Practical scikit-learn for Machine Learning: 4-in-1

Practical scikit-learn for Machine Learning: 4-in-1

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. scikit-learn is arguably the most popular Python library for Machine Learning today. 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.

scikit-learn is one of the most powerful Python Libraries with has a clean API, and is robust, fast and easy to use. It solves real-world problems in the areas of health, population analysis, and figuring out buying behavior, and more!

This comprehensive 4-in-1 course is an easy-to-follow, step-by-step guide that will help you get to grips with real-world applications of algorithms for Machine Learning. You’ll firstly learn how to build and evaluate the performance of efficient models using scikit-learn. Observe data from multiple angles and use machine learning algorithms to solve real-world problem to make your projects successful. Use Regression Trees, Support Vector Machines, K-Means Clustering, and customer segmentation algorithms in real world situations. Finally, apply your knowledge to practical real-world projects using ML models to get insightful solutions!

By the end of this course, you'll build a strong foundation for entering the world of Machine Learning and data science with Python’s own scikit-learn the help of this comprehensive guide!

Contents and Overview

This training program includes 4 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Machine Learning with scikit-learn, covers learning to implement and evaluate machine learning solutions with scikit-learn. This course examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It also discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You’ll learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance.

By the end of this course, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.

The second course, Fundamentals of Machine Learning with scikit-learn, covers building strong foundation for entering the world of Machine Learning and data science. In this course you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi-supervised learning. A few famous algorithms that are covered in this book are: Linear regression, Logistic Regression, SVM, Naive Bayes, K-Means, Random Forest, and Feature engineering. In this course, you will also learn how these algorithms work and their practical implementation to resolve your problems.

The third course, Hands-on scikit-learn for Machine Learning, covers Machine Learning projects with Python’s own scikit-learn on real-world datasets. 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.

The fourth course, Real-World Machine Learning Projects with scikit-learn, covers prediction of heart disease, customer-buying behaviors, and much more in this course filled with real-world projects. In this course you will build powerful projects using scikit-learn. Using algorithms, you will learn to read trends in the market to address market demand. You'll delve more deeply to decode buying behavior using Classification algorithms; cluster the population of a place to gain insights into using K-Means Clustering; and create a model using Support Vector Machine classifiers to predict heart disease.

By the end of the course you will be adept at working on professional projects using scikit-learn and Machine Learning algorithms.

By the end of this course, you'll build a strong foundation for entering the world of Machine Learning and data science with Python’s own scikit-learn the help of this comprehensive guide!

About the Authors

• Giuseppe Bonaccorso is an experienced team leader/manager in AI, machine/deep learning solution design, management, and delivery. He got his MSc Eng in electronics in 2005 from the University of Catania, Italy, and continued his studies at the University of Rome Tor Vergata and the University of Essex, UK. His main interests include machine/deep learning, reinforcement learning, big data, bio-inspired adaptive systems, cryptocurrencies, and NLP.

• 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.

• Nikola Zivkovic is a software developer with over 7 years' experience in the industry. He earned his Master’s degree in Computer Engineering from the University of Novi Sad in 2011, but by then he was already working for several companies. At the moment he works for Vega IT Sourcing from Novi Sad. During this period, he worked on large enterprise systems as well as on small web projects. Also, he frequently talks at meetups and conferences and he is a guest lecturer at the University of Novi Sad.

Machine Learning in practice with Python’s own scikit-learn on real-world datasets!

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What you will learn

• Predict the values of continuous variables using linear regression and K Nearest Neighbors.
• 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.

Rating: 4.35

Level: Beginner Level

Duration: 17.5 hours

Instructor: Packt Publishing