IBM Generative AI Engineering Professional Certificate

Colleagues, in the “IBM Generative AI Engineering Professional Certificate” program you will build highly sought-after gen AI engineering skills and practical experience in just 6 months. Acquire job-ready skills employers are crying out for in gen AI, machine learning, deep learning, NLP apps, and large language models in just 6 months. Build and deploy generative AI applications, agents and chatbots using Python libraries like Flask, SciPy and ScikitLearn, Keras, and PyTorch. Key gen AI architectures and NLP models, and how to apply techniques like prompt engineering, model training, and fine-tuning. Apply transformers like BERT and LLMs like GPT for NLP tasks, with frameworks like RAG and LangChain. Gain highly marketable skills in Machine Learning, Algorithms, Artificial Neural Networks, Deep Learning, Human Learning, Machine Learning Algorithms, Network Model, Applied Machine Learning, Network Architecture, Python Programming, and Software Testing. Take a deep dive into AI, gen AI, and prompt engineering, along with data analysis, machine learning, and deep learning using Python. You'll work with libraries like SciPy and scikit-learn and build apps using frameworks and models such as BERT, GPT, and LLaMA. You'll use Hugging Face Transformers, PyTorch, RAG, and LangChain for developing and deploying LLM NLP-based apps, while exploring tokenization, language models, and transformer techniques. Skill-based training modules include: 1) Introduction to Artificial Intelligence (AI), 2) Generative AI: Introduction and Applications, 2) Prompt Engineering Basics, 3) Python for Data Science, AI & Development, 4) Developing AI Applications with Python and Flask, 4) Building Generative AI-Powered Applications with Python, 5) Data Analysis with Python, 6) Machine Learning with Python, 7) Introduction to Deep Learning & Neural Networks with Keras, 8) Generative AI and LLMs: Architecture and Data Preparation, 9) Gen AI Foundational Models for NLP & Language Understanding, 10) Generative AI Language Modeling with Transformers, 11) Generative AI Engineering and Fine-Tuning Transformers, 12) Generative AI Advance Fine-Tuning for LLMs, 13) Fundamentals of AI Agents Using RAG and LangChain, and 14) Project: Generative AI Applications with RAG and LangChain. 

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

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)

Generative AI (Nanodegree Program)

Embark on a transformative journey into Generative AI! According to Talent.com the average salary for a Generative AI Engineer is $187,306 USD. The “Generative AI - Nanodegree Program” Begin by diving into the essentials with an introductory course, progress to mastering text generation with Large Language Models, unravel the complexities of image creation in computer vision and cap it off by bringing AI to life in real-world applications. From foundational theories to building sophisticated chatbots and AI agents, this program will empower you with job-ready skills in the exciting field of Generative AI. Skill-based training lessons address: 1) Generative AI Fundamentals - Generative AI Fluency • Image classification • Transfer learning • Training neural networks • Hugging Face • Parameter-Efficient Fine-Tuning • Prompt Engineering • Deep learning • PyTorch • Foundation Models • Ethical AI, 2) Large Language Models (LLMs) & Text Generation - Together AI API • Search implementation in Python • NLP transformers • GPT • Selenium • Large Language Models • Data cleaning • Natural language processing • Bert • OpenAI API • Retrieval-Augmented Generation • Transformer neural networks • Prompt Engineering • Pandas • PyTorch • Tokenization • Cosine • API requests • Recurrent neural networks • Attention mechanisms • Text generation • Beautifulsoup • Data quality assessment • Word embeddings • Data scraping, 3) Computer Vision and Generative AI - Image pre-processing • Transfer learning • Word embeddings • Ethical AI • Diffusion Models • Yolo algorithm • Model evaluation • Text generation • Computer vision fluency • Image classification • Large Language Models • Pandas • Image generation • Training neural networks • Convolutional neural networks • Parameter-Efficient Fine-Tuning • Image segmentation • Computer Vision Transformers • Tokenization • Data quality assessment • Generative adversarial networks, and 4) Building Generative AI Solutions - Vectors • Retrieval-Augmented Generation • OpenAI API • LangChain.

Enroll today (teams & execs welcome): https://imp.i115008.net/9LJxMe 

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)

“Transformative Innovation” Audio and eBook series

Transformative Innovation series:

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 

Machine Learning - Discover the Top 3 Career & Earnings Growth Strategies for 2025

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 (https://imp.i384100.net/555bxN) 

• Structuring Machine Learning Projects (https://imp.i384100.net/RGPGQa) 

• Convolutional Neural Networks (https://imp.i384100.net/YRRxkK) 

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

Generative AI: Prompt Engineering Basics (training)

Colleagues, the “Generative AI: Prompt Engineering Basics” program will equip you to explain the concept and relevance of prompt engineering in generative AI models, apply best practices for creating prompts and explore examples of impactful prompts, practice common prompt engineering techniques and approaches for writing effective prompts, and explore commonly used tools for prompt engineering to aid with prompt engineering. You will gain high-demand skills in Artificial Intelligence, Computer Science, Artificial Intelligence and Machine Learning (AI/ML), ChatGPT, OpenAI, and Generative AI. Explain the concept and relevance of prompt engineering in generative AI models. Apply best practices for creating prompts and explore examples of impactful prompts. Practice common prompt engineering techniques and approaches for writing effective prompts. Explore commonly used tools for prompt engineering to aid with prompt engineering. Learn about prompt techniques like zero-shot and few-shot, which can improve the reliability and quality of large language models (LLMs). You will also explore various prompt engineering approaches like Interview Pattern, Chain-of-Thought, and Tree-of-Thought, which aim at generating precise and relevant responses. You will be introduced to commonly used prompt engineering tools like IBM Watsonx Prompt Lab, Spellbook, and Dust. Skill-based training modules include: 1) Prompt Engineering for Generative AI, 2) Prompt Engineering: Techniques and Approaches, and 3) Course Quiz, Project, and Wrap-up. 

Enroll today (teams & execs welcome): https://imp.i384100.net/xLn1zA 

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)

Deep Learning: Convolutional Neural Networks in Python

Colleagues, in the “Deep Learning: Convolutional Neural Networks in Python” program you will learn Tensorflow, CNNs for Computer Vision, Natural Language Processing (NLP), Data Science and Machine Learning. [79 lectures - 13+ hours of training]. Understand convolution and why it's useful for Deep Learning and explain the architecture of a convolutional neural network (CNN). Implement a CNN in TensorFlow 2. Apply CNNs to challenging Image Recognition tasks and CNNs to Natural Language Processing (NLP) for Text Classification (e.g. Spam Detection, Sentiment Analysis). Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion. Skill-based training lessons include: 1) Google Colab, 2) Machine Learning and Neurons, 3) Feedforward Artificial Neural Networks, 4) Convolutional Neural Networks, 5) Natural Language Processing (NLP), 6) Convolution In-Depth, 7) Convolutional Neural Network Description, 8) Practical Tips, 9) Loss Functions, 10) Gradient Descent, 11) Setting Up Your Environment (FAQ by Student Request), 12) Extra Help With Python Coding for Beginners, and 13) Effective Learning Strategies for Machine Learning.

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

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) (Kindle)

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