This comprehensive Supervised and Unsupervised Machine Learning program will equip you with essential skills for data modeling and analysis. You鈥檒l master regression techniques, classification models, and clustering algorithms to address real-world challenges and drive impactful data solutions. By the end of this course, you will be able to: - Master Regression Techniques: Learn linear and logistic regression to predict variables and classify data, and select the right method for your projects. - Apply Classification Models: Gain expertise in Decision Trees, Random Forest, and Naive Bayes for accurate data analysis and predictions. - Implement Clustering Algorithms: Understand and apply K-Means Clustering to identify patterns, group data, and solve tasks like segmentation and recognition. - Solve Real-World Problems: Use supervised and unsupervised learning techniques to tackle complex challenges and make data-driven decisions. Guided by experts, you鈥檒l acquire practical skills to excel in machine learning and deliver innovative solutions across industries.
Types of Clustering
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Skills You'll Learn
Regression Analysis, Linear Regression, Classification of ML, Random Forest Algorithm, Decision Tree in ML, K Means Clustering Algorithm, Logistic Regression, Linear vs Logistic Regression
From the lesson
Unsupervised Learning 鈥� Clustering Algorithms
Explore clustering techniques, focusing on K-Means, its applications, and real-world use cases.
Taught By
Simplilearn Instructor