Machine Learning - Discover the Top 3 Career & Earnings Growth Strategies

Dev Colleagues, on average a Machine Learning Engineer earns $167k+ USD in the US according to Glassdoor. The first strategy is to Get Certified: A high quality credential from a reputable vendor or professional association can boost your income by 10%+. Topping our list is the popular Supervised Machine Learning: Regression and Classification program with some 814k students enrolled. Next is the Structuring Machine Learning Projects (481k enrollees). Third is the Convolutional Neural Networks program from DeepLearning.AI (526k enrolled). Second, Get Published: Write a series of posts or articles on Best Practices, Industry Trends and Emerging Technologies, and publish on DevPlan’s GCP portal, Medium, LinkedIn Articles, Reddit or Technology.org. Then reate a “project portfolio” on GitHub or LinkedIn that showcases your hands-on expertise. A high quality portfolio proves your expertise to future employers and gives you a competitive advantage over other job candidates. Then reate a “project portfolio” on GitHub or LinkedIn that showcases your hands-on prowess. A high quality portfolio proves your expertise to future employers and gives you a competitive advantage over other job candidates. And third, Get Connected: Professional networking and referrals are the most effective method for landing your dream job - within your current employer or at a new company (rather than simply applying for jobs online and standing in the Human Resources queue with the masses). Join and participate in 2-3 high profile groups or professional associations. Hugging Face is an excellent starting place. Consider the Machine Learning Society on LinkedIn. And explore Reddit’s Machine Learning sub-Reddit (2.9m members)

Get started today by enrolling in one or more training-certification programs:

• Convolutional Neural Networks 

• IBM Machine Learning Professional Certificate

• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

• Machine Learning with Python

• Machine Learning Specialization

• Machine Learning with PyTorch

• Structuring Machine Learning Projects 

• Supervised Machine Learning: Regression and Classification 

Enroll today (teams & eces are welcome).

Success awaits you! Lawrence E. Wilson - AI Academy (share & subscribe)

Mathematics for Machine Learning Specialization

Colleagues, the “Mathematics for Machine Learning Specialization” you will build an intuitive understanding, and relating it to Machine Learning and Data Science. Training modules cover: 1) Linear Algebra - what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them, 2) Multivariate Calculus -  look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting, 3) Dimensionality Reduction with Principal Component Analysis - uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning. Applied Learning Project: Through the assignments of this specialisation you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.

Enroll today (teams & execs welcome): https://imp.i384100.net/APPX1N 

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” (Audible) or (Kindle) 

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

The Deep Learning Sentinel (August 2025)

Colleagues, our goal is to provide Deep Learning professionals with up-to-date and actionable information to advance your career and income growth.

Product Launches & Innovations 

• Allen Institute for AI's OMAI Project (August) 

• Apple On-Device and Server Foundation Language Models (July)

• DeepSeek R1 (March)

• Google Gemini 2.5 (March)

• Litmus7's 'Neural Intelligence' Hub (August) 

• OpenAI GPT-5 (August)

Featured Articles

• “AI and Machine Learning” (Harvard Business Review) 

• “Deep Learning” (Alan Turning Institute)

• “Deep Learning” (MIT Technology Review)

• “Deep Learning News & Articles” (IEEE Spectrum)

• “Features, Models, and Applications of Deep Learning in Music Composition” (American Journal of Information Science and Technology)

Featured Resources

• AWS Deep Learning

• DeepLearning.AI

• Google Vertex AI Platform

• MathWorks - Deep Learning

• nVidia Deep Learning Institute

Events

• AI & Big Data Expo Europe - September 24-25, 2025 (Amsterdam, Netherlands)

• AI Infra Summit - September 9-11, 2025 (Santa Clara, California, USA)

• Conference on Robot Learning (CoRL) - September 27-30, 2025 (Seoul, South Korea)

• Computer Science, Machine Learning and Big Data - September 14, 2025 (Bali, Indonesia)

• Meta Connect - September 17-18, 2025 (Online/Virtual)

• The AI Conference - September 17-18, 2025 (San Francisco, California, USA)

Career Development - Certs & Training

• Data Structures, Algorithms, and Deep Learning Optimization

• Deep Learning A-Z 2025: Neural Networks, AI & ChatGPT

• Deep Learning and Reinforcement Learning

• Deep Learning Specialization

• Deep Learning with TensorFlow & PyTorch

• Deep Learning with Tensorflow, Keras and PyTorch 

• Deep Learning Specialization

• Deep Learning with PyTorch

• Introduction to Deep Learning

• Machine Learning with Python: from Linear Models to Deep Learning

• Machine Learning A-Z: AI, Python & R

• Neural Networks and Deep Learning

• Structuring Deep Learning Projects

• TensorFlow Developer Professional Certificate

Enroll today (teams & execs are welcome). 

Much success in your DL career journey, Lawrence E. Wilson - AI Academy (share with colleagues & friends)

Generative AI for Everyone (training)

Colleagues, in the “Generative AI for Everyone” program you will learn what generative AI is and how it works, its common use cases, and what this technology can and cannot do, and How to think through the lifecycle of a generative AI project, from conception to launch, including how to build effective prompts. The potential opportunities and risks that generative AI technologies present to individuals, businesses, and society. Skills you'll gain - Generative AI Tools, Large Language Models, AI strategy, Generative AI and AI Productivity. Instructed by AI pioneer Andrew Ng, Generative AI for Everyone offers his unique perspective on empowering you and your work with generative AI. Andrew will guide you through how generative AI works and what it can (and can’t) do. You’ll get hands-on time with generative AI projects to put your knowledge into action and gain insight into its impact on both business and society. Skill-based lessons include: 1) Introduction to Generative AI - How Generative AI works, LLMs as a thought partner, AI is a general purpose technology, Writing, Reading, Chatting, What LLMs can and cannot do, Tips for prompting, Image generation, 2) Generative AI Projects - Using generative AI in software applications, Trying generative AI code yourself, Lifecycle of a generative AI project, Cost intuition, Retrieval Augmented Generation (RAG), Fine-tuning, Pre-training an LLM, Choosing a model, How LLMs follow instructions: Instruction tuning and RLHF, Tool use and agents, and 3) Generative AI in Business and Society - Day-to-day usage of web UI LLMs, Task analysis of jobs, Additional job analysis examples, New workflows and new opportunities, Teams to build generative AI software, Automation potential across sectors, Concerns about AI, Artificial General Intelligence, Responsible AI, and Building a more intelligent world.

Enroll today (teams & execs welcome): https://imp.i384100.net/9gKrZ4 

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” (Audible) or (Kindle) 

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

Google Prompting Essentials Specialization

Colleagues, the “Google Prompting Essentials Specialization” program will enable you to practice using 5 steps to write effective prompts, apply prompting techniques to help you with every-day work tasks, use prompting to speed up data analysis and build presentations, and design prompts to create AI agents to role-play conversations and get expert feedback. Acquire highly marketable skills in Report Writing, Machine Learning, Document Management, Graphing, Human Centered Design, AI Personalization, Concision, Generative AI Agents, Data Visualization, Artificial Intelligence, Data Presentation, and Prompt Engineering. This four course program includes teaching modules: 1) Start Writing Prompts like a Pro, 2) Design Prompts for Everyday Work Tasks, 3) Speed Up Data Analysis and Presentation Building, and 4) Use AI as a Creative or Expert Partner. Through hands-on exercises and real-world examples, you’ll learn how to use AI to: Save time: Craft emails, brainstorm with ease, build tables and trackers, and summarize lengthy documents, Uncover and share powerful insights: Identify patterns in data, create compelling data visualizations, and even rehearse presentations., Tackle complex projects: Transform abstract ideas into actionable steps, use AI to role-play conversations, and get expert feedback, and Use AI responsibly: Evaluate your outputs for bias and errors, and iterate to get more helpful results. In the Applied Learning Project you will practice writing prompts for generative AI tools, developing practical skills you can apply right away. You'll use AI for idea generation, content creation, summarization, data analysis, and building AI agents.

Enroll today (teams & execs welcome): imp.i384100.net/bO1o6k 

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) or (Kindle) 

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

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