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Master Machine Learning with Practical Case Studies - nieriorefasow63 - 08-31-2024
![[Image: b62c2cb4727d1a280747ac75a52a5a8c.jpg]](https://i123.fastpic.org/big/2024/0831/8c/b62c2cb4727d1a280747ac75a52a5a8c.jpg)
Master Machine Learning with Practical Case Studies
Published 8/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 6h 36m | Size: 2.93 GB
Hands on Machine Learning with Algorithms and Case Studies using different Datasets
What you'll learn
How to use Machine Learning Model for making Predictions for Real Life Problems
Understand Machine Learning to Apply in Real Practical Scenarios
Master Machine Learning techniques
Develop Insights for Data Wrangling, Data Cleansing, Data Enrichment, Data Analytics using Machine Learning
Build Linear Regression Model
Build Logistic Regression Model
Build Decision Tree Model
Understand ARIMA
Implement KMeans Clustering
Implement Naive Bayes
Understand Boosting Algorithms
Build XGBRegressor Model
Requirements
No prior Programming Experience required.
Description
Dive into the world of machine learning with "Master Machine Learning with Practical Case Studies." This comprehensive course is designed for those who want to move beyond theory and gain hands-on experience in applying machine learning algorithms to real-world problems. Throughout the course, you'll explore a variety of machine learning techniques and methodologies, learning how to effectively implement and fine-tune algorithms for diverse datasets. You'll work with case studies spanning multiple domains, including finance, healthcare, and e-commerce, providing a broad perspective on how machine learning can be leveraged across industries.Key features of the course include
ractical Case Studies: Analyze and solve real-world problems using detailed case studies, gaining insights into best practices and industry applications.Hands-On Projects: Engage in practical exercises that involve building, training, and evaluating machine learning models.Algorithm Deep Dive: Understand the theory and application of popular machine learning algorithms like Linear Regression, Logistic Regression, Decision Tree, Random Forest, Naive Bayes, K-means and Boosting AlogrithmsDiverse Datasets: Work with a variety of datasets to learn how to handle different types of data and preprocessing techniques.By the end of the course, you'll have confidence to apply your skills to complex problems. Perfect for aspiring data scientists, analysts, and machine learning practitioners, this course will equip you with the tools and knowledge needed to excel in the evolving field of machine learning.Who this course is for
Beginner Fundamentals of Machine Learning
Learn Concepts of Machine Learning
HOMEPAGE
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