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

1. 11. Statistics - Practical Example Descriptive Statistics/1. Practical Example Descriptive Statistics.mp4 163.28 MB
2. 33. Part 5 Mathematics/16. Why is Linear Algebra Useful.mp4 147.81 MB
3. 5. The Field of Data Science - Popular Data Science Techniques/1. Techniques for Working with Traditional Data.mp4 141.62 MB
4. 3. The Field of Data Science - Connecting the Data Science Disciplines/1. Applying Traditional Data, Big Data, BI, Traditional Data Science and ML.mp4 129.91 MB
5. 5. The Field of Data Science - Popular Data Science Techniques/15. Types of Machine Learning.mp4 128.15 MB
6. 5. The Field of Data Science - Popular Data Science Techniques/10. Techniques for Working with Traditional Methods.mp4 126.48 MB
7. 15. Statistics - Hypothesis Testing/4. Rejection Region and Significance Level.mp4 115.88 MB
8. 2. The Field of Data Science - The Various Data Science Disciplines/7. Continuing with BI, ML, and AI.mp4 111.59 MB
9. 6. The Field of Data Science - Popular Data Science Tools/1. Necessary Programming Languages and Software Used in Data Science.mp4 106.01 MB
10. 44. Deep Learning - Business Case Example/4. Business Case Preprocessing.mp4 105.89 MB
11. 14. Statistics - Practical Example Inferential Statistics/1. Practical Example Inferential Statistics.mp4 105.13 MB
12. 5. The Field of Data Science - Popular Data Science Techniques/13. Machine Learning (ML) Techniques.mp4 101.71 MB
13. 15. Statistics - Hypothesis Testing/1. The Null vs Alternative Hypothesis.mp4 94.33 MB
14. 5. The Field of Data Science - Popular Data Science Techniques/7. Business Intelligence (BI) Techniques.mp4 92.10 MB
15. 44. Deep Learning - Business Case Example/1. Business Case Getting acquainted with the dataset.mp4 89.76 MB
16. 29. Advanced Statistical Methods - Logistic Regression/3. Logistic vs Logit Function.mp4 88.57 MB
17. 2. The Field of Data Science - The Various Data Science Disciplines/1. Data Science and Business Buzzwords Why are there so many.mp4 83.37 MB
18. 4. The Field of Data Science - The Benefits of Each Discipline/1. The Reason behind these Disciplines.mp4 83.14 MB
19. 13. Statistics - Inferential Statistics Confidence Intervals/3. Confidence Intervals; Population Variance Known; z-score.mp4 80.08 MB
20. 44. Deep Learning - Business Case Example/6. Creating a Data Provider.mp4 78.18 MB
21. 5. The Field of Data Science - Popular Data Science Techniques/4. Techniques for Working with Big Data.mp4 77.32 MB
22. 17. Part 3 Introduction to Python/3. Why Python.mp4 76.88 MB
23. 31. Advanced Statistical Methods - K-Means Clustering/10. How is Clustering Useful.mp4 76.24 MB
24. 8. The Field of Data Science - Debunking Common Misconceptions/1. Debunking Common Misconceptions.mp4 74.59 MB
25. 10. Statistics - Descriptive Statistics/1. Types of Data.mp4 74.26 MB
26. 30. Advanced Statistical Methods - Cluster Analysis/2. Some Examples of Clusters.mp4 73.26 MB
27. 13. Statistics - Inferential Statistics Confidence Intervals/11. Confidence intervals. Two means. Dependent samples.mp4 72.16 MB
28. 16. Statistics - Practical Example Hypothesis Testing/1. Practical Example Hypothesis Testing.mp4 71.15 MB
29. 2. The Field of Data Science - The Various Data Science Disciplines/9. A Breakdown of our Data Science Infographic.mp4 69.37 MB
30. 2. The Field of Data Science - The Various Data Science Disciplines/5. Business Analytics, Data Analytics, and Data Science An Introduction.mp4 66.06 MB
31. 12. Statistics - Inferential Statistics Fundamentals/8. Central Limit Theorem.mp4 64.39 MB
32. 43. Deep Learning - Classifying on the MNIST Dataset/9. MNIST Results and Testing.mp4 64.28 MB
33. 1. Part 1 Introduction/2. What Does the Course Cover.mp4 63.75 MB
34. 12. Statistics - Inferential Statistics Fundamentals/2. What is a Distribution.mp4 63.07 MB
35. 36. Deep Learning - How to Build a Neural Network from Scratch with NumPy/4. Basic NN Example (Part 4).mp4 62.61 MB
36. 38. Deep Learning - Digging Deeper into NNs Introducing Deep Neural Networks/3. Digging into a Deep Net.mp4 60.79 MB
37. 13. Statistics - Inferential Statistics Confidence Intervals/9. Margin of Error.mp4 60.51 MB
38. 45. Deep Learning - Conclusion/3. An overview of CNNs.mp4 60.20 MB
39. 17. Part 3 Introduction to Python/1. Introduction to Programming.mp4 59.95 MB
40. 9. Part 2 Statistics/1. Population and Sample.mp4 59.51 MB
41. 27. Advanced Statistical Methods - Linear regression/1. The Linear Regression Model.mp4 58.75 MB
42. 43. Deep Learning - Classifying on the MNIST Dataset/4. MNIST Model Outline.mp4 57.73 MB
43. 31. Advanced Statistical Methods - K-Means Clustering/9. Market Segmentation with Cluster Analysis (Part 2).mp4 57.46 MB
44. 15. Statistics - Hypothesis Testing/10. p-value.mp4 57.21 MB
45. 28. Advanced Statistical Methods - Multiple Linear Regression/17. Dealing with Categorical Data - Dummy Variables.mp4 57.00 MB
46. 35. Deep Learning - Introduction to Neural Networks/21. Optimization Algorithm 1-Parameter Gradient Descent.mp4 56.95 MB
47. 28. Advanced Statistical Methods - Multiple Linear Regression/2. Adjusted R-Squared.mp4 56.14 MB
48. 17. Part 3 Introduction to Python/7. Installing Python and Jupyter.mp4 55.71 MB
49. 10. Statistics - Descriptive Statistics/3. Levels of Measurement.mp4 55.70 MB
50. 7. The Field of Data Science - Careers in Data Science/1. Finding the Job - What to Expect and What to Look for.mp4 55.68 MB