5 Machine Learning Projects from Dataisgood / Great Reviews
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.
- Implementation of Correlation Filtering.
- Implementating Variance Filtering.
- Implementation of PCA & LDA.
- Implementation of Content and Collaborative based filtering.
- Implementing Singular Value Decomposition.
- Implementation of Different algorithms used for Time Series forecasting.
- Case studies.
- Hands on Real-World examples.
- To make sense out of this course, you should be well aware of linear algebra, calculus, statistics, probability and python programming language.
- A positive attitude to success.
- Persistence in learning
Crazy about Data Science and Machine Learning?
This course created by expert instructors at Dataisgood is a perfect fit for you.
This course will take you 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 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 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 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.
There are lots and lots of exercises for you to practice and also a 5 bonus Python Machine Learning Project "Employee Promotion Prediction", "Predicting Medical Health Expenses", "Determining Status for Loan Applicants" and "Optimizing Crop Production".
In this Python Machine Learning 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 Python Machine Learning 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 Python Machine Learning 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 Python Machine Learning 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 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.
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We have helped tens of thousands of people successfully transition into Tech Careers. To make an informed decision about our courses, we encourage you to explore the reviews about Dataisgood on platforms like Udemy. These reviews will provide valuable insights to guide you toward a smarter and more fulfilling educational journey.