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
apply a linear regression with Pythonapply hierarchical clustering with Pythoncompare deep learning platforms and frameworkscompile the model in Kerasdescribe approaches for architecting and building machine learning pipelines to implement scalable machine learning systemsdescribe approaches of implementing reinforcement learningdescribe checklists for machine learning projects that are to be prepared and adopted by project managersdescribe computational graphsdescribe dynamic programming, policy evaluation, policy iteration, value iteration, and characteristics of Bellman equationdescribe TensorFlow extended and TFX pipeline componentsdescribe the concept of deep reinforcement learning and its application in the areas of robotics, finance, and healthcaredescribe the Machine Learning workflow stepsdescribe the multi-armed bandit problem and different approaches of solving this problemdescribe the prominent statistical classification models and compare generative classifiers with discriminative classifiersdescribe the role of recurrent neural networkdescribe the various architectures of recurrent neural network that can be used in modelling natural language processingdescribe what neural networks are and their main componentsevaluate and score the performance of your neural network in Kerasidentify and work with both types of models available in Kerasidentify features of Deep Learning that can improve performanceidentify the challenges and patterns associated with deploying deep learning solutions in the enterpriseidentify the rules that should be applied when using feature engineering to pull the right features into applicationsimplement generative adversarial network Discriminator and Generator using Python and Keras and build Discriminator for training modelinstall the Markov Decision Policy toolbox and implement the Discounted Markov Decision Process using the policy iteration algorithmlist the best practices that should be adopted to build robust machine learning systems, with focus on the evaluation approachlist the various phases of machine learning workflow that can be used to achieve key milestones of machine learning projectsmake regression classifications using Kerasprepare your data in Keras by defining your input and target tensorsrecall features of commonly used Keras layers and when to use themrecall reinforcement learning algorithms and their features
recall the approach of using deep learning-based frameworks to model NLP tasks and audio data analysisrecall the best practices that should be adopted to build robust machine learning systemsrecall the concept of deep learningrecall the concept of deep learning and the approach of using deep learning-based frameworks to model NLP tasks and audio data analysisrecall the data workflows that are used to develop machine learning modelsrecognize features of commonly used Keras layers and when to use themrecognize how ELM tends to produce better scalability, generalization performance, and faster learning than traditional support vector machinerecognize key features of Decision Trees and Random Forestsrecognize key features of Linear and Logistic regressionsrecognize reinforcement learning terms that are used in building reinforcement learning workflowsrecognize the data workflows that are used to develop machine learning modelsrecognize the role of reward and discount factors in reinforcement learningtroubleshoot deep learning errors by tuning the modelunderstand how a proposed new scene-centric database is successfully used for learning deep features forunderstand how convolutional neural networks may be utilized as a powerful class of models for image recognitionunderstand how initializing a network with transferred features may boost generalization performanceunderstand 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 understandingunderstand the efforts being undertaken to reduce overfitting using the dropout techniqueunderstand the proposed learning framework for deep residual learning that improves training of networks that are significantly deeper than traditional neural networksuse case studies to analyze the impacts of adopting best practices for deep learninguse deep convolutional autoencoder with Keras and Pythonuse Keras to make regression classificationsuse Python and related data analysis libraries to perform exploratory data analysisuse Python to perform exploratory data analysisworking with deep learning autoencodersworking with Deep Learning frameworksworking with Machine Learning algorithms to build Deep Learning networkswork with reinforcement learning agents using Keras and OpenAI Gym
IN THIS COURSE
1.ML Architect33sUP NEXT
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platformDigital badges are yours to keep, forever.