Computer Vision with Embedded Machine Learning

Colleagues, the “Computer Vision with Embedded Machine Learning” will equip you to accomplish CV tasks, and as ML becomes faster and more efficient, we can deploy these techniques to embedded systems. This course, offered by a partnership among Edge Impulse, OpenMV, Seeed Studio, and the TinyML Foundation, will give you an understanding of how deep learning with neural networks can be used to classify images and detect objects in images and videos. You will have the opportunity to deploy these machine learning models to embedded systems, which is known as embedded machine learning or TinyML. Familiarity with the Python programming language and basic ML concepts (such as neural networks, training, inference, and evaluation) is advised to understand some topics as well as complete the projects. Some math (reading plots, arithmetic, algebra) is also required for quizzes and projects. If you have not done so already, taking the "Introduction to Embedded Machine Learning" course is recommended. This course covers the concepts and vocabulary necessary to understand how convolutional neural networks (CNNs) operate, and it covers how to use them to classify images and detect objects. The hands-on projects will give you the opportunity to train your own CNNs and deploy them to a microcontroller and/or single board computer. Skill-based training modules include: 1) Image Classification: how digital images are created and stored on a computer. Next, we review neural networks and demonstrate how they can be used to classify simple images. Finally, we walk you through a project to train an image classifier and deploy it to an embedded system, 2) Convolutional Neural Networks: the basics of convolutional neural networks (CNNs) and how they can be used to create a more robust image classification model. We look at the internal workings of CNNs (e.g. convolution and pooling) along with some visualization techniques used to see how CNNs make decisions. We introduce the concept of data augmentation to help provide more data to the training process. You will have the opportunity to train your own CNN and deploy it to an embedded system, and 3)Object Detection: object detection and how it differs from image classification. We will go over the math involved to measure objection detection performance. Afterwards, we will introduce several popular object detection models and demonstrate the process required to train such a model in Edge Impulse. Finally, you will be asked to deploy an object detection model to an embedded system.

Enroll today (teams & executives are welcome): https://tinyurl.com/5n7f7r5z 

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

“Transformative Innovation” book series for your listening-reading pleasure: 

 

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) 

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

AI Jobs Pay An Average of $146,244 with 69,045 Openings in the US

Colleagues, the CNBC article “U.S. companies are on a hiring spree for A.I. jobs—and they pay an average of $146,000” (July 13, 2023). This article cited recent data from research firm Adzuna that goes on to state there are “69,045 jobs in the U.S. cited AI needs, and 3,575 called for generative AI work in particular … the average job using the skill pays $146,244.” 

The new ebook “AI Software Engineer - ChatGPT, Bard … and Beyond” aims to help Software Engineers and Developers capture their ideal job offer and manage their medium-to-long-term career growth in the global Artificial Intelligence (AI) arena. First, we will look at the global market overview for AI Globally. The artificial intelligence (AI) market size was valued at around $136.55 billion in 2022 (Grand View Research, 2022). Also, it was projected to have a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030. if you're interested in AI, you should be familiar with and have expertise with at least one of the following programming languages: Python, C/C++, MATLAB. According to Indeed, salaries for artificial intelligence professionals often range from $99,568 for a full-stack developer to $142,318 for a data scientist. Glassdoor reports the annual average base salary for artificial intelligence professionals in the US is $120,048. Based on a Talent.com report, the average artificial intelligence salary is $143,054 annually. Entry positions start at $115,000, and experienced employees can earn up to $200,000 yearly. 

AI Software Engineer: ChatGPT, Bard & Beyond (Audible) (Kindle) 

Access the “Transformative Innovation” book series on Amazon today:

 

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

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

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

And download your free AI-ML-DL - Career Transformation Guide.

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

“ChatGPT … ChatGPT, Bard and Beyond” (new audio & ebook)

Colleagues, the new book “ChatGPT … ChatGPT, Bard and Beyond” (Audible) (Kindle) explores how generative conversational AI has the potential to improve accessibility for people with disabilities and those who struggle with language barriers, as AI models can be trained to understand and respond to a wide range of languages and dialects. Listen or read this new book now on Amazon. Generative conversational AI represents a major shift in how we interact with technology and has the potential to improve many aspects of our lives, from customer service and support to healthcare and accessibility. 

Audible (https://audible.com)

Kindle (https://tinyurl.com/yhf7ff27) 

Access the Transformative Innovation book series on Amazon today! 

 

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

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

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

And download your free AI-ML-DL - Career Transformation Guide.

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

IBM Professional Certificate in Python Data Science

Colleagues, the “IBM Professional Certificate in Python Data Science” program is taught by industry experts and focused on hands-on learning and job readiness. You will work with real datasets and will be given no-charge access to tools like Jupyter notebooks in the IBM Cloud. You will utilize popular Python toolkits and libraries such as pandas, numpy, matplotlib, seaborn, folium, scipy and scikitlearn. Understand Python language basics and how they apply to data science, practice iterative data science using Jupyter notebooks on IBM Cloud, analyze data using Python libraries like pandas and numpy, create stunning data visualizations with matplotlib, folium, and seaborn, build machine learning models using scipy and scikitlearn, and demonstrate proficiency in solving real life data science problems. In addition you will learn to create and access a database instance on cloud, write basic SQL statements: CREATE, DROP, SELECT, INSERT, UPDATE, DELETE, filter, sort, group results, use built-in functions, compose nested queries, access multiple tables, and access databases from Jupyter using R and SQL to query real-world datasets.

Enroll today (teams & execs are welcome): https://fxo.co/HFFK 

Download your free Data Science - Career Transformation Guide.

Here is the “Data-Driven Organizations” book series:  

1 - Data-Driven Decision-Making  (Audible) (Kindle)

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

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

Google Bard AI (training & demonstrations)

Colleagues, in the “Google Bard AI” program you will gain expertise with this new generative AI chatbot. Learn about the fundamentals of Bard AI, its evolution, common tools, and business use cases, develop an in-depth understanding of prompt engineering in Bard AI, and identify the differences between Google Bard and ChatGPT. Specialize in the application Of Google Bard - Introduction to chatbots, AI, and machine learning, Definition, key components, use cases, and limitations of Google Bard AI., Business benefits and working of Google Bard AI, Comparison between Google Bard and ChatGPT, Demonstrations of prompts and best practices for creating prompts, and Examples of Bard AI prompts for different use cases. Part 1 - Bard AI Fundamentals: What is a Chatbot?, Understanding AI/M, What is Google Bard AI?, Future of Bard, Limitations of Google Bard AI, Use Cases for Google Bard AI, Business Benefits, Whiteboard - How it all works, Demonstration - How to Get Started & Prompts Example, Comparing Bard vs ChatGPT; and Part 2 - Bard AI Prompt Demonstrations - What is a Prompt?, Best practices for writing prompts and demonstrations: Asking Questions, Language Translation, Long Form Docs, SEO Content, Content Modification, Instruction Generation, Writing Computer Code, Solving Math Problems, Create YouTube Video Outline, Write a Blog Article, Task Automation, Email Creation with Gmail, Summarize, Explain, Few Shot, etc), Social Media Tasks, and Create Google Docs and Sheets.

Enroll today (teams & executives are welcome): https://tinyurl.com/yh6uv8ej 

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

“Transformative Innovation” book series for your listening-reading pleasure: 

 

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) 

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

Advanced Predictive Modelling in R Certification Training

Colleagues, the “Advanced Predictive Modelling in R Certification Training” program will boost your career and earnings potential.  R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions. This Certification Training is intended for a broad audience as both, an introduction to predictive models as well as a guide to applying them, covering topics such as Ordinary Least Square Regression, Advanced Regression, Imputation, Dimensionality Reduction etc. Readers will also be able to learn basics of Statistics, such as Correlation and Linear Regression Analysis. Training modules will equip you in: 1) Basic Statistics in R - introduction to statistics and will conduct best test and exploratory analysis - Covariance & Correlation, Central Limit Theorem, Z Score, Normal Distributions, Hypothesis, 2) Ordinary Least Square Regression 1 - learn basic regression and multiple regression and will learn how to present the same graphically - Bivariate Data, Quantifying Association, The Best Line: Least Squares Method, Regressions, Simple Linear Regression, Deletion Diagnostics and Influential Observations, Regularization, 3) Ordinary Least Square Regression 2 - linear regression and make the model a better fit, make necessary transformation check for over fitting and under fitting and outliers’ identification and treatment, Model fitting using Linear Regression, Performing Over Fitting & Under Fitting, Collinearity, Heteroscedasticity, 4) Logistic Regression - assess the problems related with Linear Probability Model, will be introduced to logistic regression and various uses of the same and its industry usage - Binary Response Regression Model, Linear regression as Linear Probability Model, Problems with Linear Probability Model, Logistic Function and Curve, Goodness of fit matrix, IInteractions Logistic Regression, Multinomial Logit, Interpretation, Ordered Categorical Variable, 5) Advanced Regression - logistic regression and learn about more varied usage of logistic regression on various dataset, Poisson Regression, Model Fit Test, Offset Regression, Poisson Model with Offset, Negative Binomial, Dual Models, Hurdle Models, Zero-Inflated Poisson Models, Variables used in the Analysis, Poisson Regression Parameter Estimates, Zero-Inflated Negative Binomial, 6) Imputation - learn about addressing missing values and how to impute it using various processes, 7) Forecasting 1 - an introduction on forecasting and time series data, 8) Dimensionality Reduction, and 8) Survival Analysis - Churn analysis and Regression on time series data with time component. 

Enroll today (teams & executives are welcome): https://tinyurl.com/2tu4musv 

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

“Transformative Innovation” book series for your listening-reading pleasure: 

 

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) 

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