About the course
Machine Learning projects with Python’s own Scikit-learn on real-world datasets; Video Tutorial
Scikit-learn is arguably the most popular Python library for Machine Learning today. Thousands of Data Scientists and Machine Learning practitioners use it for day to day tasks throughout a Machine Learning project’s life cycle. Due to its popularity and coverage of a wide variety of ML models and built-in utilities, jobs for Scikit-learn are in high demand, both in industry and academia.
If you’re an aspiring machine learning engineer ready to take real-world projects head-on, Hands-on Scikit-Learn for Machine Learning will walk you through the most commonly used models, libraries, and utilities offered by Scikit-learn.
By the end of the course, you will have a set of ML problem-solving tools in the form of code modules and utility functions based on Scikit-learn in one place, instead of spread over several books and courses, which you can easily use on real-world projects and data sets.
All the code and supporting files for this course are available on Github at: https://github.com/PacktPublishing/Hands-on-Scikit-learn-for-Machine-Learning-V-
Style and Approach
The course enables you to immediately apply its topics to real world data sets via step-by-step code walk-through. We take a data set through several concepts such as preprocessing and cleaning, data preparation, modeling, feature extraction and engineering, dimensionality reduction, hyper-parameter tuning, and model performance enhancement while giving tips and techniques on how to choose from different models and approaches and make the best use of Scikit-learn modules.
What You Will Learn
- Tackle real-world problems in Machine Learning through a structured process using Scikit-learn
- Achieve substantially more in less time and with much less code by leveraging the power and simplicity of Scikit-learn
- Develop a thorough understanding of core predictive analytics with regression, classification, and unsupervised learning such as clustering and PCA
- Create ensemble models with Random-Forest and Gradient-boosting methods and see your model performance improve drastically
- Build a portfolio of tools and techniques that can readily be applied to your own projects
- Discover the intuition behind contemporary Machine Learning models and algorithms without going into deep mathematical details
- Develop the ability to evaluate and improve the accuracy and performance of Machine Learning models
- Explore the foundations of text analytics and develop a set of tools to apply to your common text-analysis tasks
Farhan Nazar Zaidi
Farhan Nazar Zaidi has 25 years' experience in software architecture, big data engineering, and hands-on software development in a variety of languages and technologies. He is skilled in architecting and designing networked, distributed software systems and data analytics applications, and in designing enterprise-grade software systems.
Farhan holds an MS in Computer Science from University of Southern California, Los Angeles, USA and a BS in Electrical Engineering from University of Engineering, Lahore, Pakistan. He has worked for several Silicon-Valley companies in the past in the US as a Senior Software Engineer, and also held key positions in the software industry in Pakistan. Farhan works as consultant, solutions developer, and in-person trainer on big data engineering, microservices, advanced analytics, and Machine Learning.
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