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Renat Kornilov
Renat Kornilov

Introduction To Machine Learning With Python: A Guide For Data Scientists BEST

I'm a Principal Research SDE at Microsoft (previously Columbia, NYU, Amazon), and author of the O'Reilly book "Introduction to machine learning with Python", describing a practical approach to machine learning with python and scikit-learn. I am one of the core developers of the scikit-learn machine learning library, and I have been co-maintaining it for several years. I'm also a Software Carpentry instructor. You can find my full cv here.

Introduction to Machine Learning with Python: A Guide for Data Scientists

You can find my previous institute website and information about the courses I was teaching at at Introduction to Machine Learning with Python Introduction to Machine Learning with Python provides a practial view of engineering machine learning systems in Python. The premise of the book is to enable people to learn the basics of machine learning without requiring a lot of mathematics. We therefore keep the amount of formulas to a minimum, and instead rely on code and illustrations to bring across the driving principles behind applying machine learning. We heavily focus on the use of the scikit-learn machine learning library, and give a detailed tour of its main modules and how to piece them together to a successful machine learning pipeline.

In this Advanced Machine Learning with scikit-learn training course, expert author Andreas Mueller will teach you how to choose and evaluate machine learning models. This course is designed for users that already have experience with Python.

You will start by learning about model complexity, overfitting and underfitting. From there, Andreas will teach you about pipelines, advanced metrics and imbalanced classes, and model selection for unsupervised learning. This video tutorial also covers dealing with categorical variables, dictionaries, and incomplete data, and how to handle text data. Finally, you will learn about out of core learning, including the sci-learn interface for out of core learning and kernel approximations for large-scale non-linear classification.

Once you have completed this computer based training course, you will have learned everything you need to know to be able to choose and evaluate machine learning models. Working files are included, allowing you to follow along with the author throughout the lessons.

In this talk I'm discussing the why and how of automatic machine learning. I start with an explanation of the goals of automatic machine learning, and introduce meta-learning. The talk goes on to discuss recent research, available implementation and what I think we should be working on in this area.

This talk introduction covers data representation, basic API for supervised and unsupervised learning, cross-validation, grid-search, pipelines, text processing and details about some of the most popular machine learning models. The talk concludes with remarks on scaling up computation to large datasets, and how to perform out-of-core learning with scikit-learn.

In the days of the "big data" buzz, many people build data driven applications on clusters from the start. However, working with distributed computing is not only pricey, but also requires a large engineering effort and removes interactivity from the data exploration process. In this talk I will demonstrate how to learn powerful nonlinear models on a single machine, even with large data sets. This can be achieved using the partial_fit interface provided by scikit-learn, that implements stochastic updates. Together with stateless transformation of the data, such as hashing, kernel approximation and random projections, these allow incrementally building a model without the need to store all the data in memory, or even on disk.

This tutorial covers basic concepts of machine learning, such as supervised and unsupervised learning, cross validation and model selection. I talk about how to prepare data for machine learning, and go from applying a single algorithm to building a machine learning pipeline. I also go in-depth on a couple of algorithms and describe what overfitting and underfitting looks like for these.

Data scientists, artificial intelligence engineers, machine learning engineers, and data analysts are some of the in-demand organizational roles that are embracing AI. If you aspire to apply for these types of jobs, it is crucial to know the kind of machine learning interview questions that recruiters and hiring managers may ask.

Further additions to the workshop content, including topics on statistical inference, machine learning, and HPC with Amarel, were added by Sanket Badhe and Ziqiu (Sly) Zhong, Quantitative Data Graduate Specialists from Fall 2019 to Fall 2020.

Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.

Get deeper insights from your data while lowering costs with AWS machine learning (ML). AWS helps you at every stage of your ML adoption journey with the most comprehensive set of artificial intelligence (AI) and ML services, infrastructure, and implementation resources.

Machine learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems. ML provides potential solutions in all these domains and more, and likely will become a pillar of our future civilization.

The supply of expert ML designers has yet to catch up to this demand. A major reason for this is that ML is just plain tricky. This machine learning tutorial introduces the basic theory, laying out the common themes and concepts, and making it easy to follow the logic and get comfortable with machine learning basics.

Unsupervised machine learning is typically tasked with finding relationships within data. There are no training examples used in this process. Instead, the system is given a set of data and tasked with finding patterns and correlations therein. A good example is identifying close-knit groups of friends in social network data.

Keep in mind that to really apply the theories contained in this introduction to real-life machine learning examples, a much deeper understanding of these topics is necessary. There are many subtleties and pitfalls in ML and many ways to be lead astray by what appears to be a perfectly well-tuned thinking machine. Almost every part of the basic theory can be played with and altered endlessly, and the results are often fascinating. Many grow into whole new fields of study that are better suited to particular problems.

Clearly, machine learning is an incredibly powerful tool. In the coming years, it promises to help solve some of our most pressing problems, as well as open up whole new worlds of opportunity for data science firms. The demand for machine learning engineers is only going to grow, offering incredible chances to be a part of something big. I hope you will consider getting in on the action!

In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as "machine learning"), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book.

Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, and to uncover key insights in data mining projects. These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase. They will be required to help identify the most relevant business questions and the data to answer them.


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