Python Machine Learning: From Techniques to Troubleshooting

Machine Learning is no longer the inaccessible domain it used to be. There are over 100,000 Python libraries you can download in one line of code! Machine learning allows us to interpret data structures and fit that data into models to identify patterns and make predictions. Python makes this easier with its huge set of libraries that can be easily used for Machine Learning. If you’re looking to use Machine Learning to make your Python apps smarter & build efficient, progressive models to tackle real-world data then this Course is perfect for you!

This comprehensive 4-in-1 course is an A+ guide to transforming your simple Machine Learning model into a cutting edge powerful version with Python. You’ll initially learn about supervised learning: how to classify data points and predict future numbers. You’ll build efficient, faster, and progressive machine learning models to tackle real data. Moving further, you’ll read, explore, clean, and prepare your data using Pandas, the most popular library for analyzing data tables. You’ll troubleshoot advanced models such as Random Forests and SVMs. Finally, you’ll visualize your decision trees and perform common natural language processing featuring engineering tasks.

By the end of this course, you'll build efficient, faster, and progressive machine learning models to tackle real data. Master the most popular Machine Learning tools by building your own models to tackle real-world problems.

Contents and Overview

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

The first course, Getting Started with Machine Learning in Python, covers using Machine Learning to classify objects, predict future prices, and automatically learn fixes to problems. You will learn to understand and estimate the value of your dataset. We guide you through creating the best performance metric for your task at hand, and how that takes you to the correct model to solve your problem. Understand how to clean data for your application, and how to recognize which Machine Learning task you are dealing with.

The second course, Python Machine Learning Tips, Tricks, and Techniques, covers transforming your simple machine learning model into a cutting edge powerful version. In this course, you will get hands-on experience of solving real problems by implementing cutting-edge techniques to significantly boost your Python Machine Learning skills and, as a consequence, achieve optimized results in almost any project you are working on. By the end of this course, you will know various tips, tricks, and techniques to upgrade your machine learning algorithms to reduce common problems, all the while building efficient machine learning models.

The third course, Building Predictive Models with Machine Learning and Python, covers mastering the most popular Machine Learning tools by building your own models to tackle real-world problems. This course will guide you through the tools in the Python ecosystem that Data Scientists use to get results in a matter of hours - and with practice - in a matter of minutes. The best way to learn is through examples, and this course will guide you through all the steps needed to train and test your models by tackling several classifications and regression challenges. By the end of the course, you will be able to take the Python Machine Learning toolkit we cover and apply it to your own projects to deploy models in just a few lines of code.

The fourth course, Troubleshooting Python Machine Learning, covers practical and unique solutions to common Machine Learning problems that you face. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results. We have collated for you the top issues, such as retrieving the most important regression features and explaining your results after clustering, and their corresponding solutions. We present these case studies in a problem-solution format, making it very easy for you to incorporate this into your knowledge. Taking this course will help you to precisely debug your models and research pipelines, so you can focus on pitching new ideas and not fixing old bugs.

By the end of this course, you'll build efficient, faster, and progressive machine learning models to tackle real data. Master the most popular Machine Learning tools by building your own models to tackle real-world problems.

About the Authors

• Rudy Lai is the founder of QuantCopy, a sales acceleration startup using AI to write sales emails to prospects. By taking in leads from your pipelines, QuantCopy researches them online and generates sales emails from that data. It also has a suite of email automation tools to schedule, send, and track email performance - key analytics that all feedback into how our AI generates content.

• Valeriy Babushkin has done an M. Sc. and has 5+ years' experience in industrial data science and academia. He is a Kaggle competition master and a 2018 IEEE SP Cup finalist. He has been a Data Science Team Lead at Yandex (the largest search engine in Russia; it outperforms Google) and runs an online taxi service (he acquired Uber in Russia and 15 other countries) and the biggest e-commerce platform in Russia. He was also a Head of Data Science at Moneta. Monetha is creating a universal, transferable, immutable trust, and reputation system combined with a payment solution. Finally, he is decentralized and empowered by the Ethereum Blockchain.