Final Exam: ML Architect

Machine Learning    |    Intermediate
  • 1 video | 32s
  • Includes Assessment
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Final Exam: ML Architect will test your knowledge and application of the topics presented throughout the ML Architect track of the Skillsoft Aspire ML Programmer to ML Architect Journey.

WHAT YOU WILL LEARN

  • Use keras to make regression classifications
    implement generative adversarial network discriminator and generator using python and keras and build discriminator for training model
    understand the efforts being undertaken to reduce overfitting using the dropout technique
    prepare your data in keras by defining your input and target tensors
    understand the proposed learning framework for deep residual learning that improves training of networks that are significantly deeper than traditional neural networks
    describe the concept of deep reinforcement learning and its application in the areas of robotics, finance, and healthcare
    recognize features of commonly used keras layers and when to use them
    evaluate and score the performance of your neural network in keras
    compile the model in keras
    describe what neural networks are and their main components
    use deep convolutional autoencoder with keras and python
    identify features of deep learning that can improve performance
    recognize key features of decision trees and random forests
    recall the concept of deep learning
    recognize key features of linear and logistic regressions
    apply a linear regression with python
    recall the best practices that should be adopted to build robust machine learning systems
    identify the challenges and patterns associated with deploying deep learning solutions in the enterprise
    recall the concept of deep learning and the approach of using deep learning-based frameworks to model nlp tasks and audio data analysis
    use python to perform exploratory data analysis
    describe computational graphs
    recognize reinforcement learning terms that are used in building reinforcement learning workflows
    describe the multi-armed bandit problem and different approaches of solving this problem
    install the markov decision policy toolbox and implement the discounted markov decision process using the policy iteration algorithm
    apply hierarchical clustering with python
    work with reinforcement learning agents using keras and openai gym
    troubleshoot deep learning errors by tuning the model
    recall the data workflows that are used to develop machine learning models
    recall reinforcement learning algorithms and their features
    understand how convolutional neural networks may be utilized as a powerful class of models for image recognition
  • understand how a proposed new scene-centric database is successfully used for learning deep features for
    describe the various architectures of recurrent neural network that can be used in modelling natural language processing
    list the best practices that should be adopted to build robust machine learning systems, with focus on the evaluation approach
    describe checklists for machine learning projects that are to be prepared and adopted by project managers
    describe the prominent statistical classification models and compare generative classifiers with discriminative classifiers
    working with machine learning algorithms to build deep learning networks
    recognize the data workflows that are used to develop machine learning models
    recognize how elm tends to produce better scalability, generalization performance, and faster learning than traditional support vector machine
    compare deep learning platforms and frameworks
    understand how initializing a network with transferred features may boost generalization performance
    use case studies to analyze the impacts of adopting best practices for deep learning
    list the various phases of machine learning workflow that can be used to achieve key milestones of machine learning projects
    working with deep learning frameworks
    recall features of commonly used keras layers and when to use them
    recall the approach of using deep learning-based frameworks to model nlp tasks and audio data analysis
    recognize the role of reward and discount factors in reinforcement learning
    use python and related data analysis libraries to perform exploratory data analysis
    working with deep learning autoencoders
    describe approaches for architecting and building machine learning pipelines to implement scalable machine learning systems
    identify and work with both types of models available in keras
    identify the rules that should be applied when using feature engineering to pull the right features into applications
    understand leading edge multi-label learning algorithms
    understand the dataset that advances state-of-the-art object recognition by considering the context within the question of scene understanding
    describe approaches of implementing reinforcement learning
    describe tensorflow extended and tfx pipeline components
    describe dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of bellman equation
    describe the role of recurrent neural network
    make regression classifications using keras
    describe the machine learning workflow steps

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