Data Science Methods and Techniques [2024]

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Henrik Johansson

17:02:34

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1. Introduction

1. Introduction.mp4

17:48

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. Regression, Prediction and Supervised Learning

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

3. Classification and Supervised Learning

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. The Voting Classifier.mp4

25:00

Files.zip

4. 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

Description

Learn Data Science Methods and Techniques for Data Analysis and Machine Learning [2024]

What You'll Learn?

Knowledge about Data Science methods, techniques, theory, best practices, and tasks
Deep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidence
Detailed and deep Master knowledge of Regression, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised Learning
Hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python libraries
Advanced knowledge of A.I. prediction models and automatic model creation
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 an experienced Data Scientist

This course is for you, regardless if you have a Ph.D. or no education or experience at all

What You Need to Know?

Basic knowledge of the Python programming language and preferably the Pandas library

The four ways of counting (+-*/)

Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended

Access to a computer with an internet connection

More details

Description
Welcome to the course Data Science Methods and Techniques for Data Analysis and Machine Learning!
Data Science is expanding and developing on a massive and global scale. Everywhere in society, there is a movement to implement and use Data Science Methods and Techniques to develop and optimize all aspects of our lives, businesses, societies, governments, and states.
This course will teach you a large selection of Data Science methods and techniques, which will give you an excellent foundation for Data Science jobs and studies. This course has exclusive content that will teach you many new things regardless of if you are a beginner or an experienced Data Scientist, Data Analyst, or Machine Learning Engineer.

This is a three-in-one master class video course which will teach you to master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, and Unsupervised Learning.
You will learn to master Regression, Regression analysis, Prediction and supervised learning. This course has the most complete and fundamental master-level regression content packages on Udemy, with hands-on, useful practical theory, and also 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.
You will learn to master Classification and 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 Classifiers Ensembles and Voting Classifier Ensembles.
You will learn to master Cluster Analysis and unsupervised learning. This part of the course is about unsupervised learning, cluster theory, artificial intelligence, explorative data analysis, and some useful Machine Learning clustering algorithms ranging from hierarchical cluster models to density-based cluster models.

You will learn
Knowledge about Data Science methods, techniques, theory, best practices, and tasks
Deep hands-on knowledge of Data Science and know how to handle common Data Science tasks with confidence
Detailed and deep Master knowledge of Regression, Regression analysis, Prediction, Classification, Supervised Learning, Cluster Analysis, and Unsupervised Learning
Hands-on knowledge of Scikit-learn, Statsmodels, Matplotlib, Seaborn, and some other Python libraries
Advanced knowledge of A.I. prediction models and automatic model creation
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud computing resources
Option: To use the Anaconda Distribution (for Windows, Mac, Linux)
Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages â€“ golden nuggets to improve your quality of work life
And much moreâ€¦

This course includes
an easy-to-follow guide for using the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). You may learn to use Cloud Computing resources in this course
an easy-to-follow optional guide for downloading, installing, and setting up the Anaconda Distribution, which makes anyone able to install a Python Data Science environment useful for this course or for any Data Science or coding task
content that will teach you many new things, regardless of if you are a beginner or an experienced Data Scientist, Data Analyst, or Machine Learning Engineer
a large collection of unique content, and this course will teach you many new things that only can be learned from this course on Udemy
a course structure built on a proven and professional framework for learning.
a compact course structure and no killing time

This course is an excellent way to learn to master Regression, Prediction, Classification, and Cluster analysis!
These are the most important and useful tools for modeling, AI, and forecasting.

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 a Ph.D. or no education or experience at all

This course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Regression, Prediction, Classification, Supervised Learning, Cluster analysis, and unsupervised learning.

Course requirements
Basic knowledge of the Python programming language and preferably the Pandas library
The four ways of counting (+-*/)
Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Access to a computer with an internet connection
The course only uses costless software
Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included

Enroll now to receive 15+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!
Who this course is for:
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 a Ph.D. or no education or experience at all

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Data Analysis

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.

Udemy

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