Machine Learning - Discover the Top 3 Career & Earnings Growth Strategies for 2026

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:

• Supervised Machine Learning: Regression and Classification 

• Structuring Machine Learning Projects 

• Convolutional Neural Networks 

Success awaits you! Lawrence E. Wilson - AI Academy (kindly subscribe and share with colleagues)

TensorFlow Developer Professional Certificate

Colleagues, would you like to accelerate your AI career growth and increase your earnings potential? in the “TensorFlow Developer Professional Certificate” program you will learn to build AI apps with Tensorflow. Build, train, and optimize deep neural networks and dive deep into Computer Vision, Natural Language Processing, and Time Series Analysis, along with best practices and hands-on experience in one of the most in-demand deep learning frameworks. You will also gain high-demand skills involving Applied Machine Learning, Artificial Neural Networks, Computer Vision, Convolutional Neural Networks, Data Preprocessing, Deep Learning, Embeddings, Forecasting, Image Analysis, Machine Learning, Model Evaluation, Natural Language Processing, Predictive Modeling, Recurrent Neural Networks (RNNs), Time Series Analysis and Forecasting, and Transfer Learning. Tools you will learn cover Classification Algorithms, Generative AI, Keras - Neural Network Library - , and Tensorflow. Skill based training modules address: 1) Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning, 2) Convolutional Neural Networks in TensorFlow, 3) Natural Language Processing in TensorFlow, and 4) Sequences, Time Series and Prediction.

Enroll today (teams & execs welcome): https://imp.i384100.net/n4N966

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) 

Success awaits you at the AI Academy (kindly subscribe and share with your colleagues)

Certified Agentic AI Expert™ (CAAE)

Colleauges, the Certified Agentic AI Expert program from the Blockchain Council is your definitive path to earning a globally recognized Agentic AI credential. [14 modules: 56 lessons: 6 Hours: As industries rapidly adopt autonomous systems, the demand for professionals who can develop, deploy, and manage Agentic AI solutions is growing at an unprecedented rate. This agentic AI certification is designed for forward thinking professionals who want to learn Agentic AI and lead innovation in areas like smart automation, adaptive decision making, and scalable AI infrastructures. You’ll gain real world expertise in building intelligent agents that can act, learn, and make decisions with minimal human input, transforming what’s possible in finance, healthcare, logistics, and enterprise technology. This program also grants 15 hours of official Continuing Professional Development, giving your achievement verified global credibility and strengthening your professional standing across modern tech industries. Skill-oriented lessons include: 1)Agentic AI Paradigm, 2) Agent Capabilities, 3) Automation and Workflow Optimization, 4) AI Strategy for Businesses, 5) Frameworks, 6) Post Deployment, 7) AI Governance and Risk Management, 8) Evaluating AI Agent Performance aka Business Metrics, 9) AI Agents Security, Future Trends in Agentic AI, 10) Ethical Design of AI Agents, 11) Technology Stack, and 12) Use Cases.

Enroll today - teams and executives are welcome: https://tinyurl.com/bdhf8z9a  

Recommended Reading:

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) (Kindle)

4 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)
Much success in your Cyber-AI career, AI Academy (please subscribe and share with you colleagues)

Machine Learning Engineer - 10 Best Practices, Portals & Career Development (April 2026)

Colleagues, this post will help you accelerate your career and income potential in the Machine Learning domain. Whether you are new to ML or or looking to advance your existing ML career path this post has valuable information for you.

Best Practices

1. Master Fundamentals: Deepen understanding of math, statistics, and core ML algorithms.

2. Hands-on Project Building: Create a robust portfolio of diverse, real-world projects (e.g., on GitHub, Kaggle).

3. Stay Current with Research: Regularly read papers from leading institutions (DeepMind, OpenAI) and platforms (Hugging Face) to understand cutting-edge advancements.

4. Specialize Strategically: Focus on niche areas like NLP (Hugging Face), computer vision, or MLOps, aligning with industry trends.

5. Develop Deployment Skills: Gain proficiency in bringing models to production (MLOps, cloud platforms like Azure AI).

6. Contribute to Open Source: Actively engage in projects on platforms like Hugging Face or GitHub to collaborate and gain visibility.

7. Network Actively: Connect with peers and mentors on platforms like LinkedIn and at conferences to learn and find opportunities.

8. Prioritize Ethical AI: Understand and apply principles of responsible AI, fairness, and interpretability in all projects.

9. Cultivate Business Acumen: Understand how ML solutions solve real-world business problems and communicate technical concepts clearly.

10. Continuous Learning: The field evolves rapidly, so commit to lifelong learning through courses, certifications, and experimentation.

Resource Portals

• Anthropic

• Hugging Face

• GitHub Artificial Intelligence

• Google Deepmind

• MIT Technology Review - Artificial Intelligence

• Meta AI

• Microsoft AI

• OpenAI

• Stanford AI Lab

Specializations, Master Classes and Certifications

• Applied Machine Learning Specialization 

• AWS Certified Machine Learning

• AWS Machine Learning Engineer

• Data Structures, Algorithms, and Machine Learning Optimization

• Linear Algebra for Machine Learning

• Machine Learning / AI Engineer

• Machine Learning: Foundations using R Specialization

• Machine Learning with PyTorch

• Machine Learning Fundamentals with Python and SQL Specialization

• Structuring Machine Learning Projects

• Machine Learning Specialization

• IBM Machine Learning Professional Certificate

• Structuring Machine Learning Projects

• Understanding Machine Learning 

Enroll today (teams & executives are welcome).  

Much success in your career journey, AI Academy (share with your team)

Top 3 Strategies to advance your TensorFlow Developer career (2026)

Colleagues, the average salary for a TensorFlow Developer is over $132,215 according to ZipRecruiter.  First, Get Certified: A high quality cert from a reputable vendor or professional association may boost your income by 5%-10%. In the Deep Learning with TensorFlow program you will master popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow package in Python. Deep Learning with Tensorflow, Keras and PyTorch - build deep learning models in all the major libraries: TensorFlow, Keras, and PyTorch along with artificial neural networks. And Advanced Deployment Scenarios with TensorFlow - gain expertise in TensorFlow Extended, TF Hub, Transfer learning, Inference, Module storage, Text based model, Word embeddings, Experimenting with embeddings, Colab1m, Classify cats and dogs and Transfer learning, and Tensorboard and Federated Learning. Second, Get Published: Write a 1-2 page article on Best Practices or Industry Trends for Medium, LinkedIn Articles or Technology.org. Third, Get Connected: Join the TensorFlow Forum. Sign-up for the TensorFlow Community on GitHub and subscribe to the TensorFlow User Group on LinkedIn.

Enroll in one or more programs today (teams & execs welcome): 

• Deep Learning with TensorFlow

• Deep Learning with Tensorflow, Keras and PyTorch

• Advanced Deployment Scenarios with TensorFlow

Much career success from the Artificial Intelligence Academy (please subscribe and share with your colleagues)