear Regression/15. A5 No Multicollinearity.mp4 28.71 MB
    35. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4 28.71 MB
    41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp4 28.68 MB
    35. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp4 28.44 MB
    27. Advanced Statistical Methods - Linear regression/13. What is the OLS.mp4 28.31 MB
    42. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp4 27.78 MB
    38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp4 27.68 MB
    10. Statistics - Descriptive Statistics/21. Covariance.mp4 27.48 MB
    31. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp4 27.28 MB
    29. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp4 27.07 MB
    13. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent samples (Part 2).mp4 26.82 MB
    18. Python - Variables and Data Types/1. Variables.mp4 26.61 MB
    41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules ( AdaGrad and RMSprop ).mp4 26.35 MB
    33. Part 5 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 26.12 MB
    10. Statistics - Descriptive Statistics/7. Numerical Variables - Frequency Distribution Table.mp4 25.98 MB
    38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp4 25.92 MB
    43. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp4 25.86 MB
    44. Deep Learning - Business Case Example/9. Business Case Interpretation.mp4 25.74 MB
    45. Deep Learning - Conclusion/5. An Overview of RNNs.mp4 25.27 MB
    39. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp4 25.2 MB
    35. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp4 25.11 MB
    38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp4 25.1 MB
    39. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp4 25.07 MB
    23. Python - Sequences/7. Dictionaries.mp4 25.04 MB
    28. Advanced Statistical Methods - Multiple Linear Regression/19. Making Predictions with the Linear Regression.mp4 24.7 MB
    36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp4 24.4 MB
    39. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp4 24.17 MB
    33. Part 5 Mathematics/14. Dot Product.mp4 24 MB
    22. Python - Python Functions/2. How to Create a Function with a Parameter.mp4 23.87 MB
    35. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp4 23.28 MB
    29. Advanced Statistical Methods - Logistic Regression/5. An Invaluable Coding Tip.mp4 23.05 MB
    12. Statistics - Inferential Statistics Fundamentals/10. Standard error.mp4 22.77 MB
    35. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp4 22.64 MB
    43. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp4 22.59 MB
    33. Part 5 Mathematics/8. What is a Tensor.mp4 22.53 MB
    12. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp4 22.51 MB
    41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp4 22.36 MB
    29. Advanced Statistical Methods - Logistic Regression/10. Underfitting and Overfitting.mp4 22.29 MB
    5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp4 22.03 MB
    22. Python - Python Functions/7. Built-in Functions in Python.mp4 22.02 MB
    23. Python - Sequences/1. Lists.mp4 22 MB
    23. Python - Sequences/3. Using Methods.mp4 21.95 MB
    28. Advanced Statistical Methods - Multiple Linear Regression/6. OLS Assumptions.mp4 21.85 MB
    40. Deep Learning - Initialization/1. What is Initialization.mp4 21.76 MB
    28. Advanced Statistical Methods - Multiple Linear Regression/1. Multiple Linear Regression.mp4 21.53 MB
    31. Advanced Statistical Methods - K-Means Clustering/3. Clustering Categorical Data.mp4 21.24 MB
    39. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp4 20.7 MB
    36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp4 20.6 MB
    37. Deep Learning - TensorFlow Introduction/5. Types of File Formats, supporting Tensors.mp4 20.34 MB
    45. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp4 20.13 MB
    13. Statistics - Inferential Statistics Confidence Intervals/17. Confidence intervals. Two means. Independent samples (Part 3).mp4 19.93 MB
    25. Python - Advanced Python Tools/7. Importing Modules in Python.mp4 19.93 MB
    38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.mp4 19.51 MB
    10. Statistics - Descriptive Statistics/15. Skewness.mp4 19.41 MB
    19. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp4 18.92 MB
    43. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp4 18.91 MB
    42. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp4 18.6 MB
    25. Python - Advanced Python Tools/5. What is the Standard Library.mp4 18.04 MB
    35. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp4 17.91 MB
    43. Deep Learning - Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp4 17.82 MB
    37. Deep Learning - TensorFlow Introduction/4. Actual Introduction to TensorFlow.mp4 17.41 MB
    26. Part 4 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp4 17.32 MB
    40. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 17.14 MB
    29. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp4 17.11 MB
    18. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp4 17.07 MB
    24. Python - Iterations/8. How to Iterate over Dictionaries.mp4 16.98 MB
    23. Python - Sequences/6. Tuples.mp4 16.67 MB
    41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp4 16.44 MB
    28. Advanced Statistical Methods - Multiple Linear Regression/5. Test for Significance of the Model (F-Test).mp4 16.42 MB
    24. Python - Iterations/6. Conditional Statements and Loops.mp4 16.09 MB
    22. Python - Python Functions/5. Conditional Statements and Functions.mp4 15.69 MB
    12. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp4 15.51 MB
    24. Python - Iterations/3. While Loops and Incrementing.mp4 15.44 MB
    22. Python - Python Functions/3. Defining a Function in Python - Part II.mp4 14.78 MB
    27. Advanced Statistical Methods - Linear regression/3. Correlation vs Regression.mp4 14.73 MB
    37. Deep Learning - TensorFlow Introduction/1. How to Install TensorFlow.mp4 14.56 MB
    30. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp4 14.55 MB
    40. Deep Learning - Initialization/2. Types of Simple Initializations.mp4 14.31 MB
    17. Part 3 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 13.79 MB
    10. Statistics - Descriptive Statistics/9. The Histogram.mp4 13.78 MB
    21. Python - Conditional Statements/1. The IF Statement.mp4 13.63 MB
    21. Python - Conditional Statements/3. The ELSE Statement.mp4 13.58 MB
    43. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp4 12.85 MB
    28. Advanced Statistical Methods - Multiple Linear Regression/8. A1 Linearity.mp4 12.61 MB
    38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.mp4 12.5 MB
    27. Advanced Statistical Methods - Linear regression/9. Using Seaborn for Graphs.mp4 12.24 MB
    44. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp4 12.22 MB
    42. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp4 11.84 MB
    24. Python - Iterations/1. For Loops.mp4 11.79 MB
    24. Python - Iterations/4. Lists with the range() Function.mp4 11.42 MB
    21. Python - Conditional Statements/5. A Note on Boolean Values.mp4 11.25 MB
    44. Deep Learning - Business Case Example/10. Business Case Testing the Model.mp4 11.2 MB
    33. Part 5 Mathematics/12. Errors when Adding Matrices.mp4 11.18 MB
    41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp4 11.02 MB
    20. Python - Other Python Operators/1. Comparison Operators.mp4 10.18 MB
    31. Advanced Statistical Methods - K-Means Clustering/7. Relationship between Clustering and Regression.mp4 9.93 MB
    24. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp4 9.48 MB
    41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp4 9.11 MB
    25. Python - Advanced Python Tools/3. Modules and Packages.mp4 8.5 MB
    22. Python - Python Functions/4. How to Use a Function within a Function.mp4 8.13 MB
    22. Python - Python Functions/1. Defining a Function in Python.mp4 7.74 MB
    22. Python - Python Functions/6. Functions Containing a Few Arguments.mp4 7.58 MB
    2. The Field of Data Science - The Various Data Science Disciplines/9.1 365_DataScience.png.png 6.93 MB
    2. The Field of Data Science - The Various Data Science Disciplines/7.2 365_DataScience.png.png 6.92 MB
    19. Python - Basic Python Syntax/12. Structuring with Indentation.mp4 6.81 MB
    19. Python - Basic Python Syntax/3. The Double Equality Sign.mp4 5.99 MB
    19. Python - Basic Python Syntax/10. Indexing Elements.mp4 5.94 MB
    27. Advanced Statistical Methods - Linear regression/5. Geometrical Representation of the Linear Regression Model.mp4 5.12 MB
    19. Python - Basic Python Syntax/7. Add Comments.mp4 5.01 MB
    19. Python - Basic Python Syntax/5. How to Reassign Values.mp4 4 MB
    19. Python - Basic Python Syntax/9. Understanding Line Continuation.mp4 2.35 MB
    14. Statistics - Practical Example Inferential Statistics/1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx 1.74 MB
    14. Statistics - Practical Example Inferential Statistics/2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx 1.74 MB
    14. Statistics - Practical Example Inferential Statistics/2.1 3.17. Practical example. Confidence intervals_exercise.xlsx.xlsx 1.73 MB
    15. Statistics - Hypothesis Testing/10.1 Online p-value calculator.pdf.pdf 1.22 MB
    38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1.1 Course Notes - Section 6.pdf.pdf 936.42 KB
    38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2.1 Course Notes - Section 6.pdf.pdf 936.42 KB
    35. Deep Learning - Introduction to Neural Networks/1.1 Course Notes - Section 2.pdf.pdf 927.67 KB
    35. Deep Learning - Introduction to Neural Networks/3.1 Course Notes - Section 2.pdf.pdf 927.67 KB
    44. Deep Learning - Business Case Example/1.1 Audiobooks_data.csv.csv 710.77 KB
    15. Statistics - Hypothesis Testing/4.1 Course notes_hypothesis_testing.pdf.pdf 658.6 KB
    15. Statistics - Hypothesis Testing/1.1 Course notes_hypothesis_testing.pdf.pdf 648.6 KB
    36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1.1 Shortcuts-for-Jupyter.pdf.pdf 619.17 KB
    37. Deep Learning - TensorFlow Introduction/1.1 Shortcuts-for-Jupyter.pdf.pdf 619.17 KB
    37. Deep Learning - TensorFlow Introduction/4.1 Shortcuts-for-Jupyter.pdf.pdf 619.17 KB
    10. Statistics - Descriptive Statistics/1.1 Course notes_descriptive_statistics.pdf.pdf 482.27 KB
    9. Part 2 Statistics/1.2 Course notes_descriptive_statistics.pdf.pdf 482.27 KB
    12. Statistics - Inferential Statistics Fundamentals/1.1 Course notes_inferential statistics.pdf.pdf 382.32 KB
    12. Statistics - Inferential Statistics Fundamentals/2.1 Course notes_inferential statistics.pdf.pdf 382.32 KB
    2. The Field of Data Science - The Various Data Science Disciplines/5.1 365_DataScience_Diagram.pdf.pdf 323.08 KB
    2. The Field of Data Science - The Various Data Science Disciplines/7.1 365_DataScience_Diagram.pdf.pdf 323.08 KB
    38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf 182.36 KB
    11. Statistics - Practical Example Descriptive Statistics/1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx 146.51 KB
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