Machine learning with python course

IBM: Machine Learning with Python: A Practical Introduction

Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.

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Machine Learning with Python: A Practical Introduction

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About this course

Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You’ll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We’ll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along the way, you’ll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!

Most importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

We’ll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such asTrain/Test Split, Root Mean Squared Error and Random Forests.

Mostimportantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

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Машинное обучение с использованием Python

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IBM

Об этом курсе

Get ready to dive into the world of Machine Learning (ML) by using Python! This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.

This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency.

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Massachusetts Institute of Technology: Machine Learning with Python: from Linear Models to Deep Learning

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. — Part of the MITx MicroMasters program in Statistics and Data Science.

There is one session available:

I would like to receive email from MITx and learn about other offerings related to Machine Learning with Python: from Linear Models to Deep Learning.

Machine Learning with Python: from Linear Models to Deep Learning

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About this course

If you have specific questions about this course, please contact us atsds-mm@mit.edu.

Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.

As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.

In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:

• Representation, over-fitting, regularization, generalization, VC dimension;
• Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
• On-line algorithms, support vector machines, and neural networks/deep learning.

Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.

This course is part of theMITx MicroMasters Program in Statistics and Data Science. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master’s at other universities. To learn more about this program, please visit https://micromasters.mit.edu/ds/.

Please note : edX Inc. has recently entered into an agreement to transfer the edX platform to 2U, Inc., which will continue to run the platform thereafter. The sale will not affect your course enrollment, course fees or change your course experience for this offering. It is possible that the closing of the sale and the transfer of the edX platform may be effectuated sometime in the Fall while this course is running. Please be aware that there could be changes to the edX platform Privacy Policy or Terms of Service after the closing of the sale. However, 2U has committed to preserving robust privacy of individual data for all learners who use the platform. For more information see the edX Help Center.

At a glance

• 6.00.1x or proficiency in Python programming
• 6.431x or equivalent probability theory course
• College-level single and multi-variable calculus
• Vectors and matrices

• Language: English
• Video Transcript: English
• Associated skills: Prediction, Reinforcement Learning, Machine Learning, Data Science, Recommender Systems, Artificial Neural Networks, Experimentation, Sales, Python (Programming Language), Consumer Behaviour, Physics, Deep Learning, Linear Model, Support Vector Machine, Machine Learning Algorithms, Forecasting, Statistics, Algorithms

What you’ll learn

• Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning
• Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models
• Choose suitable models for different applications
• Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.

Syllabus

• Introduction
• Linear classifiers, separability, perceptron algorithm
• Maximum margin hyperplane, loss, regularization
• Stochastic gradient descent, over-fitting, generalization
• Linear regression
• Recommender problems, collaborative filtering
• Non-linear classification, kernels
• Learning features, Neural networks
• Deep learning, back propagation
• Recurrent neural networks
• Generalization, complexity, VC-dimension
• Unsupervised learning: clustering
• Generative models, mixtures
• Mixtures and the EM algorithm
• Learning to control: Reinforcement learning
• Reinforcement learning continued
• Applications: Natural Language Processing

• Automatic Review Analyzer
• Digit Recognition with Neural Networks
• Reinforcement Learning

About the instructors

Frequently Asked Questions

Should you have further inquiries, please go to https://micromasters.mit.edu/ds/ and use the «Contact us» button.

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

This course is part of Statistics and Data Science (General track) MicroMasters Program

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Прикладное машинное обучение на языке Python

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Субтитры: Арабский, Немецкий, Тайский, Португальский (бразильский), Английский, Индонезийский , Французский, Испанский

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В этом курсе 4 модуля

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.

This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

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.

Что включено

4 материала для самостоятельного изучения

Show info about module content

• Introduction • 11 мин. • Предварительный просмотр модуля
• What’s New? • 0 мин.
• Key Concepts in Machine Learning • 13 мин.
• Python Tools for Machine Learning • 4 мин.
• An Example Machine Learning Problem • 12 мин.
• Examining the Data • 9 мин.
• K-Nearest Neighbors Classification • 23 мин.

4 материала для самостоятельного изучения • Итого 60 минут

• Syllabus • 10 мин.
• Help us learn more about you! • 10 мин.
• Notice for Auditing Learners: Assignment Submission • 10 мин.
• Zachary Lipton: The Foundations of Algorithmic Bias (optional) • 30 мин.

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