19 Files  |  05 Folders  |  390 MB total space

1. Introduction to the Course
2. A Wider View
3. Business uses of ChatGPT
4. Wrap Up

           1116 Files  |  46 Folders  |  9.20 GB total space

1. Part 1 Introduction
2. The Field of Data Science – The Various Data Science Disciplines
3. The Field of Data Science – Connecting the Data Science Disciplines
4. The Field of Data Science – The Benefits of Each Discipline
5. The Field of Data Science – Popular Data Science Techniques
6. The Field of Data Science – Popular Data Science Tools
7. The Field of Data Science – Careers in Data Science
8. The Field of Data Science – Debunking Common Misconceptions
9. Part 2 Statistics
10. Statistics – Descriptive Statistics
11. Statistics – Practical Example Descriptive Statistics
12. Statistics – Inferential Statistics Fundamentals
13. Statistics – Inferential Statistics Confidence Intervals
14. Statistics – Practical Example Inferential Statistics
15. Statistics – Hypothesis Testing
16. Statistics – Practical Example Hypothesis Testing
17. Part 3 Introduction to Python
18. Python – Variables and Data Types
19. Python – Basic Python Syntax
20. Python – Other Python Operators
21. Python – Conditional Statements
22. Python – Python Functions
23. Python – Sequences
24. Python – Iterations
25. Python – Advanced Python Tools
26. Part 4 Advanced Statistical Methods in Python
27. Advanced Statistical Methods – Linear regression
28. Advanced Statistical Methods – Multiple Linear Regression
29. Advanced Statistical Methods – Logistic Regression
30. Advanced Statistical Methods – Cluster Analysis
31. Advanced Statistical Methods – K-Means Clustering
32. Advanced Statistical Methods – Other Types of Clustering
33. Part 5 Mathematics
34. Part 6 Deep Learning
35. Deep Learning – Introduction to Neural Networks
36. Deep Learning – How to Build a Neural Network from Scratch with NumPy
37. Deep Learning – TensorFlow Introduction
38. Deep Learning – Digging Deeper into NNs Introducing Deep Neural Networks
39. Deep Learning – Overfitting
40. Deep Learning – Initialization
41. Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
42. Deep Learning – Preprocessing
43. Deep Learning – Classifying on the MNIST Dataset
44. Deep Learning – Business Case Example
45. Deep Learning – Conclusion

            57 Files  |  08 Folders  |  1.50 GB total space

1. Introduction to the course
2. Introduction to AutoML
3. Auto Sklearn Part 1
4. Auto Sklearn Part2
5. TPOT Models Part 1
6. TPOT Models Part 2
7. Auto Keras Models

           11 Files  |  01 Folders  |  1.33 GB total space

1. Introduction
2. Data Science
3. Artificial Intelligence
4. Deep Learning
5. Machine Learning
6. Data Engineering
7. Data Analytics
8. Business Intelligence
9. Data Visualization
10. Cluster Analysis

          336 Files  |  21 Folders  |  7.40 GB total space

1. Introduction
2. Data Science & Machine Learning Concepts
3. Python For Data Science
4. Statistics for Data Science
5. Probability & Hypothesis Testing
6. NumPy Data Analysis
7. Pandas Data Analysis
8. Python Data Visualization
9. Machine Learning
10. Data Loading & Exploration
11. Data Cleaning
12. Feature Selecting and Engineering
13. Linear and Logistic Regression
14. K Nearest Neighbors
15. Decision Trees
16. Ensemble Learning and Random Forests
17. Support Vector Machines
18. Kmeans
19. PCA
20. Data Science Career

            18 Files  |  01 Folders  |  1.02 GB total space

1. ML.NET Machine learning introduction
2. ML.NET introduction
3. Getting started with ML.NET
4. Build an ML model for sentiment analysis
5. Build an ML model for GitHub issue classification
6. Build an ML model for predicting taxi fares
7. Build an ML model for movie recommendations
8. Deep learning with ML.NET Image classification

           164 Files  |  11 Folders  |  2.12 GB total space

1. Introduction
2. Core Data Concepts
3. Visualizing Data
4. Combinatorics
5. Probability
6. Joint Distributions
7. Data Distributions
8. The Normal Distribution
9. Sampling
10. Hypothesis Testing
11. Regression

          53 Files  |  17 Folders  |  1.26 GB total space

1. Introduction
2. Data Exploration
3. Data Modeling
4. Data Modeling
5. Error Debugging
6. Predictive Modeling
7. Report Generation
8. Explaining model
9. Explaining Code
10. Explaining data
11. Documentation
12. Time Management
13. Project Management
14. Interview preparation
15. Additional Resources for Learning more about ChatGPT
16. Conclusion

             44 Files  |  22 Folders  |  5.40 GB total space

1. Welcome to the course
2. Setting up R Studio and R crash course
3. Basics of Statistics
4. Intorduction to Machine Learning
5. Data Preprocessing for Regression Analysis
6. Linear Regression Model
7. Regression models other than OLS
8. Introduction to the classification Models
9. Logistic Regression
10. Linear Discriminant Analysis
11. K-Nearest Neighbors
12. Comparing results from 3 models
13. Simple Decision Trees
14. Simple Classification Tree
15. Ensemble technique 1 – Bagging
16. Ensemble technique 2 – Random Forest
17. Ensemble technique 3 – GBM, AdaBoost and XGBoost
18. Support Vector Machines
19. Support Vector Classifier
20. Support Vector Machines
21. Creating Support Vector Machine Model in R
22. Congratulations 

          174 Files  |  16 Folders  |  659 MB total space

1. Defining-data-science-and-what-data-scientists-do
2. Data-science-topics
3. Applications-and-careers-in-data-science
4. Data-literacy-for-data-science-optional

          299 Files  |  80 Folders  |  745 MB total space

1. open-source-tools-for-data-science
2. python-for-applied-data-science-ai
3. sql-data-science
4. statistics-for-data-science-python

          293 Files  |  26 Folders  |  16.2 GB total space

1. Introduction to Course
2. OPTIONAL: Python Crash Course
3. Machine Learning Pathway Overview
4. NumPy
5. Pandas
6. Matplotlib
7. Seaborn Data Visualizations
8. Data Analysis and Visualization Capstone Project Exercise
9. Machine Learning Concepts Overview
10. Linear Regression
11. Feature Engineering and Data Preparation
12. Cross Validation Grid Search and the Linear Regression Project
13. Logistic Regression
14. KNN K Nearest Neighbors
15. Support Vector Machines
16. Tree Based Methods Decision Tree Learning
17. Random Forests
18. Boosting Methods
19. Supervised Learning Capstone Project Cohort Analysis and Tree Based Methods
20. Naive Bayes Classification and Natural Language Processing Supervised Learning
21. Unsupervised Learning
22. KMeans Clustering
23. Hierarchical Clustering
24. DBSCAN Densitybased spatial clustering of applications with noise
25. PCA Principal Component Analysis and Manifold Learning
26. Model Deployment