This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modelling relates to Machine Learning, and do a comparison of each.Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!Explore many algorithms and models:• Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.• Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.Get ready to do more learning than your machine!This course helps participants understand what data scientists do, the problems they solve, and the tools and techniques they use. Through in-class simulations, participants apply data science methods to real-world challenges in different industries and, ultimately, prepare for data scientist roles in the field.
Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, and develop concrete skills such as:
1. How to identify potential business use cases where data science can provide impactful results
2. How to obtain, clean and combine disparate data sources to create a coherent picture for analysis
3. What statistical methods to leverage for data exploration that will provide critical insight into your data
4. Where and when to Python for data science pipelines
5. What machine learning technique to use for a particular data science project
6. How to implement and manage recommenders using Python, and how to set up and evaluate data experiments
7. What are the pitfalls of deploying new analytics projects to production, at scale
This course is suitable for students, developers, data analysts, and statisticians with basic knowledge of Computer Science.
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