Explanation
This course will take you step by step into the world of Machine Learning. Machine Learning is the study of computer algorithms that automate the building of analytical models. It is a branch of artificial intelligence based on the idea that systems can learn from data, recognize patterns and make decisions with minimal human intervention. Machine Learning is actively used today, perhaps in more areas than one might expect. It covers many topics and the course will cover all of them step by step. This Machine Learning course will give you an insight and practical knowledge about Machine Learning. This Machine Learning course is fun as well as interesting. It will cover all the common and important algorithms and give you the experience of working on real world projects. We covered each topic in detail and also learned to apply it to real world problems. There are many more exercises that you can practice as well as 5 bonus Python Machine Learning Project “Employee Motivation Forecasting”, “Health Care Cost Forecasting”, “Determining the Status of Loan Applicants” and “Productivity Optimization”. Crop”.
In this Python Machine Learning Employee Motivation Project, you will learn how to implement a Predictive Model to Identify the Right Employees Who Are Eligible for Promotion. Also, learn how to balance databases. In this Python Machine Learning Project on Forecasting Health Costs, you will learn how to implement a Regression Analysis Forecasting Model for Forecasting Future Health Costs of People using Linear Regression, Random Forest, Gradient Boosting, etc. In this Python machine for determining the level of learning of project loan applicants, you will learn how to implement a Classification Prediction Model to determine whether a person should be granted a loan or not. In this Python Machine Learning Optimizing Crop Yield project, you will learn about Precision Farming using Data Science Technologies such as Convergent Analysis and Discrete Analysis. You will be able to recommend the best crops to farmers to increase their production.
What will you learn?
- Theory and practical implementation of linear regression using sklearn.
- Theory and practical implementation of logistic regression using sklearn.
- Feature selection using RFECV.
- Data transfer with linear and logistic delay.
- Evaluation parameters to analyze the performance of models.
- Vertical industrial fit and reverse logistics.
- The math behind KNN, SVM and Naive Bayes algorithms.
- Implementation of KNN, SVM and Naive Bayes using sklearn.
- Feature selection methods- Gini Index and Entropy.
- The mathematics behind decision trees and random forests.
- Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost.
- Different Clustering Algorithms.
- Different methods for dealing with unbalanced data.
- Implementing Link Filtering
Who is this course for?
- Theory and practical implementation of linear regression using sklearn.
- Theory and practical implementation of logistic regression using sklearn.
- Feature selection using RFECV.
- Data transfer with linear and logistic delay.
- Evaluation parameters to analyze the performance of models.
- Vertical industrial fit and reverse logistics.
- The math behind KNN, SVM and Naive Bayes algorithms.
- Implementation of KNN, SVM and Naive Bayes using sklearn.
- Feature selection methods- Gini Index and Entropy.
- The mathematics behind decision trees and random forests.
- Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost.
- Different Clustering Algorithms.
- Different methods for dealing with unbalanced data.
- Implementing Link Filtering
Machine Learning Guide 2022 A to Z: 5 Machine Learning Projects
- Publisher: Udemy
- Teacher: Information Is Good Academy
- Language : English
- Level: All levels
- Number of courses: 412
- Duration: 26 hours and 15 minutes
Contents 2022 Machine Learning A to Z: 5 Machine Learning Projects
Requirements
- To make sense of this course, you must be well versed in linear algebra, calculus, statistics, probability and the Python programming language.
- A positive attitude for success.
- Persistence in learning
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