Machine Learning Engineer with Microsoft Azure

Colleagues, students of the Machine Learning Engineer with Microsoft Azure will gain experience in understanding ML models, protecting people and their data, and controlling the end-to-end ML lifecycle at scale. Develop high-demand and highly marketable skills involving Azure Machine Learning, Azure data services, AI business context, Azure ML platform, Azure ML pipelines, Open neural network exchange, Hyperparameter tuning, Azure ML designer, Model interpretation, Azure ML sdk, Azure ML experiments, Model maintenance, Cloud asset management, Azure ml automated ML, Kubernetes security, Azure ML pipelines, API troubleshooting, REST APIs, Deployment testing, Azure kubernetes service, Swagger, Docker and ApacheBench. Learning modules include: 1) Welcome to Machine Learning Engineer with Microsoft Azure - learn more about the pre-requisites, structure of the program, and getting started, 2) Machine Learning Engineer Program Introduction, the structure of the program and meet your instructors, 3) Using Azure Machine Learning - learn how to configure machine learning pipelines in Azure, identify use cases for Automated Machine Learning, and use the Azure ML SDK to design, create, and manage machine learning pipelines in Azure, 4) Introduction to Azure ML - learn about Machine Learning in the cloud and how workspaces and AzureML studio enable you to be more productive as a data scientist or ML engineer, 5) Datastores and Datasets - learn how to integrate third party datasets and open datasets into our ML pipeline to quickly develop working solutions, 6) Training Models in Azure ML - learn how to manage pipelines and use hyperparameters in experiments, as well as how to automate changes that create huge value in terms of prediction accuracy, 7) The AzureML SDK - programmatically create and manage pipelines. We'll see that this approach makes pipeline creation and management a reproducible process, Automated ML and Hyperparameter Tuning, 8) Optimizing an ML Pipeline in Azure - create and optimize an ML pipeline. You'll do this using both HyperDrive and AutoML, so that you can compare the results, 9) Machine Learning Operations This course covers a lot of the key concepts of operationalizing Machine Learning, from selecting the appropriate targets for deploying models, to enabling Application Insights, identifying problems in logs, and harnessing the power of Azure’s Pipelines. All these concepts are part of core DevOps pillars that will allow you to demonstrate solid skills for shipping machine learning models into production, 10) Operationalizing Machine Learning - work with the Bank Marketing dataset. You will use Azure to configure a cloud-based machine learning production model, deploy it, and consume it, and Capstone - Azure Machine Learning Engineer - this capstone project gives you the opportunity to use the Azure Machine learning knowledge you have obtained from this Nanodegree to solve the problem of your interest.

Enroll today (teams & executives are welcome): https://fxo.co/IJ2h 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity (Audible) (Kindle)

3 - ChatGPT - The Era of Generative Conversational AI Has Begun (Audible) (Kindle) 

4 - The Race for Quantum Computing (Audible) (Kindle) 

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

 

“The Promise of Data-Driven Decision Making - From Analytics to Visualization and Beyond” (new audio & ebook)

Colleagues, “The Promise of Data-Driven Decision Making” is a powerful resource that has the potential to transform the way we approach problem-solving in both our personal and professional lives. By collecting and analyzing data, we can gain valuable insights into the world around us, and use that knowledge to make more informed decisions. Throughout this book, we have explored the various aspects of data-based decision making, including the benefits, challenges, and best practices. We have also examined the different tools and techniques that can be used to collect and analyze data, as well as the ethical considerations that must be taken into account.

 Highly data-driven firms are three times more likely to report a major improvement in decision making, according to a large decision survey conducted by PWC. However, only one in three CEOs claim that their company is heavily data-driven. It comes up frequently in meetings with corporate leaders that executives have instant access to large volumes of data. We also learn that their personal intuition or gut feeling plays a significant role in their decision-making. How might the art and science of decision-making be combined better? A more efficient use of data and the capacity to draw insights are seen to present potential for enterprises to generate higher value. Analytics may support an organization's growth and innovation, increase productivity, and improve risk management when they are integrated into the culture of decision-making within the company. The use of facts, metrics, and data to inform strategic business decisions that are in line with a company's goals, objectives, and activities is known as data-driven decision-making. Interactive dashboards, work management platforms, and OKR tools are examples of modern analytics tools that assist individuals overcome prejudice and make the best management decisions that are in line with business strategies. Instead of making decisions based on intuition, opinion, or personal experience, it compiles historical data to examine trends and make better decisions for the future in relation to what has previously worked.

Listen today via Amazon Audible (https://tinyurl.com/ydbyh2t9) 

Or read now on Kindle (https://tinyurl.com/hptundzs) 

This book is part of the “Data-Driven Organizations” series.

1 - The Promise of Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Order today, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9 

Discover the ”Transformative Innovation” (audio & ebook series)

 

Transformative Innovation (https://tinyurl.com/yk64kp3r) 

1 - ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity (Audible) (Kindle)

2 - ChatGPT - The Era of Generative Conversational AI Has Begun (Audible) (Kindle) 

3 - The Race for Quantum Computing (Audible) (Kindle) 

Order today, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9 

Generative AI Fluency (training)

Colleagues, the “Generative AI Fluency” program offers a comprehensive high-level overview of Generative AI, beginning with foundational concepts and terminology and then delving into specific applications such as Large Language Models (LLMs) for text generation and diffusion-based models image creation. Key lessons include an in-depth look at LLMs, AI image generation methods, and hands-on experience with tools like DALL-E and Midjourney. The course concludes by addressing practical aspects of deploying Generative AI in production environments, focusing on data collection, prompt execution, maintenance, and orchestration strategies. Skill-based lessons include: 1) Introduction to Generative AI - covers the key concepts, basic terminology, and typical applications of Generative AI, 2) Large Language Models (LLMs) and Text Generation. In this lesson, you'll dive deeper into one of the most prominent applications of Generative AI: Large Language Models (LLMs), 3) Introduction to AI Image Generation - learn what AI imaging generation is, how it works, and some AI image generation models and techniques, 4) AI Image Generation Tools - learn how to generate images from text with some popular tools, like DALL-E and Midjourney, 5) Generative AI in Production. Learn about the practical considerations for using generative AI in production, including techniques for data collection, prompt execution, maintenance, and orchestration. Projects are based on real-world scenarios and challenges, allowing you to apply the skills you learn to practical situations, while giving you real hands-on experience.

Enroll today (teams & executives are welcome): https://tinyurl.com/yr7rmjfn 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Kindle) or (Audible - coming soon!)

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

Machine Learning with Python for Everyone (Part 1)

Colleagues, the Machine Learning with Python for Everyone Part 1 Learning Foundations focus is on showing you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends. Our focus is on stories, graphics, and code that build your understanding of machine learning; while minimizing pure mathematics. You learn how to load and explore simple datasets; build, train, and perform basic learning evaluation for a few models; compare the resource usage of different models in code snippets and scripts; and briefly explore some of the software and mathematics behind these techniques. Learn how to Build and apply simple classification and regression models, Evaluate learning performance with train-test splits, Assess learning performance with metrics tailored to classification and regression, and measure the resource usage of your learning models. Skill-based training modules include: 1) Software Background - the environment used to run the code and several of the fundamental software packages used throughout the lessons. Mark discusses scikit-learn, seaborn, and pandas--high-level packages that have many powerful features. Mark also introduces numpy and matplotlib--more foundational packages, 2) Mathematical Background - mathematical ideas: probability, linear combinations, and geometry. He approaches these concepts from a practical and computational viewpoint. He introduces them but shies away from theory, 3) Beginning Classification (Part I) - focuses on building, training, and evaluating simple classification models. He starts by introducing you to a practice dataset. It also covers train-test splits, accuracy, and two models: k-nearest neighbors and naive Bayes, 4) Beginning Classification (Part II) - two ways to evaluate classifiers. He shows you how to evaluate learning performance with accuracy and how to evaluate resource utilization for memory and time, 5) Beginning Regression (Part I) - demonstrates building, training, and basic evaluation of simple regression models. He starts with a practice dataset. It discusses different ways of measuring the center of numerical data, and then he discusses two models: k-nearest neighbors and linear regression, and 6) Beginning Regression (Part II) - how to select good models from a basket of possible models. Then, it covers how to evaluate learning and resource consumption of regressors in notebook and standalone scenarios.

Enroll today (teams & executives are welcome): https://tinyurl.com/ya5rrrpf 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Kindle) or (Audible - coming soon!)

Much career success, Lawrence E. Wilson - AI Academy (share with your team)

AI and Machine Learning (Masters Program)

Colleagues, the AI and Machine Learning (Masters Program) is developed by industry professionals, imparts a deep understanding of principles and practices. The average salary for a Machine Learning Engineer is $136,047+ per year in the US. With an intensive curriculum and hands-on projects, participants gain experience in model design, AI/ML solutions, feature engineering, big data handling, and data-driven decision-making. Acquire skills to develop cutting-edge solutions tailored to organizational needs. Skill-based training lessons include: 1) Python Programming Certification - this Python Bootcamp Course will help you master Python programming concepts such as Sequences and File Operations, Conditional statements, Functions, Loops, OOPs, Modules and Handling Exceptions, various libraries such as NumPy, Pandas, Matplotlib, and also focuses on GUI Programming, Web Maps, Data Operations in python and more. Throughout this online Python Course, you will be working on real-time projects and this course will prepare you to clear PCEP, PCAP and PCPP Professional Certification Exams to become a certified programmer; 2) Data Science with Python Certification is accredited by NASSCOM - it will help you master important Python programming concepts such as data operations, file operations, object-oriented programming, and various Python libraries such as Pandas, Numpy, and Matplotlib essential for Data Science. This course is well-suited for professionals and beginners. This training will introduce you to different types of Machine Learning, Recommendation Systems; 3) Artificial Intelligence Certification - you will master essentials of text processing and classifying texts along with important concepts such as Tokenization, Stemming, Lemmatization, POS tagging and many more. You will learn to perform image pre-processing, image classification, transfer learning, object detection, computer vision and also be able implement popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. This course is curated by the industry experts after extensive research to meet the latest industry requirements and demands; 4) ChatGPT Complete Course: Beginners to Advanced - will empower you to engage with the latest in generative AI – ChatGPT. Enhance prompt engineering, integrate plugins, and leverage ChatGPT APIs for increased efficiency. Build a personalized chatbot, drawing insights from real-life applications and projects. Gain a glimpse into the future with GPT-4 and ChatGPT Plus; and 5) PySpark Course Online Training - teaches you how to leverage Python's functionality as you deploy it in the Spark ecosystem.Our online training is instructor-led & enables you to master key PySpark concepts with hands-on demonstrations. Enroll now with this course to learn from top-rated instructors. Electives include: 1) Python Scripting Certification Training, 2) Reinforcement Learning, 3) Graphical Models Certification, and 4) Sequence Learning Certification Training.

Enroll today (teams & executives are welcome): https://tinyurl.com/63huj9fc 

Download your free AI-ML-DL - Career Transformation Guide.

For your listening-reading pleasure:

1 - “AI Software Engineer: ChatGPT, Bard & Beyond” (Audible) or (Kindle)  

2 - “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) or (Kindle) 

3 - “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (Kindle) or (Audible - coming soon!)

Much career success, Lawrence E. Wilson - AI Academy (share with your team)