Machine Learning for Beginner

Machine Learning for Beginner

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What you'll learn:
  • Fundamental of Machine Learning; Introduction, 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
  • Classifier; Introduction, non-linear logistic regression, sigmoid function, project in Python
  • Decision Tree Classifier; Introduction, project 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 and graphical explanations. Some daily projects have been solved by using Python . Downloadable files of ebooks and Python codes have been attached to all the sections. The lectures are appealing, fancy and fast. They take less to walk you through the whole . 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 , replacing 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. Non-linear Regression
  9. Support Vector Machine Classifier
  10. Linear SVM machine model
  11. Non-linear SVM machine model
  12. Kernel technique
  13. Project of SVM in Python
  14. K-Nearest Neighbors (KNN) Classifier
  15. k-value in KNN machine model
  16. Euclidean distance
  17. Manhattan distance
  18. Outliers of KNN machine model
  19. Project of KNN machine model in Python
  20. Naive Bayes Classifier
  21. Byes rule
  22. Project of Naive Bayes machine model in Python
  23. Logistic Regression Classifier
  24. Non-linear logistic regression
  25. Project of Logistic Regression machine model in Python
  26. Decision Tree Classifier
  27. Project of Decision Tree machine model in Python

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Who this course is for:
  • Beginners of Machine learning developers curious about machine model

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