Machine Learning with Python online course: The Complete Guide

1. Machine Learning With Python Machine learning online course - skillup.online

2. COURSE DETAILS 01 Classify data 01 DETAILS Mercury is the closest planet to the Sun

3. Machine Learning With Python Certification Online Course Starts On 02 February 2022 Enrollment closes on 31 December 2030 TAP BELOW TO ENROLL NOW !!! Machine Learning Online Course

4. About The Course This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known programming language. Through hands-on tutorials, students will test their skills with various algorithms and techniques to build smart systems that are capable of making predictive and actionable decisions. From data visualization to data analysis, you’ll learn how to implement machine learning in your own projects.

5. Explore many algorithms and models: Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction. 01 Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. 02 Get ready to do more learning than your machine!

6. Course Syllabus Supervised vs Unsupervised Learning Module 1 1. Machine Learning vs Statistical Modelling 3. Supervised Learning Classification 2. Supervised vs Unsupervised Learning 4. Unsupervised Learning Module 2 Supervised Learning I 1. K-Nearest Neighbors 3. Random Forests 5. Advantages & Disadvantages of Decision Trees 2. Decision Trees 4. Reliability of Random Forests Module 3 Supervised Learning II 1. Regression Algorithms 3. Model Evaluation: Overfitting & Underfitting 4. Understanding Different Evaluation Models 2. Model Evaluation

7. Course Syllabus Module 4 Unsupervised Learning 1. K-Means Clustering plus Advantages & Disadvantages 2. Hierarchical Clustering plus Advantages & Disadvantages 3. Measuring the Distances Between Clusters - Single Linkage Clustering 4. Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering 5.Density-Based Clustering Dimensionality Reduction & Collaborative Filtering Module 5 1. Collaborative Filtering & Its Challenges 2. Dimensionality Reduction: Feature Extraction & Selection

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