Learn Machine Learning & Data Mining with Python

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Data Science Guide

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1. Introduction to Data Mining & Machine Learning in Python (Course 1)

1. Introduction to Data Mining & Machine Learning in Python (Course 1).mp4

02:32

2. Course Contents.mp4

01:40

3. Control a pace of a video.mp4

00:44

4. Introduction to Data Mining.mp4

02:02

5. Data Mining Definition.html

6. Business Applications of Data Mining.mp4

04:19

7. Data Mining Process Pyramid.mp4

01:55

8. Introduction to Machine Learning.mp4

02:01

9. Machine Leaning Sub-fields..html

10. How Does Machine Learning Work.mp4

03:46

11. Train and Test Sets..html

12. Machine Learning Algorithms Types.mp4

03:32

13. Machine Leaning Types.html

14. Reinforcement Learning overview.mp4

02:05

15. Course Rating.html

2. Setup Programming Environment

1. Install Anaconda package.mp4

03:25

2. Introduction to Jupyter.mp4

04:22

3.1 1_create (lists, tuples, and dictionaries).zip

3. Introduction to Python Part-1(Create Lists).mp4

05:47

4. Introduction to Python Part-2 (Create Tuples & Dictionaries).mp4

03:10

5.1 2_loops in python.zip

5. Introduction to Python Part-3 (Loops & Functions).mp4

07:04

6.1 3_import libraries.zip

6.2 Sales_data.csv

6. Introduction to Pandas Library.mp4

09:06

7. Introduction to NumPy & Matplotlib Libraries.mp4

03:44

8. Introduction to Scikit-learn Library.mp4

00:44

3. Supervised Learning Algorithms

1. Introduction to Supervised Learning Algorithms.mp4

01:10

2. Types of Variables.mp4

02:05

3. Data Types.html

4. Introduction to Regression Analysis.mp4

05:54

5. Regression Model.html

6. Regression Model Slope.mp4

05:49

7. Regression Slope.html

8. The Intercept Value.mp4

00:34

9. The Intercept Value.html

10. R-Squared Value.mp4

06:51

11. P-Value.mp4

03:43

12. Simple Linear Regression.mp4

00:49

13. Concepts used in Machine Learning (Important).html

14.1 Study_Hours.csv

14. Overview on the dataset.mp4

00:53

15.1 simple linear regression.zip

15. Create Simple Linear Regression Model in Python-Part 1.mp4

06:01

16. Create Simple Linear Regression Model in Python-Part 2.mp4

05:17

17. Create Simple Linear Regression Model in Python-Part 3.mp4

03:47

18. Create Simple Linear Regression Model in Python-Part 4.mp4

03:58

19. Create Simple Linear Regression Model in Python-Part 5.mp4

08:00

20. Multiple Linear Regression.mp4

02:41

21. Dummy Variables.mp4

04:11

22. Dummy Variables Trap.html

23. Stepwise Approach.mp4

05:48

24. Assumptions of Multiple Linear Regression.mp4

07:29

25.1 Companies spends and profits.csv

25. Overview on the business problem data.mp4

00:53

26.1 multiple linear regression.zip

26. Create Multiple Linear Regression Model in Python-Part 1.mp4

04:25

27. Create Multiple Linear Regression Model in Python-Part 2.mp4

05:58

28. Create Multiple Linear Regression Model in Python-Part 3.mp4

08:56

29. Create Multiple Linear Regression Model in Python-Part 4.mp4

08:37

30. Polynomial Regression.mp4

02:29

31.1 Reward_system.csv

31. Overview on the business problem data.mp4

01:03

32.1 polynomial regression model.zip

32. Create Polynomial Regression Model in Python-Part 1.mp4

06:52

33. Create Polynomial Regression Model in Python-Part 2.mp4

07:35

34. Create Polynomial Regression Model in Python-Part 3.mp4

03:20

35. Course Rating.html

36. Introduction to Classification.mp4

04:07

37. Introduction to Logistic Regression.mp4

07:59

38. Confusion Matrix.mp4

04:09

39. Standard Scaler.mp4

02:52

40.1 Bank_Data.csv

40. Overview on the business problem data.mp4

01:27

41.1 logistic regression model.zip

41. Create Logistic Regression Model in Python-Part 1.mp4

08:12

42. Create Logistic Regression Model in Python-Part 2.mp4

05:12

43. KNN Classification Algorithm.mp4

04:11

44.1 Bank_Data.csv

44.2 knn model.zip

44. Create KNN Model in Python.mp4

06:14

45. Support Vector Machine (SVM) Classification Algorithm.mp4

03:56

46.1 support vector machine.zip

46. Create Support Vector Machine in Python.mp4

06:14

47. Naive Bayes Algorithm Part 1.mp4

04:45

48. Naive Bayes Algorithm Part 2.mp4

06:52

49.1 naive bayes model.zip

49. Create Naive Bayes Model in Python.mp4

02:35

50. Decision Tree Algorithm.mp4

06:26

51.1 Bank_Data.csv

51.2 decision tree model .zip

51. Create Decision Tree Model in Python.mp4

02:15

52. Random Forest Algorithm.mp4

01:15

53.1 random forest model.zip

53. Create Random Forest Model in Python.mp4

04:09

54. Course Rating.html

4. Unsupervised Learning Algorithms

1. Review Unsupervised Learning Algorithms.mp4

01:40

2. Hierarchical Clustering Algorithm.mp4

02:58

3. Dendrogram Diagram Method.mp4

04:33

4.1 Movies.csv

4. Overview on the business problem data.mp4

00:37

5.1 hc clustering.zip

5. Create Hierarchical Clustering Algorithm in Python-1.mp4

08:31

6. Create Hierarchical Clustering Algorithm in Python-2.mp4

07:44

7. K-means Clustering Algorithm.mp4

03:49

8. Using Elbow Method to Determine Optimal Number of Clusters.mp4

09:38

9.1 k-means clustering model.zip

9. Create K-means Clustering Algorithm Model in Python - 1.mp4

07:23

10. Create K-means Clustering Algorithm Model in Python - 2.mp4

03:03

11. Association Rules (Market Basket Analysis).mp4

09:20

12.1 GroceryStoreDataSet.csv

12. Overview on the business problem data.mp4

00:44

13.1 apriori model.zip

13. Create Association Rules (Market Basket Analysis) Model in Python - 1.mp4

06:03

14. Create Association Rules (Market Basket Analysis) Model in Python - 2.mp4

04:44

15. Create Association Rules (Market Basket Analysis) Model in Python - 3.mp4

03:01

5. Deep Learning

1. Introduction to Deep Learning.mp4

05:41

2. Use Deep Learning in Classification.mp4

02:04

3. How Does Deep Learning Work.mp4

06:40

4. Activation Functions.mp4

06:37

5. What is Tensorflow.mp4

02:38

6. Introduction to the Deep Learning Problem and Dataset.mp4

01:05

7.1 deep learning model.zip

7.2 Medical_data.csv

7. Create Artificial Neural Network Model in Python Part-1.mp4

05:19

8. Create Artificial Neural Network Model in Python Part-2.mp4

08:02

9.1 Link to the Keras documentation website..html

9. Create Artificial Neural Network Model in Python Part-3.mp4

05:19

10. Course Rating.html

6. Appendix Statistics Overview

1. What is Statistics.mp4

01:30

2. Sample And Population.mp4

01:15

3. Descriptive and Inferential Statistics.mp4

01:47

4. Data types.mp4

03:54

5.1 Walmart+Stores.xlsx

5. Visualize Data.mp4

02:00

6.1 Histogram.xlsx

6. Histogram.mp4

04:38

7. Central Tendency Measures.mp4

03:07

8. Variability Measures.mp4

03:16

9.1 Variance+and+SD.xlsx

9. Calculate Central and Variability Measures (Practical).mp4

03:09

10. Symmetry and skewness in data.mp4

01:47

11. Correlation and Covariance.mp4

04:37

12. Introduction to Inferential Statistics.mp4

02:11

13. Discrete Probability Distributions.mp4

03:15

14. Normal Distribution.mp4

04:55

15. Variable standardization.mp4

02:27

16. Variable standardization Demo.mp4

00:51

17. Introduction to Central Limit Theorem.mp4

03:38

18. Estimators.mp4

01:27

19. Introduction to Confidence Interval.mp4

05:04

20. Calculate Confidence Interval for one Sample with a Known Population Variance.mp4

05:49

21. Introduction to the Business Problem.mp4

02:19

22.1 1_Confidence intervals variance known.xlsx

22. Calculate Confidence Interval in Excel.mp4

08:12

23. t - Distribution.mp4

03:52

24.1 2_Confidence intervals variance unknown.xlsx

24. Calculate Confidence Interval for one Sample with a Unknown Population Variance.mp4

03:45

25. Reduce Margin of Error.mp4

02:23

26. Confidence Interval for two Dependent Samples.mp4

01:14

27. Calculate Confidence Interval for two Dependent Samples in Excel.mp4

04:17

28.1 3_Confidence intervals dependent samples.xlsx

28. Confidence Interval for two Independent Samples with a Known Population Variance.mp4

02:56

29. Calculate Confidence Interval for two Independent Samples Known Var in Excel.mp4

04:31

30.1 5_Confidence intervals independent samples variance unknown and equal.xlsx

30. Confidence Interval for two Independent Samples Unknown Population Variance.mp4

05:01

31. What is a Statistical Hypothesis.mp4

02:29

32. Types of Hypotheses.mp4

03:54

33. P-Value.mp4

02:22

34. Link to z-value Calculator.html

35. Testing a Hypothesis for one Sample, Variance is Known.mp4

01:42

36.1 6_Test Hypothises population variance known.xlsx

36. Testing the Hypothesis in Excel.mp4

04:08

37.1 7_Test hypothises population variance unknown.xlsx

37. Testing a Hypothesis for one Sample, Variance is Unknown.mp4

02:58

38.1 8_Test hypothises dependent samples.xlsx

38. Testing a Hypothesis for two Dependent Samples.mp4

04:40

39. Link to t-value Calculator.html

40.1 9_Test hypothises two independent samples variance known.xlsx

40. Testing a Hypothesis for two Independent Samples, Variance is Known.mp4

03:58

41.1 10_Test hypothises two independent samples variance unknown.xlsx

41. Testing a Hypothesis for two Independent Samples, Variance is Unknown.mp4

03:11

Description

Learn Building Machine Learning & Deep Learning Models in Python, and use the Results in Data Mining Analyses

What You'll Learn?

Learn everything about Data Mining and its applications
Understand Machine Learning and its connection with Data Mining
Learn all Machine Learning algorithms, their types, and their usage in business
Learn how to implement Machine Learning algorithms in different business scenarios
Learn how to install and use Python programming language to create machine learning algorithms in a simple way
Learn how to import your data sets into Python and make required cleaning before creating the algorithms
Learn how to interpret the results of each algorithms and compare them with each other to choose the optimum one
Learn how to create graphs in Pythons, such as scattered and regression graphs and use them in your analyses
Learn data analysis in PySpark

Who is this for?

Anyone who need to use machine learning algorithms in data mining for business implementation.

Anyone wants to learn Machine Learning in Python.

Anyone wants to learn data analysis in PySpark.

What You Need to Know?

Basic knowledge in Statistics and operating systems

More details

Description
If you seek to learn how to create machine learning models and use them in data mining process, this course is for you. You will understand in this course what is data mining process and how to implement machine learning algorithms in data mining. Moreover, you will learn in details how deep learning does work and how to build a deep learning model to solve a business problem. In the beginning of the course, you will understand the basic concepts of data mining and learn about the business fields where data mining is implemented.
After that you will learn how to create machine learning models in Python using several data science libraries developed especially for this purpose. NumPy, Pandas, and Matplotlib are some examples of these models that you will learn how to import and use to create machine learning algorithms in Python. You will learn typing codes in Python from scratch without the need to have a pervious knowledge in coding. You will be familiar with the essential code needed to build machine learning models. This course is designed to provide you with the knowledge you need in a simple and straightforward way to smooth the learning process. You will build your knowledge step by step until you become familiar with the most used Machine Learning algorithms.Â

Who this course is for:
Anyone who need to use machine learning algorithms in data mining for business implementation.
Anyone wants to learn Machine Learning in Python.
Anyone wants to learn data analysis in PySpark.

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My name is Abraham Joudah I have worked in IT and Data Science for more than 15 years. After completed my bachelor’s in computer science, I worked as Database Administrator in one of the engineering companies. I have obtained several certificates from Microsoft like MCSE, MCDBA and MCSA. After several years of working in IT, I started focusing on Data Science field and learning SQL in depth to enhance my business data analysis skills. I also worked as Data Analyst in several companies. Over several years of working in this field I mastered several analytical tools, such as: R, SAS, SQL, Tableau, and Excel. As I enjoy working at data science field I pursued my study in this major and obtained my Master’s degree in Business Analytics from the University of North Texas.I love teaching Data Science, So I decided to create several courses in this field to share my knowledge with others. I tried to be more practical in my classes rather than repeating the same materials and curriculums in this field. I used materials based on real business scenarios to provide practical knowledge for students. I focused on examples from real business. In other words, I created shortcuts for learners to gain practical experience while studying my courses.

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