Data Analytics With
Machine Learning
Machine Learning and
Modeling Techniques Course
Students will learn how to build and implement machine learning models that can help their organization answer big data questions. Students will learn some of the most popular and used algorithms for supervised and unsupervised machine learning.
Some topics this unit will cover following machine learning algorithms:
- Linear regression
- Logistic regression
- KNN
- Decision Trees
- Random Forests
- Scikit-Learn
Machine Learning and
Modeling Techniques Course
Students will learn how to build and implement machine learning models that can help their organization answer big data questions. Students will learn some of the most popular and used algorithms for supervised and unsupervised machine learning.
Some topics this unit will cover following machine learning algorithms:
- Linear regression
- Logistic regression
- KNN
- Decision Trees
- Random Forests
- Scikit-Learn
Outcomes of this unit
- A solid foundation of machine learning techniques, and how to use them in Python.
- Understand and use the modeling process and why it’s important for all machine learning.
- How to leverage Python’s advanced libraries to perform Machine Learning.
Projects
- Use Machine Learning techniques to predict if Boston housing market prices
- Use Machine Learning techniques to predict if a passenger was more likely to survive the Titanic.
- Use Machine Learning to explore Amazon Alexa Review data and perform sentiment analysis
Upcoming Data Analytics with Machine Learning Classes
For more info about this course, download the PDF using this form.
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