Master Machine Learning Algorithms and Models in Python with hands-on Projects in Data Science. Code workbooks included.
Crazy about Data Science and Machine learning?
This course is a perfect fit for you.
This course will take you to step by step into the world of Machine learning.
Machine learning is the study of computer algorithms that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
Machine learning is actively being used today, perhaps in many more places than one world expects.
It contains a lot of topics and this course will cover all step by step.
This course will give you theoretical as well as practical knowledge of machine learning.
This course is fun as well as exciting.
It will cover all common and important algorithms and give you experience working on some real-world projects.
This 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.
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 are used for Time Series forecasting.
20. Case studies
We have covered each topic in detail and also learned to apply them to real-world problems.
There are lots and lots of exercises for you to practice and also a 5 bonus capstone project “Employee Promotion Prediction,” “Predicting Medical Health Expenses,” “Determining Status for Loan Applicants,” and “Optimizing Crop Production.”
In this Employee Promotion Prediction project, you will learn how to Implement a Predictive Model for Identifying the Right Employees deserving of Promotion. Also, learn how to balance Imbalanced Datasets.
In this Predicting Medical Health Expenses project, 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 this Determining Status for Loan Applicants 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 Optimizing Crop Production project, you will learn about Precision Farming using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend the best Crops to Farmers to Increase their Productivity.
You will make use of all the topics read in this 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 has already started their data science journey and now wanting to master machine learning.
- This course is for machine learning beginners as well as intermediates.
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
PCA & LDA
Content and Collaborative based filtering
Singular Value Decomposition
Different algorithms used for Time Series forecasting
To make sense of this course, you should be well aware of linear algebra, calculus, statistics, probability, and python programming language.
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