Machine Learning for Beginner

Learn Machine Learning from scratch. Theoretical & Graphical explanation of classifiers with projects in Python

Learn Machine Learning from scratch, this course for beginner who want to learn the fundamental of machine learning and artificial intelligence. the course includes video explanation with introductions(basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way. It’s highly recommended for the students who don’t know the fundamental of machine learning studying at college and university level.

What you’ll learn

• Fundamental of Machine Learning; Introduction, types of machine learning, applications.
• Supervised, Unsupervised and Reinforcement learning.
• Principal Component Analysis (PCA); Introduction, mathematical and graphical concepts.
• Confusion matrix, Under-fitting and Over-fitting, classification and regression of machine model.
• Support Vector Machine (SVM) Classifier; Introduction, linear and non-linear SVM model, optimal hyperplane, kernel trick, project in Python.
• K-Nearest Neighbors (KNN) Classifier; Introduction, k-value, Euclidean and Manhattan distances, outliers, project in Python.
• Naive Bayes Classifier; Introduction, Bayes rule, project in Python.
• Logistic Regression Classifier; Introduction, non-linear logistic regression, sigmoid function, project in Python.
• Decision Tree Classifier; Introduction, project in Python.

Course Content

• Introduction –> 1 lecture • 1min.
• Introduction to Machine Learning –> 10 lectures • 34min.
• Principal Component Analysis (PCA) –> 5 lectures • 38min.
• Confusion Matrix, Under-fitting & Over-fitting –> 5 lectures • 17min.
• Classification and Regression –> 5 lectures • 22min.
• Support Vector Machine (SVM) Classifier –> 13 lectures • 1hr 3min.
• K-Nearest Neighbors (KNN) Classifier –> 10 lectures • 1hr 40min.
• Naive Bayes Classifier –> 6 lectures • 1hr 2min.
• Logistic Regression Classifier –> 7 lectures • 51min.
• Decision Tree Classifier –> 5 lectures • 25min.

Requirements

• Basics of Python.

Learn Machine Learning from scratch, this course for beginner who want to learn the fundamental of machine learning and artificial intelligence. the course includes video explanation with introductions(basics), detailed theory and graphical explanations. Some daily life projects have been solved by using Python programming. Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less time to walk you through the whole content. Each and every topic has been taught extensively in depth to cover all the possible areas to understand the concept in most possible easy way. It’s highly recommended for the students who don’t know the fundamental of machine learning studying at college and university level.

The objective of this course is to explain the Machine learning and artificial intelligence in a very simple and way to understand. I strive for simplicity and accuracy with every definition, code I publish. All the codes have been conducted through colab which is an online editor. Python remains a popular choice among numerous companies and organization. Python has a reputation as a beginner-friendly language, replacing Java as the most widely used introductory language because it handles much of the complexity for the user, allowing beginners to focus on fully grasping programming concepts rather than minute details.

Below is the list of topics that have been covered:

1. Introduction to Machine Learning
2. Supervised, Unsupervised and Reinforcement learning
3. Types of machine learning
4. Principal Component Analysis (PCA)
5. Confusion matrix
6. Under-fitting & Over-fitting
7. Classification
8. Linear Regression
9. Non-linear Regression
10. Support Vector Machine Classifier
11. Linear SVM machine model
12. Non-linear SVM machine model
13. Kernel technique
14. Project of SVM in Python
15. K-Nearest Neighbors (KNN) Classifier
16. k-value in KNN machine model
17. Euclidean distance
18. Manhattan distance
19. Outliers of KNN machine model
20. Project of KNN machine model in Python
21. Naive Bayes Classifier
22. Byes rule
23. Project of Naive Bayes machine model in Python
24. Logistic Regression Classifier
25. Non-linear logistic regression
26. Project of Logistic Regression machine model in Python
27. Decision Tree Classifier
28. Project of Decision Tree machine model in Python