Published 3/2023
Created by Satyendra Singh (NCFM and NSIM certified ) Technical analyst, Research analyst and portfolio manager
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 14 Lectures ( 4h 57m ) | Size: 2.64 GB
Basics of machine learning,Linear Regression,Logistic Regression, Naïve Bayes ,KNN alogrthim , K-means, PCA, Custering,
What you’ll learn
Basics of machine learning
Linear Regression
Logistic Regression
KNN alogrithm
Clustering
K-Means Clustering
Principal component analysis
Data preprocsseing
EDA
The Machine Learning Process
Naive Bayes Classifier
Supervised learning and unsupervised learning
Confusion Matrix
The Elbow Method
Feature Scaling
Feature Scaling
Make Predictions
Splitting your data into a Training set and a Test set
Classification
Machine Learning preparation
Ordinary Least Squares
Accuracy
Requirements
Learner should be aware of basic python
Description
This course will cover following topics1. Basics of machine learning2. Supervised and unsuperivsed learning3. Linear regression 4. Logistic regression5. KNN Algorithm6. Naive Bayes Classifier7. Principal component analyis8. K-means clustering9. Agglomerative clustering 10. There will pratical excerscise based on Linear regression, Logistic regression,Navie Bayes,K-Means, PCA 11. There will be quiz for each topics and total 200 Questions on machine learning courseWe 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 RegressionWe will look in to Navie bais classifier which will give full details of Bayes Theorem, implemention of Navie bais in machine learning. This can be used in Spam Filtering, Text analysis, •Recommendation Systems.We will look in to KNN alogrithm which will working way of KNN alogrithm, compute KNN distance matrix, Minkowski distance, live examples of implemention 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.
Who this course is for
Anyone interested in Data Science
Data Science professionals
Machine learning engineer
Learner who want to use Machine Learning to their CV or career toolkit
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