[FreeTutorials.Us] Udemy - The Data Science Course 2018 Complete Data Science Bootcamp

Creation Time	May 29, 2024, 4:48 p.m.
Last Access Time	Nov. 4, 2024, 3:41 a.m.
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11. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4 159.5 MB
33. Part 5 Mathematics/16. Why is Linear Algebra Useful.mp4 144.3 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 126.9 MB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4 125.2 MB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4 123.5 MB
15. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4 113.2 MB
2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4 109.0 MB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4 103.5 MB
44. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4 103.4 MB
14. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4 102.7 MB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4 99.3 MB
15. Statistics - Hypothesis Testing/1. The Null vs Alternative Hypothesis.mp4 92.1 MB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4 89.9 MB
44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp4 87.7 MB
29. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4 86.5 MB
2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp4 81.4 MB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp4 81.2 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 76.3 MB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4 75.5 MB
17. Part 3 Introduction to Python/3. Why Python.mp4 75.1 MB
31. Advanced Statistical Methods - K-Means Clustering/10. How is Clustering Useful.mp4 74.5 MB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4 72.8 MB
10. Statistics - Descriptive Statistics/1. Types of Data.mp4 72.5 MB
30. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4 71.5 MB
13. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Dependent samples.mp4 70.5 MB
16. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp4 69.5 MB
2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp4 67.7 MB
2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 64.5 MB
12. Statistics - Inferential Statistics Fundamentals/8. Central Limit Theorem.mp4 62.9 MB
43. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp4 62.8 MB
1. Part 1 Introduction/2. What Does the Course Cover.mp4 62.3 MB
12. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4 61.6 MB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4 61.1 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp4 59.4 MB
13. Statistics - Inferential Statistics Confidence Intervals/9. Margin of Error.mp4 59.1 MB
45. Deep Learning - Conclusion/3. An overview of CNNs.mp4 58.8 MB
17. Part 3 Introduction to Python/1. Introduction to Programming.mp4 58.5 MB
9. Part 2 Statistics/1. Population and Sample.mp4 58.1 MB
27. Advanced Statistical Methods - Linear regression/1. The Linear Regression Model.mp4 57.4 MB
43. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp4 56.4 MB
31. Advanced Statistical Methods - K-Means Clustering/9. Market Segmentation with Cluster Analysis (Part 2).mp4 56.1 MB
15. Statistics - Hypothesis Testing/10. p-value.mp4 55.9 MB
28. Advanced Statistical Methods - Multiple Linear Regression/17. Dealing with Categorical Data - Dummy Variables.mp4 55.7 MB
35. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 55.6 MB
28. Advanced Statistical Methods - Multiple Linear Regression/2. Adjusted R-Squared.mp4 54.8 MB
17. Part 3 Introduction to Python/7. Installing Python and Jupyter.mp4 54.4 MB
10. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4 54.4 MB
7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp4 54.4 MB
15. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp4 54.2 MB
2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp4 53.6 MB
30. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp4 53.4 MB
44. Deep Learning - Business Case Example/7. Business Case Model Outline.mp4 53.1 MB
31. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp4 51.8 MB
42. Deep Learning - Preprocessing/3. Standardization.mp4 51.0 MB
10. Statistics - Descriptive Statistics/17. Variance.mp4 51.0 MB
15. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp4 50.4 MB
13. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp4 50.0 MB
12. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp4 49.9 MB
33. Part 5 Mathematics/5. Linear Algebra and Geometry.mp4 49.8 MB
27. Advanced Statistical Methods - Linear regression/11. Decomposition of Variability.mp4 49.7 MB
33. Part 5 Mathematics/15. Dot Product of Matrices.mp4 49.4 MB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp4 49.0 MB
12. Statistics - Inferential Statistics Fundamentals/11. Estimators and Estimates.mp4 47.8 MB
37. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.mp4 47.7 MB
43. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.mp4 46.7 MB
10. Statistics - Descriptive Statistics/19. Standard Deviation and Coefficient of Variation.mp4 45.1 MB
35. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp4 45.1 MB
45. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp4 44.8 MB
27. Advanced Statistical Methods - Linear regression/10. How to Interpret the Regression Table.mp4 44.6 MB
32. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp4 44.6 MB
27. Advanced Statistical Methods - Linear regression/7. First Regression in Python.mp4 44.6 MB
17. Part 3 Introduction to Python/5. Why Jupyter.mp4 44.3 MB
31. Advanced Statistical Methods - K-Means Clustering/4. How to Choose the Number of Clusters.mp4 44.1 MB
15. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp4 43.9 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 43.0 MB
35. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp4 42.9 MB
5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp4 42.8 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 41.5 MB
27. Advanced Statistical Methods - Linear regression/14. R-Squared.mp4 41.0 MB
27. Advanced Statistical Methods - Linear regression/6. Python Packages Installation.mp4 40.6 MB
15. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp4 40.2 MB
10. Statistics - Descriptive Statistics/11. Cross Table and Scatter Plot.mp4 39.8 MB
45. Deep Learning - Conclusion/1. Summary of What You Learned.mp4 39.8 MB
35. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp4 39.4 MB
44. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.mp4 39.4 MB
37. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 38.5 MB
10. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4 38.5 MB
29. Advanced Statistical Methods - Logistic Regression/8. Binary Predictors in a Logistic Regression.mp4 38.4 MB
35. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 38.3 MB
33. Part 5 Mathematics/13. Transpose of a Matrix.mp4 38.1 MB
31. Advanced Statistical Methods - K-Means Clustering/5. Pros and Cons of K-Means Clustering.mp4 37.7 MB
37. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.mp4 37.4 MB
35. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp4 37.2 MB
10. Statistics - Descriptive Statistics/13. Mean, median and mode.mp4 37.1 MB
5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp4 36.8 MB
44. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp4 36.4 MB
15. Statistics - Hypothesis Testing/17. Test for the mean. Independent samples (Part 2).mp4 36.4 MB
30. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp4 36.2 MB
28. Advanced Statistical Methods - Multiple Linear Regression/10. A2 No Endogeneity.mp4 35.7 MB
13. Statistics - Inferential Statistics Confidence Intervals/5. Student's T Distribution.mp4 35.4 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp4 35.0 MB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4 34.9 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 33.8 MB
25. Python - Advanced Python Tools/1. Object Oriented Programming.mp4 33.6 MB
33. Part 5 Mathematics/1. What is a matrix.mp4 33.6 MB
21. Python - Conditional Statements/4. The ELIF Statement.mp4 33.2 MB
29. Advanced Statistical Methods - Logistic Regression/9. Calculating the Accuracy of the Model.mp4 32.9 MB
39. Deep Learning - Overfitting/3. What is Validation.mp4 32.7 MB
33. Part 5 Mathematics/10. Addition and Subtraction of Matrices.mp4 32.6 MB
37. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.mp4 32.5 MB
29. Advanced Statistical Methods - Logistic Regression/7. What do the Odds Actually Mean.mp4 32.3 MB
29. Advanced Statistical Methods - Logistic Regression/11. Testing the Model.mp4 32.3 MB
13. Statistics - Inferential Statistics Confidence Intervals/7. Confidence Intervals; Population Variance Unknown; t-score.mp4 32.2 MB
28. Advanced Statistical Methods - Multiple Linear Regression/13. A4 No Autocorrelation.mp4 31.5 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 31.1 MB
23. Python - Sequences/5. List Slicing.mp4 30.8 MB
18. Python - Variables and Data Types/5. Python Strings.mp4 30.8 MB
17. Part 3 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp4 30.6 MB
29. Advanced Statistical Methods - Logistic Regression/6. Understanding Logistic Regression Tables.mp4 30.5 MB
31. Advanced Statistical Methods - K-Means Clustering/6. To Standardize or to not Standardize.mp4 30.1 MB
20. Python - Other Python Operators/3. Logical and Identity Operators.mp4 30.1 MB
15. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp4 30.0 MB
5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp4 29.9 MB
32. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4 29.6 MB
10. Statistics - Descriptive Statistics/23. Correlation Coefficient.mp4 29.6 MB
5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4 29.5 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp4 29.5 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 29.1 MB
32. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp4 29.1 MB
42. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp4 29.0 MB
13. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Independent samples (Part 1).mp4 28.8 MB
28. Advanced Statistical Methods - Multiple Linear Regression/15. A5 No Multicollinearity.mp4 28.7 MB
35. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4 28.7 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp4 28.7 MB
35. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp4 28.4 MB
27. Advanced Statistical Methods - Linear regression/13. What is the OLS.mp4 28.3 MB
42. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp4 27.8 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp4 27.7 MB
10. Statistics - Descriptive Statistics/21. Covariance.mp4 27.5 MB
31. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp4 27.3 MB
29. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp4 27.1 MB
13. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent samples (Part 2).mp4 26.8 MB
18. Python - Variables and Data Types/1. Variables.mp4 26.6 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules ( AdaGrad and RMSprop ).mp4 26.3 MB
33. Part 5 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 26.1 MB
10. Statistics - Descriptive Statistics/7. Numerical Variables - Frequency Distribution Table.mp4 26.0 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp4 25.9 MB
43. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp4 25.9 MB
44. Deep Learning - Business Case Example/9. Business Case Interpretation.mp4 25.7 MB
45. Deep Learning - Conclusion/5. An Overview of RNNs.mp4 25.3 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 25.1 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 25.1 MB
23. Python - Sequences/7. Dictionaries.mp4 25.0 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 24.2 MB
33. Part 5 Mathematics/14. Dot Product.mp4 24.0 MB
22. Python - Python Functions/2. How to Create a Function with a Parameter.mp4 23.9 MB
35. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp4 23.3 MB
29. Advanced Statistical Methods - Logistic Regression/5. An Invaluable Coding Tip.mp4 23.1 MB
12. Statistics - Inferential Statistics Fundamentals/10. Standard error.mp4 22.8 MB
35. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp4 22.6 MB
43. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp4 22.6 MB
33. Part 5 Mathematics/8. What is a Tensor.mp4 22.5 MB
12. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp4 22.5 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp4 22.4 MB
29. Advanced Statistical Methods - Logistic Regression/10. Underfitting and Overfitting.mp4 22.3 MB
5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp4 22.0 MB
22. Python - Python Functions/7. Built-in Functions in Python.mp4 22.0 MB
23. Python - Sequences/1. Lists.mp4 22.0 MB
23. Python - Sequences/3. Using Methods.mp4 22.0 MB
28. Advanced Statistical Methods - Multiple Linear Regression/6. OLS Assumptions.mp4 21.8 MB
40. Deep Learning - Initialization/1. What is Initialization.mp4 21.8 MB
28. Advanced Statistical Methods - Multiple Linear Regression/1. Multiple Linear Regression.mp4 21.5 MB
31. Advanced Statistical Methods - K-Means Clustering/3. Clustering Categorical Data.mp4 21.2 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 20.3 MB
45. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp4 20.1 MB
13. Statistics - Inferential Statistics Confidence Intervals/17. Confidence intervals. Two means. Independent samples (Part 3).mp4 19.9 MB
25. Python - Advanced Python Tools/7. Importing Modules in Python.mp4 19.9 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.mp4 19.5 MB
10. Statistics - Descriptive Statistics/15. Skewness.mp4 19.4 MB
19. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp4 18.9 MB
43. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp4 18.9 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 18.0 MB
35. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp4 17.9 MB
43. Deep Learning - Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp4 17.8 MB
37. Deep Learning - TensorFlow Introduction/4. Actual Introduction to TensorFlow.mp4 17.4 MB
26. Part 4 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp4 17.3 MB
40. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 17.1 MB
29. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp4 17.1 MB
18. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp4 17.1 MB
24. Python - Iterations/8. How to Iterate over Dictionaries.mp4 17.0 MB
23. Python - Sequences/6. Tuples.mp4 16.7 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp4 16.4 MB
28. Advanced Statistical Methods - Multiple Linear Regression/5. Test for Significance of the Model (F-Test).mp4 16.4 MB
24. Python - Iterations/6. Conditional Statements and Loops.mp4 16.1 MB
22. Python - Python Functions/5. Conditional Statements and Functions.mp4 15.7 MB
12. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp4 15.5 MB
24. Python - Iterations/3. While Loops and Incrementing.mp4 15.4 MB
22. Python - Python Functions/3. Defining a Function in Python - Part II.mp4 14.8 MB
27. Advanced Statistical Methods - Linear regression/3. Correlation vs Regression.mp4 14.7 MB
37. Deep Learning - TensorFlow Introduction/1. How to Install TensorFlow.mp4 14.6 MB
30. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp4 14.5 MB
40. Deep Learning - Initialization/2. Types of Simple Initializations.mp4 14.3 MB
17. Part 3 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 13.8 MB
10. Statistics - Descriptive Statistics/9. The Histogram.mp4 13.8 MB
21. Python - Conditional Statements/1. The IF Statement.mp4 13.6 MB
21. Python - Conditional Statements/3. The ELSE Statement.mp4 13.6 MB
43. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp4 12.9 MB
28. Advanced Statistical Methods - Multiple Linear Regression/8. A1 Linearity.mp4 12.6 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 12.2 MB
44. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp4 12.2 MB
42. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp4 11.8 MB
24. Python - Iterations/1. For Loops.mp4 11.8 MB
24. Python - Iterations/4. Lists with the range() Function.mp4 11.4 MB
21. Python - Conditional Statements/5. A Note on Boolean Values.mp4 11.3 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 11.2 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp4 11.0 MB
20. Python - Other Python Operators/1. Comparison Operators.mp4 10.2 MB
31. Advanced Statistical Methods - K-Means Clustering/7. Relationship between Clustering and Regression.mp4 9.9 MB
24. Python - Iterations/7. Conditional Statements, Functions, and Loops.mp4 9.5 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/5. Learning Rate Schedules Visualized.mp4 9.1 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 8.1 MB
22. Python - Python Functions/1. Defining a Function in Python.mp4 7.7 MB
22. Python - Python Functions/6. Functions Containing a Few Arguments.mp4 7.6 MB
2. The Field of Data Science - The Various Data Science Disciplines/9.1 365_DataScience.png.png 6.9 MB
2. The Field of Data Science - The Various Data Science Disciplines/7.2 365_DataScience.png.png 6.9 MB
19. Python - Basic Python Syntax/12. Structuring with Indentation.mp4 6.8 MB
19. Python - Basic Python Syntax/3. The Double Equality Sign.mp4 6.0 MB
19. Python - Basic Python Syntax/10. Indexing Elements.mp4 5.9 MB
27. Advanced Statistical Methods - Linear regression/5. Geometrical Representation of the Linear Regression Model.mp4 5.1 MB
19. Python - Basic Python Syntax/7. Add Comments.mp4 5.0 MB
19. Python - Basic Python Syntax/5. How to Reassign Values.mp4 4.0 MB
19. Python - Basic Python Syntax/9. Understanding Line Continuation.mp4 2.4 MB
14. Statistics - Practical Example Inferential Statistics/1.1 3.17. Practical example. Confidence intervals_lesson.xlsx.xlsx 1.7 MB
14. Statistics - Practical Example Inferential Statistics/2.2 3.17.Practical-example.Confidence-intervals-exercise-solution.xlsx.xlsx 1.7 MB
14. Statistics - Practical Example Inferential Statistics/2.1 3.17. Practical example. Confidence intervals_exercise.xlsx.xlsx 1.7 MB
15. Statistics - Hypothesis Testing/10.1 Online p-value calculator.pdf.pdf 1.2 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1.1 Course Notes - Section 6.pdf.pdf 936.4 KB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2.1 Course Notes - Section 6.pdf.pdf 936.4 KB
35. Deep Learning - Introduction to Neural Networks/1.1 Course Notes - Section 2.pdf.pdf 927.7 KB
35. Deep Learning - Introduction to Neural Networks/3.1 Course Notes - Section 2.pdf.pdf 927.7 KB
44. Deep Learning - Business Case Example/1.1 Audiobooks_data.csv.csv 710.8 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 619.2 KB
37. Deep Learning - TensorFlow Introduction/1.1 Shortcuts-for-Jupyter.pdf.pdf 619.2 KB
37. Deep Learning - TensorFlow Introduction/4.1 Shortcuts-for-Jupyter.pdf.pdf 619.2 KB
10. Statistics - Descriptive Statistics/1.1 Course notes_descriptive_statistics.pdf.pdf 482.3 KB
9. Part 2 Statistics/1.2 Course notes_descriptive_statistics.pdf.pdf 482.3 KB
12. Statistics - Inferential Statistics Fundamentals/1.1 Course notes_inferential statistics.pdf.pdf 382.3 KB
12. Statistics - Inferential Statistics Fundamentals/2.1 Course notes_inferential statistics.pdf.pdf 382.3 KB
2. The Field of Data Science - The Various Data Science Disciplines/5.1 365_DataScience_Diagram.pdf.pdf 323.1 KB
2. The Field of Data Science - The Various Data Science Disciplines/7.1 365_DataScience_Diagram.pdf.pdf 323.1 KB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/9.1 Backpropagation-a-peek-into-the-Mathematics-of-Optimization.pdf.pdf 182.4 KB
11. Statistics - Practical Example Descriptive Statistics/1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx 146.5 KB
11. Statistics - Practical Example Descriptive Statistics/2.1 2.13. Practical-example.Descriptive-statistics-exercise-solution.xlsx.xlsx 146.2 KB
11. Statistics - Practical Example Descriptive Statistics/2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx.xlsx 120.2 KB
16. Statistics - Practical Example Hypothesis Testing/1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx 51.7 KB
16. Statistics - Practical Example Hypothesis Testing/2.2 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx.xlsx 44.0 KB
16. Statistics - Practical Example Hypothesis Testing/2.1 4.10. Hypothesis testing section_practical example_exercise.xlsx.xlsx 43.4 KB
35. Deep Learning - Introduction to Neural Networks/21.1 GD-function-example.xlsx.xlsx 42.2 KB
10. Statistics - Descriptive Statistics/6.1 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx 41.1 KB
10. Statistics - Descriptive Statistics/12.1 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx 40.4 KB
10. Statistics - Descriptive Statistics/15.1 2.8. Skewness_lesson.xlsx.xlsx 34.6 KB
10. Statistics - Descriptive Statistics/5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx.xlsx 30.8 KB
10. Statistics - Descriptive Statistics/22.2 2.11. Covariance_exercise_solution.xlsx.xlsx 29.5 KB
10. Statistics - Descriptive Statistics/24.2 2.12. Correlation_exercise_solution.xlsx.xlsx 29.5 KB
10. Statistics - Descriptive Statistics/24.1 2.12. Correlation_exercise.xlsx.xlsx 29.3 KB
10. Statistics - Descriptive Statistics/11.1 2.6. Cross table and scatter plot.xlsx.xlsx 26.1 KB
10. Statistics - Descriptive Statistics/21.1 2.11. Covariance_lesson.xlsx.xlsx 24.9 KB
12. Statistics - Inferential Statistics Fundamentals/7.2 3.4. Standard normal distribution_exercise_solution.xlsx.xlsx 23.7 KB
11. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.srt 20.6 KB
10. Statistics - Descriptive Statistics/22.1 2.11. Covariance_exercise.xlsx.xlsx 20.2 KB
9. Part 2 Statistics/1.1 Glossary.xlsx.xlsx 20.0 KB
10. Statistics - Descriptive Statistics/16.2 2.8. Skewness_exercise_solution.xlsx.xlsx 19.8 KB
12. Statistics - Inferential Statistics Fundamentals/2.2 3.2. What is a distribution_lesson.xlsx.xlsx 19.5 KB
10. Statistics - Descriptive Statistics/9.1 2.5. The Histogram_lesson.xlsx.xlsx 18.6 KB
13. Statistics - Inferential Statistics Confidence Intervals/3.2 3.9. The z-table.xlsx.xlsx 18.5 KB
13. Statistics - Inferential Statistics Confidence Intervals/4.2 3.9. The z-table.xlsx.xlsx 18.5 KB
11. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.vtt 17.9 KB
10. Statistics - Descriptive Statistics/10.2 2.5.The-Histogram-exercise-solution.xlsx.xlsx 17.1 KB
10. Statistics - Descriptive Statistics/12.2 2.6. Cross table and scatter plot_exercise.xlsx.xlsx 16.3 KB
13. Statistics - Inferential Statistics Confidence Intervals/7.2 3.11. The t-table.xlsx.xlsx 15.8 KB
10. Statistics - Descriptive Statistics/10.1 2.5.The-Histogram-exercise.xlsx.xlsx 15.5 KB
10. Statistics - Descriptive Statistics/6.2 2.3. Categorical variables. Visualization techniques_exercise.xlsx.xlsx 15.2 KB
15. Statistics - Hypothesis Testing/12.1 4.6.Test-for-the-mean.Population-variance-unknown-lesson.xlsx.xlsx 14.5 KB
15. Statistics - Hypothesis Testing/15.2 4.7. Test for the mean. Dependent samples_exercise_solution.xlsx.xlsx 14.4 KB
13. Statistics - Inferential Statistics Confidence Intervals/12.1 3.13. Confidence intervals. Two means. Dependent samples_exercise_solution.xlsx.xlsx 14.2 KB
13. Statistics - Inferential Statistics Confidence Intervals/12.2 3.13. Confidence intervals. Two means. Dependent samples_exercise.xlsx.xlsx 13.7 KB
14. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.srt 13.6 KB
44. Deep Learning - Business Case Example/4. Business Case Preprocessing.srt 13.5 KB
10. Statistics - Descriptive Statistics/8.1 2.4. Numerical variables. Frequency distribution table_exercise_solution.xlsx.xlsx 13.2 KB
15. Statistics - Hypothesis Testing/15.1 4.7. Test for the mean. Dependent samples_exercise.xlsx.xlsx 12.8 KB
10. Statistics - Descriptive Statistics/20.1 2.10. Standard deviation and coefficient of variation_exercise_solution.xlsx.xlsx 12.4 KB
14. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.vtt 11.9 KB
2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.srt 11.9 KB
15. Statistics - Hypothesis Testing/13.1 4.6.Test-for-the-mean.Population-variance-unknown-exercise-solution.xlsx.xlsx 11.9 KB
12. Statistics - Inferential Statistics Fundamentals/7.1 3.4. Standard normal distribution_exercise.xlsx.xlsx 11.8 KB
33. Part 5 Mathematics/16. Why is Linear Algebra Useful.srt 11.8 KB
10. Statistics - Descriptive Statistics/8.2 2.4. Numerical variables. Frequency distribution table_exercise.xlsx.xlsx 11.7 KB
44. Deep Learning - Business Case Example/4. Business Case Preprocessing.vtt 11.7 KB
10. Statistics - Descriptive Statistics/14.1 2.7. Mean, median and mode_exercise_solution.xlsx.xlsx 11.4 KB
15. Statistics - Hypothesis Testing/13.2 4.6.Test-for-the-mean.Population-variance-unknown-exercise.xlsx.xlsx 11.3 KB
10. Statistics - Descriptive Statistics/7.1 2.4. Numerical variables. Frequency distribution table_lesson.xlsx.xlsx 11.3 KB
10. Statistics - Descriptive Statistics/20.2 2.10. Standard deviation and coefficient of variation_exercise.xlsx.xlsx 11.3 KB
15. Statistics - Hypothesis Testing/9.2 4.4. Test for the mean. Population variance known_exercise_solution.xlsx.xlsx 11.2 KB
13. Statistics - Inferential Statistics Confidence Intervals/3.1 3.9. Population variance known, z-score_lesson.xlsx.xlsx 11.2 KB
13. Statistics - Inferential Statistics Confidence Intervals/4.3 3.9. Population variance known, z-score_exercise_solution.xlsx.xlsx 11.2 KB
13. Statistics - Inferential Statistics Confidence Intervals/8.2 3.11. Population variance unknown, t-score_exercise_solution.xlsx.xlsx 11.1 KB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.srt 11.1 KB
10. Statistics - Descriptive Statistics/18.2 2.9. Variance_exercise_solution.xlsx.xlsx 11.1 KB
15. Statistics - Hypothesis Testing/9.1 4.4. Test for the mean. Population variance known_exercise.xlsx.xlsx 11.0 KB
10. Statistics - Descriptive Statistics/19.1 2.10. Standard deviation and coefficient of variation_lesson.xlsx.xlsx 11.0 KB
15. Statistics - Hypothesis Testing/8.1 4.4. Test for the mean. Population variance known_lesson.xlsx.xlsx 11.0 KB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).srt 10.9 KB
10. Statistics - Descriptive Statistics/14.2 2.7. Mean, median and mode_exercise.xlsx.xlsx 10.9 KB
13. Statistics - Inferential Statistics Confidence Intervals/4.1 3.9. Population variance known, z-score_exercise.xlsx.xlsx 10.8 KB
10. Statistics - Descriptive Statistics/18.1 2.9. Variance_exercise.xlsx.xlsx 10.8 KB
44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.srt 10.8 KB
13. Statistics - Inferential Statistics Confidence Intervals/7.1 3.11. Population variance unknown, t-score_lesson.xlsx.xlsx 10.8 KB
2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.srt 10.6 KB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.srt 10.6 KB
13. Statistics - Inferential Statistics Confidence Intervals/8.1 3.11. Population variance unknown, t-score_exercise.xlsx.xlsx 10.6 KB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.srt 10.5 KB
10. Statistics - Descriptive Statistics/13.1 2.7. Mean, median and mode_lesson.xlsx.xlsx 10.5 KB
13. Statistics - Inferential Statistics Confidence Intervals/11.1 3.13. Confidence intervals. Two means. Dependent samples_lesson.xlsx.xlsx 10.5 KB
2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.vtt 10.4 KB
12. Statistics - Inferential Statistics Fundamentals/6.1 3.4. Standard normal distribution_lesson.xlsx.xlsx 10.4 KB
33. Part 5 Mathematics/16. Why is Linear Algebra Useful.vtt 10.3 KB
15. Statistics - Hypothesis Testing/18.2 4.9. Test for the mean. Independent samples (Part 2)_exercise_solution.xlsx.xlsx 10.2 KB
43. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.srt 10.2 KB
13. Statistics - Inferential Statistics Confidence Intervals/14.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_exercise_solution.xlsx.xlsx 10.1 KB
10. Statistics - Descriptive Statistics/17.1 2.9. Variance_lesson.xlsx.xlsx 10.1 KB
13. Statistics - Inferential Statistics Confidence Intervals/13.1 3.14. Confidence intervals. Two means. Independent samples (Part 1)_lesson.xlsx.xlsx 9.8 KB
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13. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.srt 9.8 KB
13. Statistics - Inferential Statistics Confidence Intervals/16.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise_solution.xlsx.xlsx 9.8 KB
15. Statistics - Hypothesis Testing/14.1 4.7. Test for the mean. Dependent samples_lesson.xlsx.xlsx 9.8 KB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.vtt 9.7 KB
15. Statistics - Hypothesis Testing/16.1 4.8. Test for the mean. Independent samples (Part 1)_lesson.xlsx.xlsx 9.6 KB
31. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.srt 9.6 KB
33. Part 5 Mathematics/15. Dot Product of Matrices.srt 9.5 KB
13. Statistics - Inferential Statistics Confidence Intervals/15.1 3.15. Confidence intervals. Two means. Independent samples (Part 2)_lesson.xlsx.xlsx 9.5 KB
10. Statistics - Descriptive Statistics/16.1 2.8. Skewness_exercise.xlsx.xlsx 9.5 KB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).vtt 9.5 KB
15. Statistics - Hypothesis Testing/18.1 4.9. Test for the mean. Independent samples (Part 2)_exercise.xlsx.xlsx 9.5 KB
44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.vtt 9.4 KB
15. Statistics - Hypothesis Testing/17.1 4.9. Test for the mean. Independent samples (Part 2)_lesson.xlsx.xlsx 9.3 KB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.vtt 9.3 KB
2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.vtt 9.3 KB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.vtt 9.2 KB
31. Advanced Statistical Methods - K-Means Clustering/9. Market Segmentation with Cluster Analysis (Part 2).srt 9.2 KB
13. Statistics - Inferential Statistics Confidence Intervals/16.2 3.15. Confidence intervals. Two means. Independent samples (Part 2)_exercise.xlsx.xlsx 9.2 KB
43. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.srt 9.1 KB
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15. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.srt 9.0 KB
43. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.vtt 8.9 KB
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13. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.vtt 8.6 KB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.srt 8.6 KB
16. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.srt 8.5 KB
35. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.srt 8.5 KB
31. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.vtt 8.3 KB
33. Part 5 Mathematics/15. Dot Product of Matrices.vtt 8.2 KB
43. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.srt 8.2 KB
28. Advanced Statistical Methods - Multiple Linear Regression/17. Dealing with Categorical Data - Dummy Variables.srt 8.2 KB
15. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.srt 8.1 KB
13. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Dependent samples.srt 8.0 KB
31. Advanced Statistical Methods - K-Means Clustering/9. Market Segmentation with Cluster Analysis (Part 2).vtt 8.0 KB
37. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.srt 7.9 KB
43. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.vtt 7.9 KB
27. Advanced Statistical Methods - Linear regression/7. First Regression in Python.srt 7.9 KB
3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.vtt 7.9 KB
15. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.vtt 7.8 KB
17. Part 3 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.srt 7.8 KB
44. Deep Learning - Business Case Example/6. Creating a Data Provider.srt 7.8 KB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.vtt 7.7 KB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.vtt 7.6 KB
10. Statistics - Descriptive Statistics/17. Variance.srt 7.5 KB
28. Advanced Statistical Methods - Multiple Linear Regression/2. Adjusted R-Squared.srt 7.5 KB
31. Advanced Statistical Methods - K-Means Clustering/8. Market Segmentation with Cluster Analysis (Part 1).srt 7.5 KB
35. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.srt 7.5 KB
18. Python - Variables and Data Types/5. Python Strings.srt 7.5 KB
35. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.vtt 7.4 KB
16. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.vtt 7.4 KB
31. Advanced Statistical Methods - K-Means Clustering/4. How to Choose the Number of Clusters.srt 7.4 KB
37. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.srt 7.4 KB
32. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.srt 7.4 KB
15. Statistics - Hypothesis Testing/1. The Null vs Alternative Hypothesis.srt 7.4 KB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.srt 7.3 KB
43. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.vtt 7.1 KB
17. Part 3 Introduction to Python/7. Installing Python and Jupyter.srt 7.1 KB
15. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.vtt 7.1 KB
28. Advanced Statistical Methods - Multiple Linear Regression/17. Dealing with Categorical Data - Dummy Variables.vtt 7.1 KB
13. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Dependent samples.vtt 7.1 KB
27. Advanced Statistical Methods - Linear regression/1. The Linear Regression Model.srt 7.1 KB
17. Part 3 Introduction to Python/3. Why Python.srt 7.0 KB
44. Deep Learning - Business Case Example/7. Business Case Model Outline.srt 6.9 KB
27. Advanced Statistical Methods - Linear regression/7. First Regression in Python.vtt 6.9 KB
17. Part 3 Introduction to Python/1. Introduction to Programming.srt 6.9 KB
37. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.vtt 6.9 KB
39. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.srt 6.9 KB
17. Part 3 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.vtt 6.8 KB
44. Deep Learning - Business Case Example/6. Creating a Data Provider.vtt 6.8 KB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).srt 6.8 KB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.srt 6.7 KB
15. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.srt 6.7 KB
10. Statistics - Descriptive Statistics/11. Cross Table and Scatter Plot.srt 6.7 KB
28. Advanced Statistical Methods - Multiple Linear Regression/12. A3 Normality and Homoscedasticity.srt 6.7 KB
31. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.srt 6.7 KB
21. Python - Conditional Statements/4. The ELIF Statement.srt 6.7 KB
10. Statistics - Descriptive Statistics/17. Variance.vtt 6.6 KB
2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.srt 6.6 KB
35. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.vtt 6.6 KB
10. Statistics - Descriptive Statistics/19. Standard Deviation and Coefficient of Variation.srt 6.6 KB
44. Deep Learning - Business Case Example/8. Business Case Optimization.srt 6.6 KB
27. Advanced Statistical Methods - Linear regression/14. R-Squared.srt 6.6 KB
28. Advanced Statistical Methods - Multiple Linear Regression/2. Adjusted R-Squared.vtt 6.6 KB
29. Advanced Statistical Methods - Logistic Regression/11. Testing the Model.srt 6.6 KB
31. Advanced Statistical Methods - K-Means Clustering/8. Market Segmentation with Cluster Analysis (Part 1).vtt 6.5 KB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.srt 6.5 KB
37. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.vtt 6.5 KB
18. Python - Variables and Data Types/5. Python Strings.vtt 6.5 KB
45. Deep Learning - Conclusion/3. An overview of CNNs.srt 6.4 KB
15. Statistics - Hypothesis Testing/1. The Null vs Alternative Hypothesis.vtt 6.4 KB
10. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.srt 6.4 KB
31. Advanced Statistical Methods - K-Means Clustering/4. How to Choose the Number of Clusters.vtt 6.4 KB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.vtt 6.4 KB
32. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.vtt 6.4 KB
31. Advanced Statistical Methods - K-Means Clustering/10. How is Clustering Useful.srt 6.4 KB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.srt 6.4 KB
32. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.srt 6.3 KB
27. Advanced Statistical Methods - Linear regression/10. How to Interpret the Regression Table.srt 6.3 KB
30. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.srt 6.2 KB
17. Part 3 Introduction to Python/7. Installing Python and Jupyter.vtt 6.2 KB
13. Statistics - Inferential Statistics Confidence Intervals/9. Margin of Error.srt 6.2 KB
18. Python - Variables and Data Types/1. Variables.srt 6.2 KB
27. Advanced Statistical Methods - Linear regression/1. The Linear Regression Model.vtt 6.1 KB
17. Part 3 Introduction to Python/3. Why Python.vtt 6.1 KB
25. Python - Advanced Python Tools/1. Object Oriented Programming.srt 6.1 KB
17. Part 3 Introduction to Python/1. Introduction to Programming.vtt 6.1 KB
13. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Independent samples (Part 1).srt 6.1 KB
44. Deep Learning - Business Case Example/7. Business Case Model Outline.vtt 6.1 KB
39. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.vtt 6.0 KB
42. Deep Learning - Preprocessing/3. Standardization.srt 6.0 KB
10. Statistics - Descriptive Statistics/1. Types of Data.srt 6.0 KB
33. Part 5 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.srt 5.9 KB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.srt 5.9 KB
15. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.srt 5.9 KB
35. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.srt 5.9 KB
31. Advanced Statistical Methods - K-Means Clustering/6. To Standardize or to not Standardize.srt 5.9 KB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).vtt 5.9 KB
10. Statistics - Descriptive Statistics/11. Cross Table and Scatter Plot.vtt 5.9 KB
12. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.srt 5.9 KB
15. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.vtt 5.9 KB
2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.vtt 5.8 KB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.vtt 5.8 KB
28. Advanced Statistical Methods - Multiple Linear Regression/12. A3 Normality and Homoscedasticity.vtt 5.8 KB
29. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.srt 5.8 KB
27. Advanced Statistical Methods - Linear regression/14. R-Squared.vtt 5.8 KB
20. Python - Other Python Operators/3. Logical and Identity Operators.srt 5.8 KB
44. Deep Learning - Business Case Example/8. Business Case Optimization.vtt 5.8 KB
31. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.vtt 5.8 KB
10. Statistics - Descriptive Statistics/19. Standard Deviation and Coefficient of Variation.vtt 5.8 KB
21. Python - Conditional Statements/4. The ELIF Statement.vtt 5.8 KB
10. Statistics - Descriptive Statistics/13. Mean, median and mode.srt 5.7 KB
13. Statistics - Inferential Statistics Confidence Intervals/7. Confidence Intervals; Population Variance Unknown; t-score.srt 5.7 KB
29. Advanced Statistical Methods - Logistic Regression/11. Testing the Model.vtt 5.7 KB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.vtt 5.7 KB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.srt 5.7 KB
15. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.srt 5.7 KB
10. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.vtt 5.7 KB
45. Deep Learning - Conclusion/3. An overview of CNNs.vtt 5.7 KB
31. Advanced Statistical Methods - K-Means Clustering/10. How is Clustering Useful.vtt 5.7 KB
12. Statistics - Inferential Statistics Fundamentals/8. Central Limit Theorem.srt 5.6 KB
27. Advanced Statistical Methods - Linear regression/6. Python Packages Installation.srt 5.6 KB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.vtt 5.6 KB
39. Deep Learning - Overfitting/1. What is Overfitting.srt 5.6 KB
29. Advanced Statistical Methods - Logistic Regression/6. Understanding Logistic Regression Tables.srt 5.6 KB
23. Python - Sequences/5. List Slicing.srt 5.6 KB
15. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).srt 5.5 KB
27. Advanced Statistical Methods - Linear regression/10. How to Interpret the Regression Table.vtt 5.5 KB
9. Part 2 Statistics/1. Population and Sample.srt 5.5 KB
32. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.vtt 5.5 KB
35. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.srt 5.5 KB
13. Statistics - Inferential Statistics Confidence Intervals/9. Margin of Error.vtt 5.4 KB
15. Statistics - Hypothesis Testing/17. Test for the mean. Independent samples (Part 2).srt 5.4 KB
30. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.vtt 5.4 KB
29. Advanced Statistical Methods - Logistic Regression/8. Binary Predictors in a Logistic Regression.srt 5.4 KB
33. Part 5 Mathematics/13. Transpose of a Matrix.srt 5.4 KB
18. Python - Variables and Data Types/1. Variables.vtt 5.4 KB
25. Python - Advanced Python Tools/1. Object Oriented Programming.vtt 5.3 KB
13. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Independent samples (Part 1).vtt 5.3 KB
44. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.srt 5.3 KB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.srt 5.3 KB
42. Deep Learning - Preprocessing/3. Standardization.vtt 5.3 KB
35. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.srt 5.3 KB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.srt 5.3 KB
10. Statistics - Descriptive Statistics/1. Types of Data.vtt 5.2 KB
28. Advanced Statistical Methods - Multiple Linear Regression/10. A2 No Endogeneity.srt 5.2 KB
35. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.srt 5.2 KB
45. Deep Learning - Conclusion/1. Summary of What You Learned.srt 5.2 KB
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37. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.srt 5.2 KB
43. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.srt 5.2 KB
15. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.vtt 5.2 KB
35. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.vtt 5.2 KB
33. Part 5 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.vtt 5.1 KB
31. Advanced Statistical Methods - K-Means Clustering/6. To Standardize or to not Standardize.vtt 5.1 KB
45. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.srt 5.1 KB
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2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.srt 5.1 KB
12. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.vtt 5.1 KB
29. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.vtt 5.0 KB
15. Statistics - Hypothesis Testing/10. p-value.srt 5.0 KB
13. Statistics - Inferential Statistics Confidence Intervals/7. Confidence Intervals; Population Variance Unknown; t-score.vtt 5.0 KB
10. Statistics - Descriptive Statistics/13. Mean, median and mode.vtt 5.0 KB
20. Python - Other Python Operators/3. Logical and Identity Operators.vtt 5.0 KB
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