Machine Learning Practical Course

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Onesinus Tamba

5:06:58

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1. Introduction

1. Introduction.mp4

00:48

2. What exactly machine learning is.mp4

06:26

3. Machine Learning Types.mp4

05:00

4. ML Tools.mp4

05:47

2. Basic Knowledge & Practice

1. Dataset Repository.mp4

03:06

2.1 Load Dataset Source Code Example.html

2. Load the dataset.mp4

08:41

3.1 Basic Commands.html

3. Basic Commands Data Preparation.mp4

04:31

4.1 Basic Visualization Source Code.html

4. Basic Data Visualization.mp4

11:26

5.1 Test Train Split Source Code Example.html

5. Train Test Split.mp4

11:30

6.1 Source Code Algorithm Implementation.html

6. Algorithms implementation.mp4

10:40

3. Introduction to Algorithms Modeling

1. Decision Tree Classification.mp4

08:01

2.1 Algorithms executor source code.html

2. Support Vector Classification.mp4

10:11

3. Random Forest Classification.mp4

06:12

4.1 XGB + Label Encoding.html

4. Xgb Classification.mp4

07:46

5. Gradient Boosting Classification.mp4

03:44

6. Neural Network Classification.mp4

05:28

7. Logistic Regression & KNN.mp4

04:26

8.1 All codes for this session.html

8. Add other performance metrics.mp4

08:13

4. [Study Case] Intrusion Detection System (practicing with different datasets)

1. Introduction.mp4

01:26

2. Intrusion Detection System.mp4

02:11

3. Quick review about dataset.mp4

03:46

4. Load the Dataset.mp4

04:37

5.1 Feature Names List.html

5. Define features name.mp4

09:31

6. Dataframe info and describe.mp4

01:24

7.1 Set Class Value.html

7. Data preprocessing - set class values.mp4

02:56

8.1 Pie Plot Code Example.html

8. Pie Plot to know the data distribution.mp4

06:56

9.1 Data Scaling with RobustScaler Code Example.html

9. Data Preprocessing - Scaling the data with RobustScaler.mp4

14:06

10.1 Define features and target.html

10. Define x and y variable.mp4

01:53

11.1 PCA Code Example.html

11. Principal Component Analysis (PCA) - Feature Selection.mp4

08:44

12.1 Test Train Split Code Example.html

12. Splitting data for training and testing phase.mp4

07:36

13.1 Classifiers Executor Code Example.html

13. Create a method as classifier executor.mp4

17:42

14.1 The implementation of algorithms example.html

14. Classifiers Algorithms implementation.mp4

16:26

15.1 Feature Importances Code Example.html

15. Create a method to list down features importance.mp4

11:01

16.1 Plot Tree Code Example.html

16. Plot tree.mp4

04:11

17.1 Random Forest Code Example.html

17. Other Algorithms - Random Forest.mp4

03:24

18.1 Source Code Example of XGboost Regressor, Classifier, Mathplotlib Legend.html

18. XGBoost (Regressor, Classifier, Plot Legend Actual vs Predicted value).mp4

13:41

19.1 Use Reduced Data.html

19. The use of reduced data.mp4

04:11

20.1 Metrics Performances value display in bar chart.html

20. All Algorithms score comparison in bar chart.mp4

08:23

21.1 Save, load data and model then make a new prediction.html

21. Load saved input and model then making new prediction.mp4

10:07

22.1 Source Code Example of Cross Validation (CV).html

22. Cross Validation.mp4

06:04

23.1 Grid Search CV Code Example.html

23. Gridsearch CV.mp4

07:02

5. Nice to know (short brief only, no practice)

1. Clustering.mp4

01:56

2. K-Means.mp4

01:23

3. Bias and Variance.mp4

03:41

4. Bootstrap Aggregating (Bagging).mp4

02:56

5. Boosting.mp4

02:41

6. Ensemble Modeling.mp4

02:56

7. Model Optimization.mp4

02:11

Description

Learn Machine Learning using PYTHON and SKLEARN

What You'll Learn?

Python for Machine Learning
Machine Learning
Basic Data Science
Hands-on ML
Case Study (Intrusion Detection System)
Case Study (Missing Data Imputation)

Who is this for?

Anyone who wants to learn machine learning practically

What You Need to Know?

Basic Python Coding Skill

More details

Description
Python, Machine Learning, Scikit Learn, Algorithms, Classification, Machine Learning Case Study, Dataset, Machine Learning Techniques, Machine Learning Terms, Google Collab

Welcome to our innovative and practical Python-based machine learning course! This course is specifically designed to equip you with the skills needed for developing intrusion detection systems using machine learning technology. With a primary focus on the Python programming language and leveraging the scikit-learn (sklearn) library, this course provides a robust foundation for understanding machine learning concepts and their real-world applications.

You will gain expertise in implementing machine learning techniques using the scikit-learn library, delving into profound insights from the Intrusion Detection System dataset, which serves as the primary case study. Throughout the course, you'll develop a deep understanding of machine learning algorithms, data preprocessing, and model evaluation, learning how to apply these concepts effectively in the context of intrusion detection.

Combining structured theory and hands-on labs, this course not only enhances your knowledge of machine learning but also instills confidence to tackle professional challenges. The certificate earned upon completion adds significant value to your profile. Join now to seize better career opportunities in the field of machine learning and become an expert in intrusion detection using Python and scikit-learn.

Important Note:Â Every codes we will practice in this course can you get on Resources section, find the source code link for every video that contains code

Thank You.
Who this course is for:
Anyone who wants to learn machine learning practically

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Machine Learning

Onesinus Tamba

Instructor's Courses

Onesinus Saut Parulian is a practitioner in the IT field, specifically in the field of software development since 2015. His educational background was in computer and network engineering at a vocational high school which was then continued with a major in informatics engineering. has a hobby of writing ebooks and sharing knowledge. He really likes the field of programming, making the author continue his postgraduate education in the field of computer science in 2023 while continuing his professional career as an Engineering Manager at a startup.--Onesinus Saut Parulian adalah seorang praktisi di bidang IT secara spesifik di bidang software development sejak tahun 2015. Latar belakang pendidikan teknik komputer dan jaringan pada sekolah menengah kejuruan yang kemudian dilanjutkan dengan jurusan teknik informatika. memiliki hobi menulis ebook dan berbagi pengetahuan. Sangat menyukai bidang programming menjadikan penulis melanjutkan pendidikan pascasarjana di bidang ilmu komputer pada tahun 2023 ini sambil terus melanjutkan perjalanan karir profesional sebagai Engineering Manager di salah satu startup.

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