Machine Learning course

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Price: 24.99$

This course will cover following topics1. Basics of machine learning2. Supervised and unsupervised learning3. Linear regression 4. Logistic regression5. KNN Algorithm6. Nave Bayes Classifier7. Random forest Algorithm8. Decision Tree Algorithm7. Principal component analysis8. K-means clustering9. Agglomerative clustering 10. There will practical exercise based on Linear regression, Logistic regression , Naive Bayes, KNN algorithm, Random forest, Decision tree, K-Means, PCA.11. There will be quiz for each topics and total 200 Questions on machine learning course We will look first in to linear Regression, where we will learn to predict continuous variables and this will details of Simple and Multiple Linear Regression, Ordinary Least Squares, Testing your Model, R-Squared and Adjusted R-Squared. We will get full details of Logistic Regression, which is by far the most popular model for Classification. We will learn all about Maximum Likelihood, Feature Scaling, The Confusion Matrix, Accuracy Ratios.. and you will build your very first Logistic Regression We will look in to Nave bias classifier which will give full details of Bayes Theorem, implementation of Nave bias in machine learning. This can be used in Spam Filtering, Text analysis, Recommendation Systems. Random forest algorithm can be used in regression and classification problems. This gives good accuracy even ifdata is incomplete. Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. We will look in to KNN algorithm which will working way of KNN algorithm, compute KNN distance matrix, Makowski distance, live examples of implementation of KNN in industry. We will look in to PCA, K-means clustering, Agglomerative clustering which will be part of unsupervised learning. Along all part of machine supervised and unsupervised learning , we will be following data reading , data prerprocessing, EDA, data scaling, preparation of training and testing data along machine learning model selection , implemention and prediction of models.