Hands-on Scikit-learn for Machine Learning

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 9h 03m | 1.57 GB

Machine Learning projects with Python’s own Scikit-learn on real-world datasets

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.

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