“…Els principals resultats d'aquest estudi han estat que: un programa d'AF que inicia en els CAPs i es vincula amb recursos esportius de la comunitat és eficaç en: (1) la creació de l'hàbit de realitzar AF i mantenir-lo a llarg termini (mes 0= 749.45 ± 774.6, mes 15= 1312.96 ± 1782.2 METs minut/ setmana), (2) la millora de la percepció de la salut avaluada a partir de la qualitat de vida autopercebuda (mes 0: component físic, CF = 41.8 ± 7.6; component mental, CM= 34.6 ± 7.4; month 15: CF= 45.4 ± 6.4, CM= 38.9 ± 6.4), (3) un major suport social (mes 0= 20.37 ± 18.7, mes 9= 43.00 ± 26.6), i (4) la disminució del nombre total de visites al CAP en els pacients insuficientment actius (18.2 ± 7.4 - 14.8 ± 8.5). …”
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“…Domain 1: Data Engineering 1.0 course intro 1.1 technology prerequisite 1.2 sagemaker studio lab 1.3 learn aws cloudshell 1.4 cloud developer workspace advantage 1.5 prototyping ai apis aws cloudshell bash 1.6 cloud9 with codewhisperer 1.7 domain one intro 1.8 data storage 1.9 determine storage medium 1.10 using s3 demo 1.11 job styles batch vs streaming 1.12 data ingestion pipelines 1.13 aws batch demo 1.14 step function demo Domain 2: Exploratory Data Analysis 2.0 domain intro Sanitize and prepare data for modeling 2.1 cleanup data 2.2 scaling data 2.3 labeling data 2.4 mechanical turk labeling Perform feature engineering 2.5 identify extract features 2.6 feature engineering concepts Analyze and visualize data for machine learning 2.7 graphing data 2.9 clustering Conclusion 2.10 conclusion Domain 3: Modeling 3.0 domain intro Frame business problems as machine learning problems 3.1 when to use ml 3.2 supervised vs unsupervized 3.3 selection right ml solution Select the appropriate model(s) for a given machine learning problem 3.4 select models 3.5 sagemaker canvas demo Train machine learning models 3.6 train test split 3.7 optimization 3.8 compute choice Perform hyperparameter optimization 3.14 neural network architecture Evaluate machine learning models 3.18 overfitting vs underfitting 3.19 selecting metrics 3.22 compare models experiment tracking Conclusion 3.23 Conclusion Domain 4: Machine Learning Implementation and Operations 4.0 course intro Build machine learning solutions for performance, availability, scalability, resiliency, and fault 4.1 logging monitoring 4.2 multiple regions 4.3 reproducible workflow 4.4 aws flavored devops Recommend and implement the appropriate machine learning services and features for a given 4.5 provisioning ec2 4.5 compute choices 4.6 provisioning ebs 4.7 aws ai ml services Apply basic AWS security practices to machine learning solutions. 4.9 plp aws lambda 4.10 integrated security Deploy and operationalize machine learning solutions 4.13 sagemaker workflow 4.14 doing predictions with sagemaker canvas 4.16 retrain models Conclusion 5.0 course conclusion Topics Covered Include: Domain 1: Data Engineering Domain 2: Exploratory Data Analysis Domain 3: Modeling Domain 4: Machine Learning Implementation and Operations Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ 900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! …”

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