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Description

[UDEMY] THE DATA SCIENCE COURSE 2018: COMPLETE DATA SCIENCE BOOTCAMP [FTU]

Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

Best Seller

Created by 365 Careers

Last updated 8/2018

English

What Will I Learn?

The course provides the entire toolbox you need to become a data scientist

Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow

Impress interviewers by showing an understanding of the data science field

Learn how to pre-process data

Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)

Start coding in Python and learn how to use it for statistical analysis

Perform linear and logistic regressions in Python

Carry out cluster and factor analysis

Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn

Apply your skills to real-life business cases

Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data

Unfold the power of deep neural networks

Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance

Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations

For More Udemy Free Courses >>> http://www.freetutorials.us
For more Lynda and other Courses >>> https://www.freecoursesonline.me/


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Files

11. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4 160 MB
33. Part 5 Mathematics/16. Why is Linear Algebra Useful.mp4 144 MB
5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4 138 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 127 MB
5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4 125 MB
5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4 124 MB
15. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4 113 MB
2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4 109 MB
6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4 104 MB
44. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4 103 MB
14. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4 103 MB
5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4 99 MB
15. Statistics - Hypothesis Testing/1. The Null vs Alternative Hypothesis.mp4 92 MB
5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4 90 MB
44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp4 88 MB
29. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4 86 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 MB
4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp4 81 MB
13. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp4 78 MB
44. Deep Learning - Business Case Example/6. Creating a Data Provider.mp4 76 MB
5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4 76 MB
17. Part 3 Introduction to Python/3. Why Python.mp4 75 MB
31. Advanced Statistical Methods - K-Means Clustering/10. How is Clustering Useful.mp4 74 MB
8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4 73 MB
10. Statistics - Descriptive Statistics/1. Types of Data.mp4 72 MB
30. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4 72 MB
13. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Dependent samples.mp4 70 MB
16. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp4 70 MB
2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp4 68 MB
2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 64 MB
12. Statistics - Inferential Statistics Fundamentals/8. Central Limit Theorem.mp4 63 MB
43. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp4 63 MB
1. Part 1 Introduction/2. What Does the Course Cover.mp4 62 MB
12. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4 62 MB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4 61 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp4 59 MB
13. Statistics - Inferential Statistics Confidence Intervals/9. Margin of Error.mp4 59 MB
45. Deep Learning - Conclusion/3. An overview of CNNs.mp4 59 MB
17. Part 3 Introduction to Python/1. Introduction to Programming.mp4 58 MB
9. Part 2 Statistics/1. Population and Sample.mp4 58 MB
27. Advanced Statistical Methods - Linear regression/1. The Linear Regression Model.mp4 57 MB
43. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp4 56 MB
31. Advanced Statistical Methods - K-Means Clustering/9. Market Segmentation with Cluster Analysis (Part 2).mp4 56 MB
15. Statistics - Hypothesis Testing/10. p-value.mp4 56 MB
28. Advanced Statistical Methods - Multiple Linear Regression/17. Dealing with Categorical Data - Dummy Variables.mp4 56 MB
35. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 56 MB
28. Advanced Statistical Methods - Multiple Linear Regression/2. Adjusted R-Squared.mp4 55 MB
17. Part 3 Introduction to Python/7. Installing Python and Jupyter.mp4 54 MB
10. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4 54 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 MB
15. Statistics - Hypothesis Testing/8. Test for the Mean. Population Variance Known.mp4 54 MB
2. The Field of Data Science - The Various Data Science Disciplines/3. What is the difference between Analysis and Analytics.mp4 54 MB
30. Advanced Statistical Methods - Cluster Analysis/1. Introduction to Cluster Analysis.mp4 53 MB
44. Deep Learning - Business Case Example/7. Business Case Model Outline.mp4 53 MB
31. Advanced Statistical Methods - K-Means Clustering/2. A Simple Example of Clustering.mp4 52 MB
42. Deep Learning - Preprocessing/3. Standardization.mp4 51 MB
10. Statistics - Descriptive Statistics/17. Variance.mp4 51 MB
15. Statistics - Hypothesis Testing/14. Test for the Mean. Dependent Samples.mp4 50 MB
13. Statistics - Inferential Statistics Confidence Intervals/1. What are Confidence Intervals.mp4 50 MB
12. Statistics - Inferential Statistics Fundamentals/4. The Normal Distribution.mp4 50 MB
33. Part 5 Mathematics/5. Linear Algebra and Geometry.mp4 50 MB
27. Advanced Statistical Methods - Linear regression/11. Decomposition of Variability.mp4 50 MB
33. Part 5 Mathematics/15. Dot Product of Matrices.mp4 49 MB
1. Part 1 Introduction/1. A Practical Example What You Will Learn in This Course.mp4 49 MB
12. Statistics - Inferential Statistics Fundamentals/11. Estimators and Estimates.mp4 48 MB
37. Deep Learning - TensorFlow Introduction/3. TensorFlow Outline and Logic.mp4 48 MB
43. Deep Learning - Classifying on the MNIST Dataset/8. MNIST Learning.mp4 47 MB
10. Statistics - Descriptive Statistics/19. Standard Deviation and Coefficient of Variation.mp4 45 MB
35. Deep Learning - Introduction to Neural Networks/5. Types of Machine Learning.mp4 45 MB
45. Deep Learning - Conclusion/6. An Overview of non-NN Approaches.mp4 45 MB
27. Advanced Statistical Methods - Linear regression/10. How to Interpret the Regression Table.mp4 45 MB
32. Advanced Statistical Methods - Other Types of Clustering/1. Types of Clustering.mp4 45 MB
27. Advanced Statistical Methods - Linear regression/7. First Regression in Python.mp4 45 MB
17. Part 3 Introduction to Python/5. Why Jupyter.mp4 44 MB
31. Advanced Statistical Methods - K-Means Clustering/4. How to Choose the Number of Clusters.mp4 44 MB
15. Statistics - Hypothesis Testing/6. Type I Error and Type II Error.mp4 44 MB
43. Deep Learning - Classifying on the MNIST Dataset/6. Calculating the Accuracy of the Model.mp4 44 MB
31. Advanced Statistical Methods - K-Means Clustering/8. Market Segmentation with Cluster Analysis (Part 1).mp4 43 MB
35. Deep Learning - Introduction to Neural Networks/1. Introduction to Neural Networks.mp4 43 MB
5. The Field of Data Science - Popular Data Science Techniques/12. Real Life Examples of Traditional Methods.mp4 43 MB
28. Advanced Statistical Methods - Multiple Linear Regression/12. A3 Normality and Homoscedasticity.mp4 43 MB
44. Deep Learning - Business Case Example/8. Business Case Optimization.mp4 42 MB
27. Advanced Statistical Methods - Linear regression/14. R-Squared.mp4 41 MB
27. Advanced Statistical Methods - Linear regression/6. Python Packages Installation.mp4 41 MB
15. Statistics - Hypothesis Testing/12. Test for the Mean. Population Variance Unknown.mp4 40 MB
10. Statistics - Descriptive Statistics/11. Cross Table and Scatter Plot.mp4 40 MB
45. Deep Learning - Conclusion/1. Summary of What You Learned.mp4 40 MB
35. Deep Learning - Introduction to Neural Networks/23. Optimization Algorithm n-Parameter Gradient Descent.mp4 39 MB
44. Deep Learning - Business Case Example/3. The Importance of Working with a Balanced Dataset.mp4 39 MB
37. Deep Learning - TensorFlow Introduction/6. Basic NN Example with TF Inputs, Outputs, Targets, Weights, Biases.mp4 38 MB
10. Statistics - Descriptive Statistics/5. Categorical Variables - Visualization Techniques.mp4 38 MB
29. Advanced Statistical Methods - Logistic Regression/8. Binary Predictors in a Logistic Regression.mp4 38 MB
35. Deep Learning - Introduction to Neural Networks/11. The Linear model with Multiple Inputs and Multiple Outputs.mp4 38 MB
33. Part 5 Mathematics/13. Transpose of a Matrix.mp4 38 MB
31. Advanced Statistical Methods - K-Means Clustering/5. Pros and Cons of K-Means Clustering.mp4 38 MB
37. Deep Learning - TensorFlow Introduction/8. Basic NN Example with TF Model Output.mp4 37 MB
35. Deep Learning - Introduction to Neural Networks/19. Common Objective Functions Cross-Entropy Loss.mp4 37 MB
10. Statistics - Descriptive Statistics/13. Mean, median and mode.mp4 37 MB
5. The Field of Data Science - Popular Data Science Techniques/17. Real Life Examples of Machine Learning (ML).mp4 37 MB
44. Deep Learning - Business Case Example/11. Business Case A Comment on the Homework.mp4 36 MB
15. Statistics - Hypothesis Testing/17. Test for the mean. Independent samples (Part 2).mp4 36 MB
30. Advanced Statistical Methods - Cluster Analysis/3. Difference between Classification and Clustering.mp4 36 MB
28. Advanced Statistical Methods - Multiple Linear Regression/10. A2 No Endogeneity.mp4 36 MB
13. Statistics - Inferential Statistics Confidence Intervals/5. Student's T Distribution.mp4 35 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/7. Backpropagation.mp4 35 MB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2. Basic NN Example (Part 2).mp4 35 MB
29. Advanced Statistical Methods - Logistic Regression/2. A Simple Example in Python.mp4 35 MB
33. Part 5 Mathematics/3. Scalars and Vectors.mp4 34 MB
25. Python - Advanced Python Tools/1. Object Oriented Programming.mp4 34 MB
33. Part 5 Mathematics/1. What is a matrix.mp4 34 MB
21. Python - Conditional Statements/4. The ELIF Statement.mp4 33 MB
29. Advanced Statistical Methods - Logistic Regression/9. Calculating the Accuracy of the Model.mp4 33 MB
39. Deep Learning - Overfitting/3. What is Validation.mp4 33 MB
33. Part 5 Mathematics/10. Addition and Subtraction of Matrices.mp4 33 MB
37. Deep Learning - TensorFlow Introduction/7. Basic NN Example with TF Loss Function and Gradient Descent.mp4 32 MB
29. Advanced Statistical Methods - Logistic Regression/7. What do the Odds Actually Mean.mp4 32 MB
29. Advanced Statistical Methods - Logistic Regression/11. Testing the Model.mp4 32 MB
13. Statistics - Inferential Statistics Confidence Intervals/7. Confidence Intervals; Population Variance Unknown; t-score.mp4 32 MB
28. Advanced Statistical Methods - Multiple Linear Regression/13. A4 No Autocorrelation.mp4 32 MB
34. Part 6 Deep Learning/1. What to Expect from this Part.mp4 31 MB
39. Deep Learning - Overfitting/1. What is Overfitting.mp4 31 MB
23. Python - Sequences/5. List Slicing.mp4 31 MB
18. Python - Variables and Data Types/5. Python Strings.mp4 31 MB
17. Part 3 Introduction to Python/9. Prerequisites for Coding in the Jupyter Notebooks.mp4 31 MB
29. Advanced Statistical Methods - Logistic Regression/6. Understanding Logistic Regression Tables.mp4 30 MB
31. Advanced Statistical Methods - K-Means Clustering/6. To Standardize or to not Standardize.mp4 30 MB
20. Python - Other Python Operators/3. Logical and Identity Operators.mp4 30 MB
15. Statistics - Hypothesis Testing/16. Test for the mean. Independent samples (Part 1).mp4 30 MB
5. The Field of Data Science - Popular Data Science Techniques/3. Real Life Examples of Traditional Data.mp4 30 MB
32. Advanced Statistical Methods - Other Types of Clustering/3. Heatmaps.mp4 30 MB
10. Statistics - Descriptive Statistics/23. Correlation Coefficient.mp4 30 MB
5. The Field of Data Science - Popular Data Science Techniques/9. Real Life Examples of Business Intelligence (BI).mp4 30 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2. What is a Deep Net.mp4 30 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/4. Learning Rate Schedules, or How to Choose the Optimal Learning Rate.m 29 MB
32. Advanced Statistical Methods - Other Types of Clustering/2. Dendrogram.mp4 29 MB
42. Deep Learning - Preprocessing/5. Binary and One-Hot Encoding.mp4 29 MB
13. Statistics - Inferential Statistics Confidence Intervals/13. Confidence intervals. Two means. Independent samples (Part 1).mp4 29 MB
28. Advanced Statistical Methods - Multiple Linear Regression/15. A5 No Multicollinearity.mp4 29 MB
35. Deep Learning - Introduction to Neural Networks/3. Training the Model.mp4 29 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/1. Stochastic Gradient Descent.mp4 29 MB
35. Deep Learning - Introduction to Neural Networks/7. The Linear Model (Linear Algebraic Version).mp4 28 MB
27. Advanced Statistical Methods - Linear regression/13. What is the OLS.mp4 28 MB
42. Deep Learning - Preprocessing/1. Preprocessing Introduction.mp4 28 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/4. Non-Linearities and their Purpose.mp4 28 MB
10. Statistics - Descriptive Statistics/21. Covariance.mp4 28 MB
31. Advanced Statistical Methods - K-Means Clustering/1. K-Means Clustering.mp4 27 MB
29. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.mp4 27 MB
13. Statistics - Inferential Statistics Confidence Intervals/15. Confidence intervals. Two means. Independent samples (Part 2).mp4 27 MB
18. Python - Variables and Data Types/1. Variables.mp4 27 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/6. Adaptive Learning Rate Schedules ( AdaGrad and RMSprop ).mp4 26 MB
33. Part 5 Mathematics/7. Arrays in Python - A Convenient Way To Represent Matrices.mp4 26 MB
10. Statistics - Descriptive Statistics/7. Numerical Variables - Frequency Distribution Table.mp4 26 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/6. Activation Functions Softmax Activation.mp4 26 MB
43. Deep Learning - Classifying on the MNIST Dataset/5. MNIST Loss and Optimization Algorithm.mp4 26 MB
44. Deep Learning - Business Case Example/9. Business Case Interpretation.mp4 26 MB
45. Deep Learning - Conclusion/5. An Overview of RNNs.mp4 25 MB
39. Deep Learning - Overfitting/4. Training, Validation, and Test Datasets.mp4 25 MB
35. Deep Learning - Introduction to Neural Networks/9. The Linear Model with Multiple Inputs.mp4 25 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/5. Activation Functions.mp4 25 MB
39. Deep Learning - Overfitting/2. Underfitting and Overfitting for Classification.mp4 25 MB
23. Python - Sequences/7. Dictionaries.mp4 25 MB
28. Advanced Statistical Methods - Multiple Linear Regression/19. Making Predictions with the Linear Regression.mp4 25 MB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3. Basic NN Example (Part 3).mp4 24 MB
39. Deep Learning - Overfitting/6. Early Stopping or When to Stop Training.mp4 24 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 24 MB
35. Deep Learning - Introduction to Neural Networks/17. Common Objective Functions L2-norm Loss.mp4 23 MB
29. Advanced Statistical Methods - Logistic Regression/5. An Invaluable Coding Tip.mp4 23 MB
12. Statistics - Inferential Statistics Fundamentals/10. Standard error.mp4 23 MB
35. Deep Learning - Introduction to Neural Networks/13. Graphical Representation of Simple Neural Networks.mp4 23 MB
43. Deep Learning - Classifying on the MNIST Dataset/2. MNIST How to Tackle the MNIST.mp4 23 MB
33. Part 5 Mathematics/8. What is a Tensor.mp4 22 MB
12. Statistics - Inferential Statistics Fundamentals/6. The Standard Normal Distribution.mp4 22 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/7. Adam (Adaptive Moment Estimation).mp4 22 MB
29. Advanced Statistical Methods - Logistic Regression/10. Underfitting and Overfitting.mp4 22 MB
5. The Field of Data Science - Popular Data Science Techniques/6. Real Life Examples of Big Data.mp4 22 MB
22. Python - Python Functions/7. Built-in Functions in Python.mp4 22 MB
23. Python - Sequences/1. Lists.mp4 22 MB
23. Python - Sequences/3. Using Methods.mp4 22 MB
28. Advanced Statistical Methods - Multiple Linear Regression/6. OLS Assumptions.mp4 22 MB
40. Deep Learning - Initialization/1. What is Initialization.mp4 22 MB
28. Advanced Statistical Methods - Multiple Linear Regression/1. Multiple Linear Regression.mp4 22 MB
31. Advanced Statistical Methods - K-Means Clustering/3. Clustering Categorical Data.mp4 21 MB
39. Deep Learning - Overfitting/5. N-Fold Cross Validation.mp4 21 MB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1. Basic NN Example (Part 1).mp4 21 MB
37. Deep Learning - TensorFlow Introduction/5. Types of File Formats, supporting Tensors.mp4 20 MB
45. Deep Learning - Conclusion/2. What's Further out there in terms of Machine Learning.mp4 20 MB
13. Statistics - Inferential Statistics Confidence Intervals/17. Confidence intervals. Two means. Independent samples (Part 3).mp4 20 MB
25. Python - Advanced Python Tools/7. Importing Modules in Python.mp4 20 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/8. Backpropagation picture.mp4 20 MB
10. Statistics - Descriptive Statistics/15. Skewness.mp4 19 MB
19. Python - Basic Python Syntax/1. Using Arithmetic Operators in Python.mp4 19 MB
43. Deep Learning - Classifying on the MNIST Dataset/3. MNIST Relevant Packages.mp4 19 MB
42. Deep Learning - Preprocessing/4. Preprocessing Categorical Data.mp4 19 MB
25. Python - Advanced Python Tools/5. What is the Standard Library.mp4 18 MB
35. Deep Learning - Introduction to Neural Networks/15. What is the Objective Function.mp4 18 MB
43. Deep Learning - Classifying on the MNIST Dataset/1. MNIST What is the MNIST Dataset.mp4 18 MB
37. Deep Learning - TensorFlow Introduction/4. Actual Introduction to TensorFlow.mp4 17 MB
26. Part 4 Advanced Statistical Methods in Python/1. Introduction to Regression Analysis.mp4 17 MB
40. Deep Learning - Initialization/3. State-of-the-Art Method - (Xavier) Glorot Initialization.mp4 17 MB
29. Advanced Statistical Methods - Logistic Regression/4. Building a Logistic Regression.mp4 17 MB
18. Python - Variables and Data Types/3. Numbers and Boolean Values in Python.mp4 17 MB
24. Python - Iterations/8. How to Iterate over Dictionaries.mp4 17 MB
23. Python - Sequences/6. Tuples.mp4 17 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/3. Momentum.mp4 16 MB
28. Advanced Statistical Methods - Multiple Linear Regression/5. Test for Significance of the Model (F-Test).mp4 16 MB
24. Python - Iterations/6. Conditional Statements and Loops.mp4 16 MB
22. Python - Python Functions/5. Conditional Statements and Functions.mp4 16 MB
12. Statistics - Inferential Statistics Fundamentals/1. Introduction.mp4 16 MB
24. Python - Iterations/3. While Loops and Incrementing.mp4 15 MB
22. Python - Python Functions/3. Defining a Function in Python - Part II.mp4 15 MB
27. Advanced Statistical Methods - Linear regression/3. Correlation vs Regression.mp4 15 MB
37. Deep Learning - TensorFlow Introduction/1. How to Install TensorFlow.mp4 15 MB
30. Advanced Statistical Methods - Cluster Analysis/4. Math Prerequisites.mp4 14 MB
40. Deep Learning - Initialization/2. Types of Simple Initializations.mp4 14 MB
17. Part 3 Introduction to Python/8. Understanding Jupyter's Interface - the Notebook Dashboard.mp4 14 MB
10. Statistics - Descriptive Statistics/9. The Histogram.mp4 14 MB
21. Python - Conditional Statements/1. The IF Statement.mp4 14 MB
21. Python - Conditional Statements/3. The ELSE Statement.mp4 14 MB
43. Deep Learning - Classifying on the MNIST Dataset/7. MNIST Batching and Early Stopping.mp4 13 MB
28. Advanced Statistical Methods - Multiple Linear Regression/8. A1 Linearity.mp4 13 MB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/1. What is a Layer.mp4 12 MB
27. Advanced Statistical Methods - Linear regression/9. Using Seaborn for Graphs.mp4 12 MB
44. Deep Learning - Business Case Example/2. Business Case Outlining the Solution.mp4 12 MB
42. Deep Learning - Preprocessing/2. Types of Basic Preprocessing.mp4 12 MB
24. Python - Iterations/1. For Loops.mp4 12 MB
24. Python - Iterations/4. Lists with the range() Function.mp4 11 MB
21. Python - Conditional Statements/5. A Note on Boolean Values.mp4 11 MB
44. Deep Learning - Business Case Example/10. Business Case Testing the Model.mp4 11 MB
33. Part 5 Mathematics/12. Errors when Adding Matrices.mp4 11 MB
41. Deep Learning - Digging into Gradient Descent and Learning Rate Schedules/2. Problems with Gradient Descent.mp4 11 MB
20. Python - Other Python Operators/1. Comparison Operators.mp4 10 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 kB
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/2.1 Course Notes - Section 6.pdf.pdf 936 kB
35. Deep Learning - Introduction to Neural Networks/1.1 Course Notes - Section 2.pdf.pdf 928 kB
35. Deep Learning - Introduction to Neural Networks/3.1 Course Notes - Section 2.pdf.pdf 928 kB
44. Deep Learning - Business Case Example/1.1 Audiobooks_data.csv.csv 711 kB
15. Statistics - Hypothesis Testing/4.1 Course notes_hypothesis_testing.pdf.pdf 659 kB
15. Statistics - Hypothesis Testing/1.1 Course notes_hypothesis_testing.pdf.pdf 649 kB
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1.1 Shortcuts-for-Jupyter.pdf.pdf 619 kB
37. Deep Learning - TensorFlow Introduction/1.1 Shortcuts-for-Jupyter.pdf.pdf 619 kB
37. Deep Learning - TensorFlow Introduction/4.1 Shortcuts-for-Jupyter.pdf.pdf 619 kB
10. Statistics - Descriptive Statistics/1.1 Course notes_descriptive_statistics.pdf.pdf 482 kB
9. Part 2 Statistics/1.2 Course notes_descriptive_statistics.pdf.pdf 482 kB
12. Statistics - Inferential Statistics Fundamentals/1.1 Course notes_inferential statistics.pdf.pdf 382 kB
12. Statistics - Inferential Statistics Fundamentals/2.1 Course notes_inferential statistics.pdf.pdf 382 kB
2. The Field of Data Science - The Various Data Science Disciplines/5.1 365_DataScience_Diagram.pdf.pdf 323 kB
2. The Field of Data Science - The Various Data Science Disciplines/7.1 365_DataScience_Diagram.pdf.pdf 323 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 kB
11. Statistics - Practical Example Descriptive Statistics/1.1 2.13. Practical example. Descriptive statistics_lesson.xlsx.xlsx 146 kB
11. Statistics - Practical Example Descriptive Statistics/2.1 2.13. Practical-example.Descriptive-statistics-exercise-solution.xlsx.xlsx 146 kB
11. Statistics - Practical Example Descriptive Statistics/2.2 2.13.Practical-example.Descriptive-statistics-exercise.xlsx.xlsx 120 kB
16. Statistics - Practical Example Hypothesis Testing/1.1 4.10.Hypothesis-testing-section-practical-example.xlsx.xlsx 52 kB
16. Statistics - Practical Example Hypothesis Testing/2.2 4.10.Hypothesis-testing-section-practical-example-exercise-solution.xlsx.xlsx 44 kB
16. Statistics - Practical Example Hypothesis Testing/2.1 4.10. Hypothesis testing section_practical example_exercise.xlsx.xlsx 43 kB
35. Deep Learning - Introduction to Neural Networks/21.1 GD-function-example.xlsx.xlsx 42 kB
10. Statistics - Descriptive Statistics/6.1 2.3. Categorical variables. Visualization techniques_exercise_solution.xlsx.xlsx 41 kB
10. Statistics - Descriptive Statistics/12.1 2.6. Cross table and scatter plot_exercise_solution.xlsx.xlsx 40 kB
10. Statistics - Descriptive Statistics/15.1 2.8. Skewness_lesson.xlsx.xlsx 35 kB
10. Statistics - Descriptive Statistics/5.1 2.3.Categorical-variables.Visualization-techniques-lesson.xlsx.xlsx 31 kB
10. Statistics - Descriptive Statistics/22.2 2.11. Covariance_exercise_solution.xlsx.xlsx 30 kB
10. Statistics - Descriptive Statistics/24.2 2.12. Correlation_exercise_solution.xlsx.xlsx 30 kB
10. Statistics - Descriptive Statistics/24.1 2.12. Correlation_exercise.xlsx.xlsx 29 kB
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29. Advanced Statistical Methods - Logistic Regression/1. Introduction to Logistic Regression.vtt 1.4 kB
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45. Deep Learning - Conclusion/4. DeepMind and Deep Learning.html 1.0 kB
19. Python - Basic Python Syntax/9. Understanding Line Continuation.vtt 1.0 kB
37. Deep Learning - TensorFlow Introduction/2. A Note on Installation of Packages in Anaconda.html 626 B
38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/9. Backpropagation - A Peek into the Mathematics of Optimization.html 539 B
10. Statistics - Descriptive Statistics/18. Variance Exercise.html 522 B
44. Deep Learning - Business Case Example/12. Business Case Final Exercise.html 439 B
44. Deep Learning - Business Case Example/5. Business Case Preprocessing Exercise.html 383 B
33. Part 5 Mathematics/12.1 Errors when Adding Matrices Python Notebook.html 220 B
33. Part 5 Mathematics/7.1 Arrays in Python Notebook.html 181 B
33. Part 5 Mathematics/10.1 Addition and Subtraction of Matrices Python Notebook.html 178 B
43. Deep Learning - Classifying on the MNIST Dataset/11.10 TensorFlow MNIST '5. Activation Functions (Part 2)' Solution.html 172 B
43. Deep Learning - Classifying on the MNIST Dataset/11.8 TensorFlow MNIST '4. Activation Functions (Part 1)' Solution.html 172 B
33. Part 5 Mathematics/15.1 Dot Product of Matrices Python Notebook.html 171 B
33. Part 5 Mathematics/13.1 Transpose of a Matrix Python Notebook.html 167 B
43. Deep Learning - Classifying on the MNIST Dataset/11.5 TensorFlow MNIST '8. Learning Rate (Part 1)' Solution.html 165 B
43. Deep Learning - Classifying on the MNIST Dataset/11.6 TensorFlow MNIST '9. Learning Rate (Part 2)' Solution.html 165 B
37. Deep Learning - TensorFlow Introduction/9.1 Basic NN Example with TensorFlow Exercise 2.4 Solution.html 162 B
37. Deep Learning - TensorFlow Introduction/9.2 Basic NN Example with TensorFlow Exercise 2.1 Solution.html 162 B
37. Deep Learning - TensorFlow Introduction/9.5 Basic NN Example with TensorFlow Exercise 2.2 Solution.html 162 B
37. Deep Learning - TensorFlow Introduction/9.8 Basic NN Example with TensorFlow Exercise 2.3 Solution.html 162 B
43. Deep Learning - Classifying on the MNIST Dataset/11.1 TensorFlow MNIST 'Time' Solution.html 162 B
43. Deep Learning - Classifying on the MNIST Dataset/11.7 TensorFlow MNIST '7. Batch size (Part 2)' Solution.html 162 B
43. Deep Learning - Classifying on the MNIST Dataset/11.9 TensorFlow MNIST '6. Batch size (Part 1)' Solution.html 162 B
10. Statistics - Descriptive Statistics/2. Types of Data.html 161 B
10. Statistics - Descriptive Statistics/4. Levels of Measurement.html 161 B
12. Statistics - Inferential Statistics Fundamentals/12. Estimators and Estimates.html 161 B
12. Statistics - Inferential Statistics Fundamentals/3. What is a Distribution.html 161 B
12. Statistics - Inferential Statistics Fundamentals/5. The Normal Distribution.html 161 B
12. Statistics - Inferential Statistics Fundamentals/9. Central Limit Theorem.html 161 B
13. Statistics - Inferential Statistics Confidence Intervals/10. Margin of Error.html 161 B
13. Statistics - Inferential Statistics Confidence Intervals/2. What are Confidence Intervals.html 161 B
13. Statistics - Inferential Statistics Confidence Intervals/6. Student's T Distribution.html 161 B
15. Statistics - Hypothesis Testing/11. p-value.html 161 B
15. Statistics - Hypothesis Testing/3. The Null vs Alternative Hypothesis.html 161 B
15. Statistics - Hypothesis Testing/5. Rejection Region and Significance Level.html 161 B
15. Statistics - Hypothesis Testing/7. Type I Error and Type II Error.html 161 B
17. Part 3 Introduction to Python/10. Jupyter's Interface.html 161 B
17. Part 3 Introduction to Python/2. Introduction to Programming.html 161 B
17. Part 3 Introduction to Python/4. Why Python.html 161 B
17. Part 3 Introduction to Python/6. Why Jupyter.html 161 B
18. Python - Variables and Data Types/2. Variables.html 161 B
18. Python - Variables and Data Types/4. Numbers and Boolean Values in Python.html 161 B
18. Python - Variables and Data Types/6. Python Strings.html 161 B
19. Python - Basic Python Syntax/11. Indexing Elements.html 161 B
19. Python - Basic Python Syntax/13. Structuring with Indentation.html 161 B
19. Python - Basic Python Syntax/2. Using Arithmetic Operators in Python.html 161 B
19. Python - Basic Python Syntax/4. The Double Equality Sign.html 161 B
19. Python - Basic Python Syntax/6. How to Reassign Values.html 161 B
19. Python - Basic Python Syntax/8. Add Comments.html 161 B
20. Python - Other Python Operators/2. Comparison Operators.html 161 B
20. Python - Other Python Operators/4. Logical and Identity Operators.html 161 B
21. Python - Conditional Statements/2. The IF Statement.html 161 B
21. Python - Conditional Statements/6. A Note on Boolean Values.html 161 B
22. Python - Python Functions/8. Python Functions.html 161 B
23. Python - Sequences/2. Lists.html 161 B
23. Python - Sequences/4. Using Methods.html 161 B
23. Python - Sequences/8. Dictionaries.html 161 B
24. Python - Iterations/2. For Loops.html 161 B
24. Python - Iterations/5. Lists with the range() Function.html 161 B
25. Python - Advanced Python Tools/2. Object Oriented Programming.html 161 B
25. Python - Advanced Python Tools/4. Modules and Packages.html 161 B
25. Python - Advanced Python Tools/6. What is the Standard Library.html 161 B
25. Python - Advanced Python Tools/8. Importing Modules in Python.html 161 B
26. Part 4 Advanced Statistical Methods in Python/2. Introduction to Regression Analysis.html 161 B
27. Advanced Statistical Methods - Linear regression/12. Decomposition of Variability.html 161 B
27. Advanced Statistical Methods - Linear regression/15. R-Squared.html 161 B
27. Advanced Statistical Methods - Linear regression/2. The Linear Regression Model.html 161 B
27. Advanced Statistical Methods - Linear regression/4. Correlation vs Regression.html 161 B
28. Advanced Statistical Methods - Multiple Linear Regression/11. A2 No Endogeneity.html 161 B
28. Advanced Statistical Methods - Multiple Linear Regression/14. A4 No autocorrelation.html 161 B
28. Advanced Statistical Methods - Multiple Linear Regression/16. A5 No Multicollinearity.html 161 B
28. Advanced Statistical Methods - Multiple Linear Regression/3. Adjusted R-Squared.html 161 B
28. Advanced Statistical Methods - Multiple Linear Regression/7. OLS Assumptions.html 161 B
28. Advanced Statistical Methods - Multiple Linear Regression/9. A1 Linearity.html 161 B
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2. The Field of Data Science - The Various Data Science Disciplines/2. Data Science and Business Buzzwords Why are there so many.html 161 B
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2. The Field of Data Science - The Various Data Science Disciplines/6. Business Analytics, Data Analytics, and Data Science An Introduction.html 161 B
2. The Field of Data Science - The Various Data Science Disciplines/8. Continuing with BI, ML, and AI.html 161 B
33. Part 5 Mathematics/11. Addition and Subtraction of Matrices.html 161 B
33. Part 5 Mathematics/2. What is a Matrix.html 161 B
33. Part 5 Mathematics/4. Scalars and Vectors.html 161 B
33. Part 5 Mathematics/6. Linear Algebra and Geometry.html 161 B
33. Part 5 Mathematics/9. What is a Tensor.html 161 B
34. Part 6 Deep Learning/2. What is Machine Learning.html 161 B
35. Deep Learning - Introduction to Neural Networks/10. The Linear Model with Multiple Inputs.html 161 B
35. Deep Learning - Introduction to Neural Networks/12. The Linear model with Multiple Inputs and Multiple Outputs.html 161 B
35. Deep Learning - Introduction to Neural Networks/14. Graphical Representation of Simple Neural Networks.html 161 B
35. Deep Learning - Introduction to Neural Networks/16. What is the Objective Function.html 161 B
35. Deep Learning - Introduction to Neural Networks/18. Common Objective Functions L2-norm Loss.html 161 B
35. Deep Learning - Introduction to Neural Networks/20. Common Objective Functions Cross-Entropy Loss.html 161 B
35. Deep Learning - Introduction to Neural Networks/22. Optimization Algorithm 1-Parameter Gradient Descent.html 161 B
35. Deep Learning - Introduction to Neural Networks/24. Optimization Algorithm n-Parameter Gradient Descent.html 161 B
35. Deep Learning - Introduction to Neural Networks/2. Introduction to Neural Networks.html 161 B
35. Deep Learning - Introduction to Neural Networks/4. Training the Model.html 161 B
35. Deep Learning - Introduction to Neural Networks/6. Types of Machine Learning.html 161 B
35. Deep Learning - Introduction to Neural Networks/8. The Linear Model.html 161 B
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4. The Field of Data Science - The Benefits of Each Discipline/2. The Reason behind these Disciplines.html 161 B
5. The Field of Data Science - Popular Data Science Techniques/11. Techniques for Working with Traditional Methods.html 161 B
5. The Field of Data Science - Popular Data Science Techniques/14. Machine Learning (ML) Techniques.html 161 B
5. The Field of Data Science - Popular Data Science Techniques/16. Types of Machine Learning.html 161 B
5. The Field of Data Science - Popular Data Science Techniques/18. Real Life Examples of Machine Learning (ML).html 161 B
5. The Field of Data Science - Popular Data Science Techniques/2. Techniques for Working with Traditional Data.html 161 B
5. The Field of Data Science - Popular Data Science Techniques/5. Techniques for Working with Big Data.html 161 B
5. The Field of Data Science - Popular Data Science Techniques/8. Business Intelligence (BI) Techniques.html 161 B
6. The Field of Data Science - Popular Data Science Tools/2. Necessary Programming Languages and Software Used in Data Science.html 161 B
7. The Field of Data Science - Careers in Data Science/2. Finding the Job - What to Expect and What to Look for.html 161 B
8. The Field of Data Science - Debunking Common Misconceptions/2. Debunking Common Misconceptions.html 161 B
9. Part 2 Statistics/2. Population and Sample.html 161 B
37. Deep Learning - TensorFlow Introduction/9.4 Basic NN Example with TensorFlow Exercise 3 Solution.html 160 B
37. Deep Learning - TensorFlow Introduction/9.6 Basic NN Example with TensorFlow Exercise 4 Solution.html 160 B
37. Deep Learning - TensorFlow Introduction/9.7 Basic NN Example with TensorFlow Exercise 1 Solution.html 160 B
43. Deep Learning - Classifying on the MNIST Dataset/11.3 TensorFlow MNIST '3. Width and Depth' Solution.html 160 B
43. Deep Learning - Classifying on the MNIST Dataset/3.1 TensorFlow MNIST Part 1 with Comments.html 159 B
43. Deep Learning - Classifying on the MNIST Dataset/4.1 TensorFlow MNIST Part 2 with Comments.html 159 B
43. Deep Learning - Classifying on the MNIST Dataset/5.1 TensorFlow MNIST Part 3 with Comments.html 159 B
43. Deep Learning - Classifying on the MNIST Dataset/6.1 TensorFlow MNIST Part 4 with Comments.html 159 B
43. Deep Learning - Classifying on the MNIST Dataset/7.1 TensorFlow MNIST Part 5 with Comments.html 159 B
43. Deep Learning - Classifying on the MNIST Dataset/8.1 TensorFlow MNIST Part 6 with Comments.html 159 B
43. Deep Learning - Classifying on the MNIST Dataset/11.11 TensorFlow MNIST 'Around 98% Accuracy' Solution.html 157 B
37. Deep Learning - TensorFlow Introduction/8.1 Basic NN Example with TensorFlow (Complete).html 156 B
33. Part 5 Mathematics/14.1 Dot Product Python Notebook.html 154 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.6 Basic NN Example Exercise 3d Solution.html 154 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.7 Basic NN Example Exercise 3b Solution.html 154 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.8 Basic NN Example Exercise 3c Solution.html 154 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.9 Basic NN Example Exercise 3a Solution.html 154 B
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37. Deep Learning - TensorFlow Introduction/6.1 Basic NN Example with TensorFlow (Part 2).html 154 B
37. Deep Learning - TensorFlow Introduction/7.1 Basic NN Example with TensorFlow (Part 3).html 154 B
37. Deep Learning - TensorFlow Introduction/9.3 Basic NN Example with TensorFlow (All Exercises).html 154 B
43. Deep Learning - Classifying on the MNIST Dataset/9.1 TensorFlow MNIST Complete Code with Comments.html 152 B
43. Deep Learning - Classifying on the MNIST Dataset/11.2 TensorFlow MNIST '1. Width' Solution.html 150 B
43. Deep Learning - Classifying on the MNIST Dataset/11.4 TensorFlow MNIST '2. Depth' Solution.html 150 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.10 Basic NN Example Exercise 6 Solution.html 149 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.1 Basic NN Example Exercise 5 Solution.html 149 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.3 Basic NN Example Exercise 4 Solution.html 149 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.4 Basic NN Example Exercise 1 Solution.html 149 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/5.5 Basic NN Example Exercise 2 Solution.html 149 B
33. Part 5 Mathematics/8.1 Tensors Notebook.html 148 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4.1 Basic NN Example (Part 4).html 145 B
43. Deep Learning - Classifying on the MNIST Dataset/10.1 TensorFlow MNIST All Exercises.html 144 B
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36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/1.2 Bais NN Example Part 1.html 136 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/2.1 Basic NN Example (Part 2).html 136 B
36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/3.1 Basic NN Example (Part 3).html 136 B
18. Python - Variables and Data Types/1.1 Variables - Resources.html 134 B
18. Python - Variables and Data Types/3.1 Numbers and Boolean Values - Resources.html 134 B
18. Python - Variables and Data Types/5.1 Strings - Resources.html 134 B
19. Python - Basic Python Syntax/10.1 Indexing Elements - Resources.html 134 B
19. Python - Basic Python Syntax/1.1 Arithmetic Operators - Resources.html 134 B
19. Python - Basic Python Syntax/12.1 Structure Your Code with Indentation - Resources.html 134 B
19. Python - Basic Python Syntax/3.1 The Double Equality Sign - Resources.html 134 B
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19. Python - Basic Python Syntax/7.1 Add Comments - Resources.html 134 B
19. Python - Basic Python Syntax/9.1 Line Continuation - Resources.html 134 B
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20. Python - Other Python Operators/3.1 Logical and Identity Operators - Resources.html 134 B
21. Python - Conditional Statements/1.1 Introduction to the If Statement - Resources.html 134 B
21. Python - Conditional Statements/3.1 Add an Else Statement - Resources.html 134 B
21. Python - Conditional Statements/4.1 Else if, for Brief - Elif - Resources.html 134 B
21. Python - Conditional Statements/5.1 A Note on Boolean Values - Resources.html 134 B
22. Python - Python Functions/1.1 Defining a Function in Python - Resources.html 134 B
22. Python - Python Functions/2.1 Creating a Function with a Parameter - Resources.html 134 B
22. Python - Python Functions/3.1 Another Way to Define a Function - Resources.html 134 B
22. Python - Python Functions/4.1 Using a Function in Another Function - Resources.html 134 B
22. Python - Python Functions/5.1 Combining Conditional Statements and Functions - Resources.html 134 B
22. Python - Python Functions/6.1 Creating Functions Containing a Few Arguments - Resources.html 134 B
22. Python - Python Functions/7.1 Notable Built-In Functions in Python - Resources.html 134 B
23. Python - Sequences/1.1 Lists - Resources.html 134 B
23. Python - Sequences/3.1 Help Yourself with Methods - Resources.html 134 B
23. Python - Sequences/5.1 List Slicing - Resources.html 134 B
23. Python - Sequences/6.1 Tuples - Resources.html 134 B
23. Python - Sequences/7.1 Dictionaries - Resources.html 134 B
24. Python - Iterations/1.1 For Loops - Resources.html 134 B
24. Python - Iterations/3.1 While Loops and Incrementing - Resources.html 134 B
24. Python - Iterations/4.1 Create Lists with the range() Function - Resources.html 134 B
24. Python - Iterations/6.1 Use Conditional Statements and Loops Together - Resources.html 134 B
24. Python - Iterations/7.1 All In - Conditional Statements, Functions, and Loops - Resources.html 134 B
24. Python - Iterations/8.1 Iterating over Dictionaries - Resources.html 134 B
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27. Advanced Statistical Methods - Linear regression/7.2 Simple linear regression - Exercise.html 134 B
27. Advanced Statistical Methods - Linear regression/8.1 Simple Linear Regression Exercise.html 134 B
28. Advanced Statistical Methods - Multiple Linear Regression/17.1 Dummies - Lecture.html 134 B
28. Advanced Statistical Methods - Multiple Linear Regression/18.1 Dummy variables Exercise.html 134 B
28. Advanced Statistical Methods - Multiple Linear Regression/19.1 Making predictions - Lecture.html 134 B
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28. Advanced Statistical Methods - Multiple Linear Regression/4.1 Multiple Linear Regression Exercise.html 134 B
29. Advanced Statistical Methods - Logistic Regression/11.1 Test dataset.html 134 B
29. Advanced Statistical Methods - Logistic Regression/2.1 Simple logistic regression example.html 134 B
29. Advanced Statistical Methods - Logistic Regression/4.1 Building a logistic regression.html 134 B
29. Advanced Statistical Methods - Logistic Regression/8.1 Binary predictors.html 134 B
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13. Statistics - Inferential Statistics Confidence Intervals/12. Confidence intervals. Two means. Dependent samples Exercise.html 81 B
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