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
Learn Machine Learning from scratch, this course for beginners 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:
Introduction to Machine Learning
Supervised, Unsupervised and Reinforcement learning
Types of machine learning
Principal Component Analysis (PCA)
Confusion matrix
Under-fitting & Over-fitting
Classification
Linear Regression
Non-linear Regression
Support Vector Machine Classifier
Linear SVM machine model
Non-linear SVM machine model
Kernel technique
Project of SVM in Python
K-Nearest Neighbors (KNN) Classifier
k-value in KNN machine model
Euclidean distance
Manhattan distance
Outliers of KNN machine model
Project of KNN machine model in Python
Naive Bayes Classifier
Byes rule
Project of Naive Bayes machine model in Python
Logistic Regression Classifier
Non-linear logistic regression
Project of Logistic Regression machine model in Python
Decision Tree Classifier
Project of Decision Tree machine model in Python
