The course is broad and pretty decent introductory course, but there is a number of presentation and course design flaws. First, while I'm not sure whether it is solely a Coursera's typical marketing approach to prevent users from refusing the course just because of the minimum amount of time required, or authors' unintended misestimations, but the actual time needed to complete the course is a way more than listed at the course home page, especially assignments. Often the time needed only to run an assignment training with no coding exceeds the given estimate. To get the value from the course one should be prepared to allocate much more time (2x-3x in total). Second, the course is too broad to be called an introductory one but too shallow in terms of math/practical/reasoning details to be named a deep one.
About the course - Cluster analysis is one of the most popular techniques used in data mining for marketing needs. The idea behind cluster analysis is to find natural groups within data in such a way that each element in the group is as similar to each other as possible. At the same time, the groups are as dissimilar to other groups as possible. Course materials- The course contains video presentations (power point presentations with voice), pdf, excel work book and sas codes. Course duration- The course should take roughly 10 hours to understand and internalize the concepts.
This tutorial is an introduction to Artificial Intelligence which explains the need to study AI, AI growth, concept of AI, its use cases and various intelligence types in detail. If you've enjoyed this video, Like us and Subscribe to our channel for more similar informative videos and free tutorials. Got any questions about AI? Ask us in the comment section below. Are you looking for something more? Enroll in our Artificial Intelligence & Deep Learning training course and become a certified AI Expert (https://goo.gl/RdA17B).
About this course: This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China).
Have you ever thought of the scenario where all the cars will be moving without a driver that means something like automated machines say for example automatic washing machine. But there is a difference. For automatic washing machine,we can write programs for the washing machine functionality. All the materials for this course are FREE. You can download and install R, with simple commands on Windows, Linux, or Mac.
About this course: Welcome to Course 2 - Sales Strategy - This course is designed to discuss the application of intelligence analysis in the sales planning process. And this approach contributes to integrating the sales planning process into the corporate strategy of the company because, in the strategy analysis and formulation process, we apply models, frameworks, tools, and techniques that also apply to the sales planning and management process. Therefore, the expected outcomes of this course focus on the transition from traditional to strategic sales planning, by discussing and applying the concepts recommended to support the development of the strategic guidelines. The concepts, models, tools, and techniques discussed and practiced during the course focus on the improvement of value creation from the sales function empowered by intelligence analysis, a process which typically applies in the strategy analysis front. The discussions go through how intelligence analysis can support the sales function, by providing methods to connect strategy to marketing and sales planning processes.
Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!
The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best? What is the best order in which to use selected resources? It would probably be helpful to have some basic understanding of one or both of the first 2 topics, but even that won't be necessary; some extra time spent on the earlier steps should help compensate.
About this course: This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.
What is machine learning / ai? How to lean machine learning in practice? Some people conceive it the "steam engine" of our century and one thing is certain: It will drastically change the world. Neural Networks (often referred to as deep learning) are particular interesting. But there are several questions to answer.