Machine Learning For Beginner (Ai) - Data Science

Learn Machine Learning from scratch. Mathematical & Graphical explanation, Python projects and ebooks

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

Requirements
Basics of Python

Description
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 LearningSupervised, Unsupervised and Reinforcement learningTypes of machine learningPrincipal Component Analysis (PCA)Confusion matrixUnder-fitting & Over-fittingClassificationLinear RegressionNon-linear RegressionSupport Vector Machine ClassifierLinear SVM machine modelNon-linear SVM machine modelKernel techniqueProject of SVM in PythonK-Nearest Neighbors (KNN) Classifierk-value in KNN machine modelEuclidean distance Manhattan distanceOutliers of KNN machine modelProject of KNN machine model in PythonNaive Bayes ClassifierByes ruleProject of Naive Bayes machine model in PythonLogistic Regression ClassifierNon-linear logistic regressionProject of Logistic Regression machine model in PythonDecision Tree ClassifierProject of Decision Tree machine model in Python

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