Inhalt: An incredible amount of unstructured text data is generated every day by social media, web pages, and a variety of other sources. But without the ability to tame and harness that data, you'll be unable to glean any value from it. In this course, learn how to translate messy text data into powerful insights using Python. Instructor Derek Jedamski begins with a quick review of foundational NLP concepts, including how to clean text data and build a model on top of vectorized text. He then jumps into more complex topics such as word2vec, doc2vec, and recurrent neural networks. To wrap up the course, he lends these concepts a real-world context by applying them to a machine learning problem. Umfang: 02:14:37
Inhalt: In the first installment of the Applied Machine Learning series, instructor Derek Jedamski covered foundational concepts, providing you with a general recipe to follow to attack any machine learning problem in a pragmatic, thorough manner. In this course-the second and final installment in the series-Derek builds on top of that architecture by exploring a variety of algorithms, from logistic regression to gradient boosting, and showing how to set a structure that guides you through picking the best one for the problem at hand. Each algorithm has its pros and cons, making each one the preferred choice for certain types of problems. Understanding what actually drives each algorithm, as well as their benefits and drawbacks, can give you a significant competitive advantage as a data scientist. Umfang: 02:24:02.00
Inhalt: The quality of the predictions coming out of your machine learning model is a direct reflection of the data you feed it during training. Feature engineering helps you extract every last bit of value out of data. This course provides the tools to take a data set, tease out the signal, and throw out the noise in order to optimize your models. The concepts generalize to nearly any kind of machine learning algorithm. Instructor Derek Jedamski provides a refresher on machine learning basics and a thorough introduction to feature engineering. He explores continuous and categorical features and shows how to clean, normalize, and alter them. Learn how to address missing values, remove outliers, transform data, create indicators, and convert features. In the final chapters, Derek explains how to prepare features for modeling and provides four variations for comparison, so you can evaluate the impact of cleaning, transforming, and creating features through the lens of model performance. Umfang: 02:26:24
Inhalt: Anyone who can write basic Python is capable of fitting a simple machine learning model on a clean dataset. The competitive edge comes in the ability to customize and optimize those models for specific problems. The workflow used to build effective machine learning models and the methods used to optimize those models are typically not algorithm or problem specific. In this course, the first installment in the two-part Applied Machine Learning series, instructor Derek Jedamski digs into the foundations of machine learning, from exploratory data analysis to evaluating a model to ensure it generalizes to unseen examples. Instead of zeroing in on any specific machine learning algorithm, Derek focuses on giving you the tools to efficiently solve nearly any kind of machine learning problem. Umfang: 02:38:54.00
Inhalt: With the increased amount of data publicly available and the increased focus on unstructured text data, understanding how to clean, process, and analyze that text data is tremendously valuable. If you have some experience with Python and an interest in natural language processing (NLP), this course can provide you with the knowledge you need to tackle complex problems using machine learning. Instructor Derek Jedamski provides a quick summary of basic natural language processing (NLP) concepts, covers advanced data cleaning and vectorization techniques, and then takes a deep dive into building machine learning classifiers. During this last step, Derek shows how to build two different types of machine learning models, as well as how to evaluate and test variations of those models. Umfang: 04:14:33.00
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