One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation.
About this Course
What you will learn
Use the basic components of building and applying prediction functions
Understand concepts such as training and tests sets, overfitting, and error rates
Describe machine learning methods such as regression or classification trees
Explain the complete process of building prediction functions
Skills you will gain
- Random Forest
- Machine Learning (ML) Algorithms
- Machine Learning
- R Programming
Syllabus - What you will learn from this course
Week 1: Prediction, Errors, and Cross Validation
Week 2: The Caret Package
Week 3: Predicting with trees, Random Forests, & Model Based Predictions
Week 4: Regularized Regression and Combining Predictors
- 5 stars66.44%
- 4 stars22.33%
- 3 stars6.89%
- 2 stars2.51%
- 1 star1.80%
TOP REVIEWS FROM PRACTICAL MACHINE LEARNING
Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.
Great primer for machine learning with ample additional resources for those who are interested. I feel this course gave me a solid basis to delve deeper into the topic.
A well descriptive experience for this subject; really steps into how to handle information and how to extract info from them. You need to be prepared with Regression Models, it's the base of it.
Some of the terms used here vary from the terms used in the industry. For example recall, precision etc. Overall this is a very good course with provides basics of machine learning.
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