Machine Learning for Beginner




What you will 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

Description

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
English
language

Content

Introduction

Introduction

Introduction to Machine Learning

What is Machine learning?
Understand machine learning concept by the examples we face in daily life
Machine learning vs Deep learning vs Artificial intelligence vs Data Science
What are the fields where we can use Machine learning?
Types of Machine learning
What are supervised, unsupervised and reinforcement of machine learning?
Methods for evaluating the machine model
Types of famous classifiers of Machine learning
How to develop a Machine model in Python? And what are the steps?
ebook

Principal Component Analysis (PCA)

Introduction to Principal Component Analysis (PCA)
How principal component analysis works? Check it graphically
Mathematical perspective of PCA. What is covariance matrix?
Understand PCA mathematically by solving an example of covariance matrix
ebook

Confusion Matrix, Under-fitting & Over-fitting

Table of a confusion matrix and derive the predicted and actual values
Accuracy and Error rate of a Machine model
Precision and Recall of a Machine model
Under-fitting & Over-fitting of a Machine model. Understand it graphically
ebook

Classification and Regression

Classification process of machine model to explore and analyze the data
Regression process of a machine model. What is leaner regression?
Understand linear regression mathematically and graphically
Non-linear regression or Logistic regression of a machine model
ebook

Support Vector Machine (SVM) Classifier

Introduction to Support Vector Machine (SVM)?
Block diagram of support vector machine. How support vector machine works?
Linear support vector machine. Understand it graphically
Optimal Hyperplane of a linear support vector machine. Optimal vs Not optimal
How linear SVM model predicts? Give a new data and check it graphically
Non-linear support vector machine. Graphs of non-linear SVM models
Kernel technique of non-linear SVM model. Three types of kernel techniques
Transformation from 1-D to 2-D using non-linear kernel trick to separate data
Transformation from 2-D to 3-D using non-linear kernel trick to separate data
Applications of support vector machine (SVM) in daily life
Project: Develop a support vector machine model in Python
Codes: Colab file and Python file. SVM machine model in Python programming
ebook

K-Nearest Neighbors Classifier (KNN)

Introduction to K-Nearest Neighbors classifier (KNN)
How to KNN machine model works? Understand it graphically
Concept of KNN machine model with a daily a life example. Check it graphically
Choosing of an accurate of k value. Why k value is important in KNN model?
Outliers in KNN model. Why outliers of a data is recommended in KNN model?
Euclidean distance formula to measure the distance between two data points
Manhattan distance formula to measure the distance between two data points
Project: Develop a KNN machine model in Python codes
Codes: Colab file and Python file. KNN machine model in Python programming
ebook

Naive Bayes Classifier

Introduction to Naive Bayes Classifier. What is Bayes rule?
Understand Naive Bayes rule by giving a daily life example
Solve an example to cement the concept of Bayes rule
Project: Develop a Naive Bayes machine model in Python codes
Codes: Colab file and Python file. Naive Bayes model in Python programming
ebook

Logistic Regression Classifier

What is Logistic Regression Classifier? Formula of Logistic regression model
Understand Logistic Regression machine model graphically
How to separate a non-linear data by using logistic regression machine model?
Applications of Logistic Regression machine model in daily life
Project: Develop a Logistic Regression machine model in Python codes
Codes:Colab file and Python file.Logistic regression model in Python programming
ebook

Decision Tree Classifier

Introduction to a Decision Tree Classifier
Block diagram of a Decision Tree Classifier. How Decision Tree model works?
Project: Develop a Decision Tree machine model in Python codes
Codes: Colab file and Python file. Decision Tree model in Python programming
ebook

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