Data Mining and Decision Making: Predictive Analytics for Business Strategies

Data Science    |    Intermediate
  • 12 videos | 51m 58s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 23 users Rating 4.5 of 23 users (23)
Predictive analytics enable modern businesses to obtain important insights about the market and gaining a competitive advantage. Use this course to explore how predictive analytics could provide insights about the future, empowering businesses to make informed decisions. Start by delving deeper into the concepts of deep learning, machine learning, and data. You'll then examine neural networks theory of operation and discover natural language processing (NLP) and computer vision applications. You'll wrap up the course by developing a deeper understanding of the future of analytics. Once you've completed this course, you'll be able to identify how regression, classification, time-series analysis, and recommender engines can be used to drive business decisions. You'll also have a solid grasp of using machine learning and deep learning methodologies for predictive analytics.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Specify the role of deep learning and artificial neural networks when dealing with data
    Describe how a neural network works
    Recognize unique features of regression problems and how these can be applied to predictive analytics
    Describe unique features of classification problems and how these can be applied to predictive analytics
    Describe how time series analysis is used for predictive analytics
  • Specify the role of actionable recommender systems in predictive analytics
    Outline the key advantages of using recurrent and convolutional neural network pipelines
    Identify key advantages of using nlp techniques in predictive analytics pipelines
    Recognize the key advantages of using computer vision in predictive analytics pipelines
    Describe the future prospects of predictive analytics alongside its most promising fields of study
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 1s
  • 4m 24s
  • Locked
    3.  Artificial Neural Networks
    6m 22s
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    4.  Business Problems: Regression
    4m 16s
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    5.  Business Problems: Classification
    5m 30s
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    6.  Business Problems: Time Series
    4m 36s
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    7.  Business Problems: Actionable Recommender Systems
    5m 40s
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    8.  Recurrent and Convolutional Neural Networks
    3m 55s
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    9.  Natural Language Processing (NLP)
    4m 50s
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    10.  Computer Vision
    5m
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    11.  World of Analytics: Integrated Futuristic Vision
    4m 9s
  • Locked
    12.  Course Summary
    1m 15s

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