Python Machine Learning Projects For Beginners 2023 | Machine Learning With Python | Simplilearn
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This video will help you build various Python Machine Learning Projects from scratch. Starting with Movie Recommendation System, it uses machine learning (ML) to predict or filter users' film preferences based on their prior decisions and actions. Next, we will start with a Sentiment Analysis of Amazon Reviews. Third, we have an implementation of a Parkinson's disease prediction system using different Python libraries. In the end, we will build a loan approval prediction system.
The tutorial covers the following topics:
How to Build A Movie Recommendation System Using Python
Sentiment Analysis of Amazon Reviews
Parkinsonās Disease Detection Using Machine Learning
Loan Approval Prediction using Machine Learning
Link for the Data Sets: https://drive.google.com/drive..../folders/1GAuj8Ge8xA
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all peopleās digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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