This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis.
Этот курс входит в специализацию ''Специализация Прикладная наука о данных с Python'
Applied Machine Learning in Python
от партнера
Об этом курсе
Карьерные результаты учащихся
34%
35%
12%
Приобретаемые навыки
Карьерные результаты учащихся
34%
35%
12%
100% онлайн
Курс 3 из 5 в программе
Гибкие сроки
Промежуточный уровень
Прибл. 24 часа на выполнение
Английский
от партнера
Мичиганский университет
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Программа курса: что вы изучите
Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn
This module introduces basic machine learning concepts, tasks, and workflow using an example classification problem based on the K-nearest neighbors method, and implemented using the scikit-learn library.
Module 2: Supervised Machine Learning - Part 1
This module delves into a wider variety of supervised learning methods for both classification and regression, learning about the connection between model complexity and generalization performance, the importance of proper feature scaling, and how to control model complexity by applying techniques like regularization to avoid overfitting. In addition to k-nearest neighbors, this week covers linear regression (least-squares, ridge, lasso, and polynomial regression), logistic regression, support vector machines, the use of cross-validation for model evaluation, and decision trees.
Module 3: Evaluation
This module covers evaluation and model selection methods that you can use to help understand and optimize the performance of your machine learning models.
Module 4: Supervised Machine Learning - Part 2
This module covers more advanced supervised learning methods that include ensembles of trees (random forests, gradient boosted trees), and neural networks (with an optional summary on deep learning). You will also learn about the critical problem of data leakage in machine learning and how to detect and avoid it.
Рецензии
Лучшие отзывы о курсе APPLIED MACHINE LEARNING IN PYTHON
Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!
This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses
This is an excellent course. The programming exercises can be solved only when you get the basics right. Else, you will need to revisit the course material. Also, the forums are pretty interactive.
Extremely useful course! You really get a lot of value from it and exactly what you would expect from such course! Very entertaining and a lot of additional educational materials! Thank You a lot!
the content of videos , quiz and exercise all work extremely well together towards the stated goal of the course i.e. to give the learner a good over view of how to apply ML theories into action
Not for the faint of heart and some experience with Python, in particular Pandas, is preferred. Great overview of the different methods used in machine learning. One of the better courses imo.
It's a nice course. It'll familiarize you with different models, evaluation metrics and basics of machine learning and let you practice with some of the real world datasets during assignment.
Concise and clear presentation of the material with the majority of time focused around using TDD to learn and practice concepts through developing solutions to open ended coding challenges.
Специализация Прикладная наука о данных с Python: общие сведения
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Когда я получу доступ к лекциям и заданиям?
Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
Что я получу, оформив подписку на специализацию?
Записавшись на курс, вы получите доступ ко всем курсам в специализации, а также возможность получить сертификат о его прохождении. После успешного прохождения курса на странице ваших достижений появится электронный сертификат. Оттуда его можно распечатать или прикрепить к профилю LinkedIn. Просто ознакомиться с содержанием курса можно бесплатно.
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