Data Science and Machine Learning Fundamentals [2024]

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Henrik Johansson

47:51:20

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1. Introduction

1. Course introduction.mp4

15:00

2. Setup of the Anaconda Cloud Notebook.mp4

14:22

3. Download and installation of the Anaconda Distribution (optional).mp4

21:05

4. The Conda Package Management System (optional).mp4

35:00

2. Master Python for data handling

1. Overview of the first part of this section.mp4

28:25

2. Python Integers.mp4

14:12

3. Python Floats.mp4

10:50

4. Python Strings I.mp4

15:03

5. Python Strings II Intermediate String Methods.mp4

22:36

6. Python Strings III DateTime Objects and Strings.mp4

27:33

7. Python Native Data Storage Overview.mp4

03:00

8. Python Set.mp4

15:20

9. Python Tuple.mp4

27:35

10. Python Dictionary.mp4

30:00

11. Python List.mp4

33:57

12. Data Transformers and Functions.mp4

03:06

13. The While Loop.mp4

19:20

14. The For Loop.mp4

17:02

15. Python Logic Operators.mp4

31:00

16. Python Functions I.mp4

03:20

17. Python Functions II.mp4

33:53

18. Python Object Oriented Programming I Theory.mp4

14:10

19. Python Object Oriented Programming II OOP.mp4

39:20

20. Python Object Oriented Programming III Files and Tables.mp4

27:17

21. Python Object Oriented Programming IV Recap and More.mp4

58:21

Files.zip

3. Master Pandas for Data Handling

1. Master Pandas for Data Handling Overview.mp4

11:21

2. Pandas theory and terminology.mp4

11:13

3. Creating a DataFrame from scratch.mp4

30:47

4. Pandas File Handling Overview.mp4

02:51

5. Pandas File Handling The .csv file format.mp4

18:48

6. Pandas File Handling The .xlsx file format.mp4

23:20

7. Pandas File Handling SQL-database files.mp4

15:08

8. Pandas Operations & Techniques Overview.mp4

03:11

9. Pandas Operations & Techniques Object Inspection.mp4

19:34

10. Pandas Operations & Techniques DataFrame Inspection.mp4

18:53

11. Pandas Operations & Techniques Column Selections.mp4

21:04

12. Pandas Operations & Techniques Row Selections.mp4

21:11

13. Pandas Operations & Techniques Conditional Selections.mp4

21:27

14. Pandas Operations & Techniques Scalers and Standardization..mp4

23:08

15. Pandas Operations & Techniques Concatenate DataFrames.mp4

29:21

16. Pandas Operations & Techniques Joining DataFrames.mp4

19:30

17. Pandas Operations & Techniques Merging DataFrames.mp4

30:48

18. Pandas Operations & Techniques Transpose & Pivot Functions.mp4

34:31

19. Pandas Data Preparation I Overview & workflow.mp4

05:23

20. Pandas Data Preparation II Edit DataFrame labels.mp4

20:16

21. Pandas Data Preparation III Duplicates.mp4

22:23

22. Pandas Data Preparation IV Missing Data & Imputation.mp4

54:35

23. Pandas Data Preparation V Data Binnings [Extra Video].mp4

46:32

24. Pandas Data Preparation VI Indicator Features [Extra Video].mp4

33:01

25. Pandas Data Description I Overview.mp4

02:35

26. Pandas Data Description II Sorting and Ranking.mp4

26:51

27. Pandas Data Description III Descriptive Statistics.mp4

31:40

28. Pandas Data Description IV Crosstabulations & Groupings.mp4

30:06

29. Pandas Data Visualization I Overview.mp4

03:35

30. Pandas Data Visualization II Histograms.mp4

42:34

31. Pandas Data Visualization III Boxplots.mp4

33:00

32. Pandas Data Visualization IV Scatterplots.mp4

40:00

33. Pandas Data Visualization V Pie Charts.mp4

45:40

34. Pandas Data Visualization VI Line plots.mp4

50:24

Files.zip

4. Regression and Prediction with Machine Learning models

1. Regression, Prediction, and Supervised Learning. Section Overview (I).mp4

10:15

2. The Traditional Simple Regression Model (II).mp4

35:08

3. The Traditional Simple Regression Model (III).mp4

38:00

4. Some practical and useful modelling concepts (IV).mp4

13:01

5. Some practical and useful modelling concepts (V).mp4

13:01

6. Linear Multiple Regression model (VI).mp4

57:00

7. Linear Multiple Regression model (VII).mp4

36:24

8. Multivariate Polynomial Multiple Regression models (VIII).mp4

10:13

9. Multivariate Polynomial Multiple Regression models (VIIII).mp4

01:06:05

10. Regression Regularization, Lasso and Ridge models (X).mp4

01:29:52

11. Decision Tree Regression models (XI).mp4

01:15:26

12. Random Forest Regression (XII).mp4

01:09:18

13. Voting Regression (XIII).mp4

48:00

Files.zip

5. Classification with Machine Learning models

1. Classification and Supervised Learning, overview.mp4

17:31

2. Logistic Regression Classifier.mp4

01:00:00

3. The Naive Bayes Classifier.mp4

48:13

4. The Decision Tree Classifier.mp4

01:06:40

5. The Random Forest Classifier.mp4

50:05

6. Linear Discriminant Analysis (LDA) [Extra Video].mp4

54:30

7. The Voting Classifier.mp4

25:00

Files.zip

6. Cluster Analysis and Unsupervised Learning

1. Cluster Analysis, an overview.mp4

22:16

2. K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms.mp4

26:47

3. Density-Based Spatial Clustering of Applications with Noise (DBSCAN).mp4

34:46

4. Four Hierarchical Clustering algorithms.mp4

21:18

Files.zip

7. Advanced Machine Learning models and tasks

1. Overview.mp4

01:44

2. Artificial Neural Networks, Feedforward Networks, and the Multi-Layer Perceptron.mp4

20:00

3. Feedforward Multi-Layer Perceptrons for Classification tasks.mp4

20:02

4. Feedforward Multi-Layer Perceptrons for Prediction tasks.mp4

28:50

Files.zip

8. Text Mining and NLP

1. Text Mining and NLP introduction.mp4

08:18

2. Text Mining Setup.mp4

16:32

3. Text Mining Tasks.mp4

12:37

4. Text Mining Process.mp4

06:15

5. Text Indexing Process.mp4

17:02

6. The Tokenization Process.mp4

53:04

7. Spelling correction and stop words.mp4

45:24

8. Lemmatization and Stemming.mp4

33:22

9. The Bag of Words Data Structure and some models.mp4

37:19

10. The TF-IDF Data Structure and some models.mp4

33:00

11. The N-grams Data Structure.mp4

33:22

12. Attention-based models and Generative Pre-trained Transformer models.mp4

30:48

13. Emotion Mining and Sentiment Analysis.mp4

01:19:24

Files.zip

Description

Learn to master Data Science and Machine Learning Fundamentals with Python and Pandas

What You'll Learn?

Knowledge about Data Science and Machine Learning theory, algorithms, methods, best practices, and tasks
Deep hands-on knowledge about Data Science and Machine Learning, and know how to do common Data Science and Machine Learning tasks
The ability to handle common Data Science and Machine Learning tasks with confidence
Master Python for Data Handling
Master Pandas for Data Handling
Knowledge and practical hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and many other Python libraries
Detailed and deep, Master knowledge of Regression, Regression Analysis, Prediction, Classification, and Cluster analysis
Advanced knowledge of A.I. prediction models and automatic model creation
Advanced Knowledge of Text Mining, Text Mining Tasks, and Emotion Mining
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources

Who is this for?

This course is for you, regardless if you are a beginner or experienced Data Scientist, regardless if you have a Ph.D., or no education or experience at all.

What You Need to Know?

The four ways of counting (+-*/)

Everyday experience with Windows, Linux, or Mac-OS

More details

Description
This course is an exciting hands-on view of the fundamentals of Data Science and Machine Learning
Data Science and Machine Learning are developing on a massive scale. Everywhere you look in society, the world wide web, or in technology, you will find Data Science and Machine Learning algorithms working behind the scenes to analyze and optimize all aspects of our lives, businesses, and our society. Data Science and Machine Learning with Artificial Intelligence are some of the hottest and fastest-developing areas right now.
This course will teach you the fundamentals of Data Science and Machine Learning. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist, and aspires to be one of the best Udemy courses in terms of education and value.Â
You will learn about
Regression and Prediction with Machine Learning models using supervised learning. This course has the most complete and fundamental master-level regression analysis content packages on Udemy, with hands-on, useful practical theory, and automatic Machine Learning algorithms for model building, feature selection, and artificial intelligence. You will learn about models ranging from linear regression models to advanced multivariate polynomial regression models.
Classification with Machine Learning models using supervised learning. You will learn about the classification process, classification theory, and visualizations as well as some useful classifier models, including the very powerful Random Forest Classifier Ensembles and Voting Classifier Ensembles.
Cluster Analysis with Machine Learning models using unsupervised learning. In this part of the course, you will learn about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and seven useful Machine Learning clustering algorithms ranging from hierarchical cluster models to density-based cluster models.
The fundamentals of Data Science and Machine Learning. This course gives a very solid foundation and knowledge base for Data Science and Machine Learning jobs or studies.
Advanced A.I. prediction models and automatic model creation. This video course includes videos where the use of very powerful algorithms for automatic model creation is taught.
Advanced Text Mining and Automation. You will learn to mine text data and the fundamentals of Text and Emotion Mining such as Tokenization, text data preparation, spell checking, lemmatization, stemming, and classification of text data.
Mastering Python for data handling.
Mastering Pandas for data handling.
This course includes
a comprehensive and easy-to-follow teaching package for Mastering Python and Pandas for data handling, which makes anyone able to learn the course contents regardless of beforehand knowledge of programming, tabulation software, Python, Pandas, Data Science, or Machine Learning.
Learn to use Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
an optional easy-to-follow guide for downloading, installing, and setting up the Anaconda Distribution, which makes anyone able create a local installation of a Python Data Science and Machine Learning environment.
content that will teach you many new things, regardless of if you are a beginner or an experienced Data Scientist.
a large collection of unique content, and will teach you many new things that only can be learned from this course on Udemy.
A complete masterclass package for Data Science and Machine Learning.
A course structure built on a proven and professional framework for learning.
A compact course structure and no killing time.
Is this course for you?
This course is for you, regardless if you are a beginner or an experienced Data Scientist.
This course is for you, regardless if you have no education or are experienced with a Ph.D.
Course requirements
The four ways of counting (+-*/)
Basic everyday experience with either Windows, Linux, Mac OS, or similar operating systems
After completing this course, you will have
Knowledge about Data Science and Machine Learning theory, algorithms, methods, best practices, and tasks.
Deep hands-on knowledge of Data Science and Machine Learning, and know how to do common Data Science and Machine Learning tasks.
The ability to handle common Data Science and Machine Learning tasks with confidence.
Knowledge to Master Python for Data Handling.
Knowledge to Master Pandas for Data Handling.
Knowledge and practical hands-on knowledge of Scikit-learn, Stats models, Matplotlib, Seaborn, and many other Python libraries.
Detailed and deep Master knowledge of Regression Prediction, Classification, and Cluster Analysis.
Advanced knowledge of A.I. prediction models and automatic model creation.
Advanced Knowledge of Text Mining, Text Mining Tasks, and Emotion Mining.
Who this course is for:
This course is for you, regardless if you are a beginner or experienced Data Scientist, regardless if you have a Ph.D., or no education or experience at all.

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Machine Learning

Henrik Johansson

Instructor's Courses

Henrik has a wide instructor/lecturer experience with more than 20 years in roles ranging from University teacher to sports coach to leadership roles in the private and public sectors.Henrik has experience teaching students from all walks of life, from the poor to royalty, and has taught students from nearly all educational backgrounds, from high school to Ph.D.s.Courses given by Henrik are intended to have unique content, and will teach you many new things.

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