Learners will implement and apply predictive, classification, clustering, and information retrieval machine learning algorithms to real data sets throughout each course in the specialization. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Description: Learn scalable data management, evaluate big data technologies, and design effective visualizations This Specialization covers intermediate topics in data science. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering.
Dr. Andrea Trevino presents a beginner introduction to the widely-used K-means clustering algorithm in this tutorial. K-means clustering is a type of unsupervised learning, which is used when the resulting categories or groups in the data are unknown. This algorithm finds the groups that exist organically in the data and the results allow the user to label new data quickly. This tutorial covers the iterative algorithm that determines the clusters and works through a delivery fleet data example in Python.
The objective of this course is to give you a wholistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets.
Deep Learning For Coders is a new online course that, for the first time, promises to teach coders how to create state of the art deep learning models. Jeremy says that this is First deep learning course to show end-to-end how to get state of the art results (including how to get a top place in a Kaggle competition) First code-centric full deep learning course (18 hours of lessons) First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet! First deep learning course to show end-to-end how to get state of the art results (including how to get a top place in a Kaggle competition) First code-centric full deep learning course (18 hours of lessons) First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet! First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet!
In this introductory course, the "Backyard Data Scientist" will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the "techno sphere around us", why it's important now, and how it will dramatically change our world today and for days to come. We'll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science: To make sense of the Machine part of Machine Learning, we'll explore the Machine Learning process: Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete. So I invite you to join me, the Backyard Data Scientist on an exquisite journey into unlocking the secrets of Machine Learning for Data Science.... for you know - everyday people... like you!
We've already learned some classic machine learning models like k-nearest neighbor and decision tree. In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of. In particular, we will study the Random Forest and AdaBoost algorithms in detail. Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.
The course covers supervised learning concepts, which require labeled training data. The supervised techniques include various types of linear regression, decision trees, k-nearest neighbors, Naive Bayes, support vector machines and ensemble methods. Unsupervised machine learning including clustering techniques and other advanced topics will be covered in separate follow-up courses. Students will complete a data mining project using the supervised algorithms learned in class.
A few weeks in, I wanted to learn how to actually code machine learning algorithms, so I started a study group with a few of my peers. The most important takeaway from this period was the leap from non-vectorized to vectorized implementations of neural networks, which involved repeating linear algebra from university. So I asked my manager if I could spend some time learning stuff during my work hours as well, which he happily approved. Having gotten a basic understanding of neural networks at this point, I wanted to move on to deep learning.