DevCourseWeb.com    Tutorials    Development    Master Machine Learning with Practical Case Studies

Master Machine Learning with Practical Case Studies

 Admin0,

Hands on Machine Learning with Algorithms and Case Studies using different Datasets

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.

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.

Course Content

  • Linear Regression using House Rent Prediction –> 10 lectures • 40min.
  • Multicollinearity using Car Dataset –> 7 lectures • 36min.
  • Logistic Regression using Credit Card Fraud Classification –> 9 lectures • 52min.
  • SVC using Bank Customer Retirement Classification –> 1 lecture • 9min.
  • Regularization using Sales Price Dataset –> 2 lectures • 20min.
  • Decision Tree –> 2 lectures • 24min.
  • Random Forest using Housing Price Dataset –> 10 lectures • 38min.
  • PCA using Housing Price Prediction –> 2 lectures • 28min.
  • K-Means using Social Media and Customer Segmentation –> 4 lectures • 33min.
  • Naive Bayes using Fraud Classification –> 5 lectures • 18min.
  • AdaBoost using Housing Price Prediction –> 3 lectures • 26min.
  • ARIMA –> 6 lectures • 33min.
  • XGB using Stock Dataset –> 6 lectures • 40min.

Auto Draft

Requirements

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:

  • Practical 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 Alogrithms
  • Diverse 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.