Final Exam: ML Architect
Machine Learning | Intermediate
- 1 video | 32s
- Includes Assessment
- Earns a Badge
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 classificationsimplement generative adversarial network Discriminator and Generator using Python and Keras and build Discriminator for training modelunderstand the efforts being undertaken to reduce overfitting using the dropout techniqueprepare your data in Keras by defining your input and target tensorsunderstand the proposed learning framework for deep residual learning that improves training of networks that are significantly deeper than traditional neural networksdescribe the concept of deep reinforcement learning and its application in the areas of robotics, finance, and healthcarerecognize features of commonly used Keras layers and when to use themevaluate and score the performance of your neural network in Kerascompile the model in Kerasdescribe what neural networks are and their main componentsuse deep convolutional autoencoder with Keras and Pythonidentify features of Deep Learning that can improve performancerecognize key features of Decision Trees and Random Forestsrecall the concept of deep learningrecognize key features of Linear and Logistic regressionsapply a linear regression with Pythonrecall the best practices that should be adopted to build robust machine learning systemsidentify the challenges and patterns associated with deploying deep learning solutions in the enterpriserecall the concept of deep learning and the approach of using deep learning-based frameworks to model NLP tasks and audio data analysisuse Python to perform exploratory data analysisdescribe computational graphsrecognize reinforcement learning terms that are used in building reinforcement learning workflowsdescribe the multi-armed bandit problem and different approaches of solving this probleminstall the Markov Decision Policy toolbox and implement the Discounted Markov Decision Process using the policy iteration algorithmapply hierarchical clustering with Pythonwork with reinforcement learning agents using Keras and OpenAI Gymtroubleshoot deep learning errors by tuning the modelrecall the data workflows that are used to develop machine learning modelsrecall reinforcement learning algorithms and their featuresunderstand 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 fordescribe the various architectures of recurrent neural network that can be used in modelling natural language processinglist the best practices that should be adopted to build robust machine learning systems, with focus on the evaluation approachdescribe checklists for machine learning projects that are to be prepared and adopted by project managersdescribe the prominent statistical classification models and compare generative classifiers with discriminative classifiersworking with Machine Learning algorithms to build Deep Learning networksrecognize the data workflows that are used to develop machine learning modelsrecognize how ELM tends to produce better scalability, generalization performance, and faster learning than traditional support vector machinecompare deep learning platforms and frameworksunderstand how initializing a network with transferred features may boost generalization performanceuse case studies to analyze the impacts of adopting best practices for deep learninglist the various phases of machine learning workflow that can be used to achieve key milestones of machine learning projectsworking with Deep Learning frameworksrecall features of commonly used Keras layers and when to use themrecall the approach of using deep learning-based frameworks to model NLP tasks and audio data analysisrecognize the role of reward and discount factors in reinforcement learninguse Python and related data analysis libraries to perform exploratory data analysisworking with deep learning autoencodersdescribe approaches for architecting and building machine learning pipelines to implement scalable machine learning systemsidentify and work with both types of models available in Kerasidentify the rules that should be applied when using feature engineering to pull the right features into applicationsunderstand leading edge multi-label learning algorithmsunderstand the dataset that advances state-of-the-art object recognition by considering the context within the question of scene understandingdescribe approaches of implementing reinforcement learningdescribe TensorFlow extended and TFX pipeline componentsdescribe dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of Bellman equationdescribe the role of recurrent neural networkmake regression classifications using Kerasdescribe the Machine Learning workflow steps
IN THIS COURSE
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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