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Machine Learning in Python with 5 Machine Learning Projects

Completing the Python Machine Learning Bootcamp will teach you everything you need to know. Learn 5 Python projects that cover all aspects of machine learning.  

What you'll learn

  • Theory and practical implementation of linear regression using sklearn
  • Theory and practical implementation of logistic regression using sklearn
  • Feature selection using RFECV
  • Data transformation with linear and logistic regression.
  • Evaluation metrics to analyze the performance of models
  • Industry relevance of linear and logistic regression
  • Mathematics behind KNN, SVM and Naive Bayes algorithms
  • Implementation of KNN, SVM and Naive Bayes using sklearn
  • Attribute selection methods- Gini Index and Entropy
  • Mathematics behind Decision trees and random forest
  • Boosting algorithms:- Adaboost, Gradient Boosting and XgBoost
  • Different Algorithms for Clustering
  • Different methods to deal with imbalanced data
  • Correlation Filtering
  • Variance Filtering
  • PCA & LDA
  • Content and Collaborative based filtering
  • Singular Value Decomposition
  • Different algorithms used for Time Series forecasting
  • Case studies

Requirements

  • For this Machine Learning in Python with 5 Machine Learning Projects course to make sense, you should be familiar with linear algebra, calculus, statistics, probability, and python programming language.

Description

Crazy about Data Science and Machine Learning?

This Machine Learning in Python with 5 Machine Learning Projects course is a perfect fit for you.

This Machine Learning in Python with 5 Machine Learning Projects course will teach you how to use Machine Learning step by step.

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Machine learning uses computer algorithms to automate the process of constructing analytical models. In general, a branch of Artificial Intelligence that makes use of the principle of learning from data, identifying patterns, and making decisions without human involvement.

There are many places where machine learning is active now, perhaps more than one would expect.

It contains a lot of topics and this Machine Learning in Python with 5 Machine Learning Projects course will cover all step by step.

This Machine Learning course will give you theoretical as well as practical knowledge of Machine Learning.

This Machine Learning course is fun as well as exciting.

It will cover all common and important algorithms and will give you the experience of working on some real-world projects.

This Machine Learning in Python with 5 Machine Learning Projects course will cover the following topics:-

1. Theory and practical implementation of linear regression using sklearn.

2. Theory and practical implementation of logistic regression using sklearn.

3. Feature selection using RFECV.

4. Data transformation with linear and logistic regression.

5. Evaluation metrics to analyze the performance of models

6. Industry relevance of linear and logistic regression.

7. Mathematics behind KNN, SVM, and Naive Bayes algorithms.

8. Implementation of KNN, SVM, and Naive Bayes using sklearn.

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9. Attribute selection methods- Gini Index and Entropy.

10. Mathematics behind Decision trees and random forest.

11. Boosting algorithms:- Adaboost, Gradient Boosting, and XgBoost.

12. Different algorithms for clustering

13. Different methods to deal with imbalanced data.

14. Correlation filtering

15. Variance filtering

16. PCA & LDA

17. Content and Collaborative based filtering

18. Singular Value decomposition

19. Different algorithms used for Time Series forecasting.

20. Case studies

We have covered each and every topic in detail and also learned to apply them to real-world problems.

You can practice plenty of exercises and also learn about five bonus Python Machine Learning Projects: “Employee Promotion (aff) Prediction”, “Predicting Medical Health Expenses”, “Determining Status for Loan Applicants” and “Optimizing Crop Production”.

As part of this Python Machine Learning Employee Promotion (aff) Prediction project,  you will learn how to Implement a Predictive Model for Identifying the Right Employees deserving of Promotion (aff). Learn how to balance a dataset that is unbalanced.

You will learn how to implement a Regression Analysis Predictive Model for Predicting the Future Medical Expenses for People using Linear Regression, Random Forest, Gradient Boosting, etc. in the Python Machine Learning Predicting Medical Health Expenses project.

You will learn how to implement a Classification Analysis Predictive Model for Determining whether a Person should be Granted a Loan or Not in this Python Machine Learning Determining Status for Loan Applicants project.

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You will learn how to implement a Classification Analysis Predictive Model for Determining whether a Person should be Granted a Loan or Not in this Python Machine Learning Determining Status for Loan Applicants project.

You will make use of all the topics read in this Machine Learning in Python with 5 Machine Learning Projects course.

You will also have access to all the resources used in this course.

Enroll now and become a master in machine learning.

Who this course is for:

  • Anyone who want to start a career in Machine Learning.
  • Students who have at least knowledge in linear algebra, calculus, statistics, probability and who want to start their journey in Machine Learning.
  • Any people who want to level up their Machine Learning Knowledge.
  • Software developers or programmers or Tech lover who want to change their career path to machine learning.
  • Technologists who are curious about how Machine Learning works in the real world.
  • Anyone who has already started their data science journey and now want to master in machine learning.
  • If you have no prior coding or scripting experience, This course is completely for you. This Course also includes Python Fundamental for beginners.

Created by Data Is Good Academy
Last updated 7/2021
English
Size: 20.82 GB

Download Course
https://www.udemy.com/course/machine-learning-data-science-python/

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