![]() ![]() This week you will build your first intelligent application that makes predictions from data. apply() for data transformation5 min Week 2 Regression: Predicting House Prices Why you should learn machine learning with us Important Update regarding the Machine Learning Specialization10 min Slides presented in this module10 minįor those interested, the slides presented in the videos for this module can be downloaded here: intro.pdf Welcome to this course and specialization41 sec Who we are5 min Machine learning is changing the world3 min Why a case study approach?7 min Specialization overview6 min Who this specialization is for and what you will be able to do How we got into M元 min Who is this specialization for?4 min What you’ll be able to do57 sec The capstone and an example intelligent application6 min The future of intelligent applications2 min Getting started with the tools for the course Reading: Getting started with Python, IPython Notebook & GraphLab Create10 min Reading: where should my files go?10 min Getting started with Python and the IPython Notebook Download the IPython Notebook used in this lesson to follow along10 min Starting an IPython Notebook5 min Creating variables in Python7 min Conditional statements and loops in Python8 min Creating functions and lambdas in Python3 min Getting started with SFrames for data engineering and analysis Download the IPython Notebook used in this lesson to follow along10 min Starting GraphLab Create & loading an SFrame4 min Canvas for data visualization4 min Interacting with columns of an SFrame4 min Using. We also discuss who we are, how we got here, and our view of the future of intelligent applications. This introduction to the specialization provides you with insights into the power of machine learning, and the multitude of intelligent applications you personally will be able to develop and deploy upon completion. Machine learning is everywhere, but is often operating behind the scenes. Analyzing the sentiment of product reviews.These assignments-one per Module 2 through 6-will walk you through Python implementations of intelligent applications for: Review the material we’ll cover each week, and preview the assignments you’ll need to complete to pass the course. To begin, we recommend taking a few minutes to explore the course site. If you continue with the subsequent courses in the Machine Learning specialization, you will delve deeper into the methods and algorithms, giving you the power to develop and deploy new machine learning services. These applications will allow you to perform predictions, personalized recommendations and retrieval, and much more. You will learn a broad range of machine learning methods for deriving intelligence from data, and by the end of the course you will be able to implement actual intelligent applications. Welcome to Machine Learning Foundations: A Case Study Approach! By joining this course, you’ve taken a first step in becoming a machine learning expert. Build an end-to-end application that uses machine learning at its core. Utilize a dataset to fit a model to analyze new data. Assess the model quality in terms of relevant error metrics for each task. Represent your data as features to serve as input to machine learning models. Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. Select the appropriate machine learning task for a potential application. Describe the core differences in analyses enabled by regression, classification, and clustering. Identify potential applications of machine learning in practice. Learning Outcomes: By the end of this course, you will be able to: Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. This first course treats the machine learning method as a black box. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. how to predict house prices based on house-level features,.At the end of the first course you will have studied In this course, you will get hands-on experience with machine learning from a series of practical case-studies. About this course: Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? ![]()
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