scorecardresearch
Naukri Learning > Data Science & Analytics > Machine Learning >

Introductionto Deep Learning

Introductionto Deep Learning

Upskilling is a better roadmap to success. Enroll in this course to learn critical principles of Machine Learning through real-life case studies & examples

107 Views

Course Highlights

  • Start instantly and learn at your own schedule.

  • Earn a certificate of learning on course completion 

  • This course is offered by National Research University Higher School of Economics

Duration: 42 Days

Mode of learning: Online self study

Course Overview

Who should do this course ?
  • This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.

What are the course deliverables ?
  • Discuss common regression, classification, and multilabel classification metrics

  • Explain the use of linear and logistic regression in supervised learning applications

  • Describe common strategies for grid searching and cross-validation

  • Employ evaluation metrics to select models for production use

  • Explain the use of tree-based algorithms in supervised learning applications

  • Explain the use of Neural Networks in supervised learning applications

  • Discuss the major variants of neural networks and recent advances

  • Create a neural net model in Tensorflow

  • Create and test an instance of Watson Visual Recognition

  • Create and test an instance of Watson NLU

More about this course
  • The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers

  • Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image.

Eligibility
  • It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

I am Interested

Curriculum

    Enquire Now

    Reviews

    Average Rating

    4
    Write a Review
    CompareRemove All
    Browse Category
    0

    Buy Safely with Naukri.com
    We support secure payment methods

    Disclaimer

    While Naukri Learning services have helped many customers over the years, we do not guarantee any interview calls or assure any job offers with any of our services.
    The services associated with Naukri Learning are only provided through the website Naukri.com. You are advised to be cautious of calls/emails asking for payment from other web sites that claim to offer similar services under the name of Naukri.com. We have no associates/agents other than the partner sites that have been specifically named on the homepage of the website Naukri.com. We also recommend that you Security Guidelines and Terms and Conditions