How to Choose the Best Machine Learning Services for Your Company https://blog.silverskills.com/how-to-choose-the-best-machine-learning-services/

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In today's digital age, cloud computing has become the backbone of many businesses, and Amazon Web Services (AWS) stands tall as a leader in this domain. As more companies shift towards cloud-based solutions, the demand for skilled AWS professionals continues to soar. If you're looking to carve a successful career path in the AWS field, you'll need to equip yourself with the right knowledge, skills, and mindset. In this blog post, we'll explore how you can become an expert in AWS. 1. Understand the Fundamentals Before diving deep into AWS services and solutions, it's crucial to build a strong foundation by understanding the fundamentals of cloud computing. Familiarize yourself with basic concepts such as virtualization, networking, storage, and security. AWS offers extensive documentation, tutorials, and training resources through its website and AWS Training and Certification program. Take advantage of these resources to grasp the core principles of cloud computing. You can get AWS classes in Pune. 2. Gain Hands-on Experience Theory alone won't make you an expert. Hands-on experience is key to mastering AWS. Start by creating a free AWS account and experiment with various services in a sandbox environment. Launch virtual servers with Amazon EC2, store data in Amazon S3, set up databases with Amazon RDS, and explore other services like AWS Lambda, Amazon DynamoDB, and Amazon SQS. The more you practice, the more confident you'll become in navigating the AWS console and implementing solutions. 3. Pursue Certification AWS certifications are widely recognized in the industry and can validate your expertise in different areas of AWS. Begin with the AWS Certified Solutions Architect – Associate certification, which covers designing and deploying scalable systems on AWS. As you progress, consider pursuing higher-level certifications such as AWS Certified Solutions Architect – Professional, AWS Certified DevOps Engineer, or specialty certifications like AWS Certified Security – Specialty or AWS Certified Machine Learning – Specialty. Certification not only enhances your knowledge but also boosts your credibility as an AWS professional. 4. Stay Updated The cloud computing landscape is constantly evolving, with AWS regularly introducing new services, features, and updates. Stay abreast of the latest developments by following AWS blogs, attending webinars, participating in forums, and joining AWS user groups or communities. Engage with fellow professionals, share knowledge, and learn from their experiences. Additionally, subscribe to AWS newsletters and follow AWS on social media platforms to receive timely updates and announcements. 5. Dive Deep into Specializations While having a broad understanding of AWS services is essential, consider specializing in specific areas based on your interests and career goals. Whether it's cloud architecture, security, networking, database management, or machine learning, delve deeper into your chosen specialization. Participate in relevant projects, seek mentorship from seasoned professionals, and continuously expand your expertise through continuous learning and practical application. 6. Build Real-world Projects Put your skills to the test by working on real-world projects or contributing to open-source projects hosted on AWS. Collaborate with peers, tackle challenges, and leverage AWS services to build scalable, cost-effective solutions that address business needs. Document your projects, highlight your achievements, and showcase your expertise through online portfolios, GitHub repositories, or personal blogs. Real-world experience demonstrates your ability to apply AWS knowledge in practical scenarios, making you a valuable asset to potential employers or clients. Enroll in one of the best AWS course in Pune. 7. Never Stop Learning The AWS ecosystem is vast and dynamic, and there's always something new to learn. Cultivate a growth mindset and embrace continuous learning as a lifelong journey. Explore advanced topics, experiment with emerging technologies, and seek opportunities for professional development. Whether it's attending conferences, enrolling in advanced courses, or pursuing advanced certifications, invest in your ongoing education to stay ahead in the rapidly evolving field of AWS. Becoming an expert in the AWS field requires dedication, persistence, and a genuine passion for cloud technology. By mastering the fundamentals, gaining hands-on experience, pursuing certifications, staying updated, specializing in specific areas, building real-world projects, and fostering a commitment to lifelong learning, you can establish yourself as a sought-after AWS professional capable of tackling complex challenges and driving innovation in the cloud computing industry. Start your journey today and unleash your potential in the world of AWS. Visit- https://www.sevenmentor.com/amazon-web-services-training-institute-in-pune.php

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INSOMNIA- AI Identifies Depression as Highest Risk Factor for Insomnia. CDC study uses AI machine learning and health records to predict insomnia risk. Reviewed by Ekua Hagan KEY POINTS- AI machine learning can identify individual risk factors for insomnia based on health record data. A machine learning model using fully anonymized data from over 7,900 patient records found depression to be the strongest predictor of insomnia. Scientists say the greatest strength of this algorithmic method of research is the ability of AI to review a massive amount of data without bias. The use of artificial intelligence (AI) machine learning for healthcare screening, medical diagnostics, and life sciences is an exciting growth area. A new peer-reviewed study published in the journal PLOS ONE demonstrates how AI machine learning can identify individual risk factors for insomnia based on health record data and found depression to be the strongest predictor. “Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors,” wrote Alexander A. Huang and Samuel Y. Huang, who co-authored the research by the National Health and Nutrition Examination Survey (NHANES). “Depression, age, weight, and waist circumference were the strongest predictors of sleep disorder." The retrospective study used fully anonymized data from over 7,900 patient records from adult Americans, aged 18 years or older, from the National Health and Nutrition Examination Survey (NHANES), a major program of the National Center for Health Statistics (NCHS) which is part of the U.S. Centers for Disease Control and Prevention (CDC). “The greatest strength of this algorithmic method for identification of the covariates is the ability to search through hundreds of covariates systematically without relying upon judgment from the researcher, which may be muddled by potential personal biases,” the scientists wrote. Each NHANES database patient record had over 680 variables with information from physical exams, lab data, and self-reported questionnaires, including the Patient Health Questionnaire (PHQ-9) for depression, as well as diet, exercise, and demographic information. The nine-question Patient Health Questionnaire (PHQ-9) incorporates DSM-IV diagnostic criteria (American Psychiatric Association, 1994) for major depressive disorder (MDD) symptoms into a self-reporting survey that is used for diagnosing, screening, and monitoring the severity of depression in adults. The PHQ-9 is from the longer Patient Health Questionnaire (PHQ) assessment. Both the PHQ and PHQ-9 were created in 1999 via an educational grant from Pfizer Inc. by Robert L. Spitzer, MD, Janet Williams, DSW, and Kurt Kroenke, MD, and their colleagues at Columbia University. “We observed that increased PHQ-9 scores were strongly linked to the odds of a sleep disorder,” the researchers observed. The researchers conducted the study using four types of AI machine learning algorithms: Artificial Neural Network (ANN), Random Forest (RF), Adaptive Boost (ADABoost), and XGBoost. Out of these four, XGBoost performed the best with the highest mean AUROC (Area under the receiver operator characteristic curve). “Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors,” the scientists concluded.