Hands-On Machine Learning with Python: Use scikit-learn to build your own classifiers, regression tools, clustering and sentiment analysis applications

Data science requires understanding the data, building the right model for the data, and operationalizing the model. This course builds on each of these skills with practical activity to understand the steps.
About This Book
* Extensive coverage on formulating the right questions about a given problem to help in model selection
* Taking students through techniques for data processing, manipulation, and transformation
* Providing practical methods for deploying models into production
Who This Book Is For
The reader of this course should have basic knowledge of the Python programming language. He/she must have knowledge of data types in Python, be able to write functions, and also have the ability to import and use libraries and packages in python. Familiarity with basic linear algebra, basic probability, and basic calculus is assumed although not required to fully complete this course.
What You Will Learn
* Use scikit-learn, pandas, numpy library to perform machine learning and data analysis tasks
* Obtain, verify, clean and transform data into a correct format for use
* Perform exploratory analysis and extract features from the data.
* Build models for regression, classification and clustering tasks.
* Evaluate the performance of a model with the right metric
* Deploy a final machine learning model into production
In Detail
This course gives students basic ideas behind machine learning methods as well as a deeper understanding of how and why they work. Emphasis is placed on how to get these algorithms to work in practice, rather than focusing on mathematical derivations. Through a project-based approach, the course gives students the opportunity to implement algorithms themselves and gain experience with them. The course covers various machine learning techniques for both supervised and unsupervised learning approaches. It also goes further to teach students about deploying a model into production.