Artificial Intelligence for Business + ChatGPT Prize

AI Colleagues, in the “Artificial Intelligence for Business + ChatGPT Prize” training you will learn to Optimize business processes, Master the General AI Framework, Implement Q-Learning, Save and Load a model, Build an Optimization Model, Implement Early Stopping, Maximize Efficiency, Maximize Revenues, Minimize Costs, Implement Thompson Sampling and Deep Q-Learning, Leverage AI to make the best decision, Build an AI Environment from scratch, Implement Online Learning, Build an Artificial Brain, and Implement Regression Analysis. [118 lectures - 15 Hours of training]. Training modules focus on: 1) Optimizing Business Processes, 2) Minimizing Costs, 3) Minimizing Costs, Maximizing Revenues, and 4) Artificial Neural Networks. 

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

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)

Natural Language Processing Specialization

Colleagues, the “Natural Language Processing Specialization” will equip you to use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies & translate words, dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words, recurrent neural networks, LSTMs, GRUs & Siamese networks for sentiment analysis, text generation & named entity recognition, and encoder-decoder, causal, & self-attention to machine translate complete sentences, summarize text, and answer questions. Build highly marketable skills in Machine Translation, Transformers, Sentiment Analysis, Word2vec, and Attention Models. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. Training modules include: 1) Natural Language Processing with Classification and Vector Spaces, 2) Natural Language Processing with Probabilistic Models, 3) Natural Language Processing with Sequence Models, and 4) Natural Language Processing with Attention Models. 

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

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)

Prompt Engineering for ChatGPT (training)

Colleagues, in the “Prompt Engineering for ChatGPT” program you will apply prompt engineering to effectively work with large language models, like ChatGPT, use prompt patterns to tap into powerful capabilities within large language models, and create complex prompt-based applications for your life, business, or education. Acquire highly marketable skill in Prompt Engineering, ChatGPT, Chain of Thought Prompting, Prompt Patterns, and Large Language Models. This course introduces students to the patterns and approaches for writing effective prompts for large language models.  Anyone can take the course and the only required knowledge is basic computer usage skills, such as using a browser and accessing ChatGPT. Students will start with basic prompts and build towards writing sophisticated prompts to solve problems in any domain. By the end of the course, students will have strong prompt engineering skills and be capable of using large language models for a wide range of tasks in their job, business, personal life, and education, such as writing, summarization, game play, planning, simulation, and programming. Skill-based training lessons address: 1) Introduction to Prompts, 2) Prompt Patterns I, 3) Few-Shot Examples, 4) Prompt Patterns II, and 5) Prompt Patterns III. 

Enroll today (teams & execs welcome): https://imp.i384100.net/DyWV72 

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)

Supervised Machine Learning: Regression and Classification

Colleagues, in the “Supervised Machine Learning: Regression and Classification” program you will build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn, and train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression. Develop high-demand skills in Linear Regression, Regularization to Avoid Overfitting, Logistic Regression for Classification, Gradient Descent, and Supervised Learning. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. Training modules: Week 1: Introduction to Machine Learning - Applications of machine learning, Supervised learning, Supervised learning, Unsupervised learning, Jupyter Notebooks, Linear regression model, Cost function formula and intuition, Visualizing the cost function, Gradient descent, Implementing gradient descent, Learning rate, Gradient descent for linear regression, and Running gradient descent; Week 2: Regression with multiple input variables - learn some methods for improving your model's training and performance, such as vectorization, feature scaling, feature engineering and polynomial regression. At the end of the week, you'll get to practice implementing linear regression in code; and Week 3: Classification - predict categories using the logistic regression model. You'll learn about the problem of overfitting, and how to handle this problem with a method called regularization. You'll get to practice implementing logistic regression with regularization. 

Enroll today (teams & execs welcome): https://imp.i384100.net/555bxN

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)

Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs)

Colleagues, the “Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs)” program is a quick-start guide to help people use and launch LLMs like GPT, T5, and BERT at scale. It shows a step-by-step approach to building and deploying LLMs, with real-world case studies to illustrate the concepts. The video covers topics such as building recommendation engines with siamese BERT architectures, launching an information retrieval system with OpenAI embeddings and GPT3, and building an image captioning system with the vision transformer and GPT-J. This guide provides clear instructions and best practices for using LLMs. It fills a gap in the market by providing a guide to using LLMs and will be a valuable resource for anyone looking to use LLMs in their projects. Learn to Launch an application using proprietary models with an example of an information retrieval system with OpenAI embeddings and GPT3 for Question/Answering, Fine-tune GPT3 with custom examples using their API to get better results, basics of prompt engineering with GPT3 to get more nuanced examples by building a chatbot with persona style depending on who they are talking to using the information retrieval system and Deploy custom LLMs to the cloud. Skill-based lessons address: 1) Introduction to Large Language Models - Overview of Large Language Models, Semantic Search with LLMs, First Steps with Prompt Engineering, 2) Getting the Most Out of LLMs - Optimizing LLMs with Fine-Tuning, Advanced Prompt Engineering, Customizing Embeddings + Model Architectures, and 3) Advanced LLM Usage - Moving Beyond Foundation Models, Advanced Open-source LLM Fine-Tuning and Moving LLMs into Production. 

Enroll today (teams & executives are welcome): https://tinyurl.com/3m7ccp3k 

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)

Introduction to Generative AI for Software Development (training)

Developers, in the “Introduction to Generative AI for Software Development” program you will learn to Integrate generative AI in development. Learn to use generative AI tools from initial design to deployment, enhancing your efficiency and creativity, Optimize your code quality. Improve your coding, if you’re just starting and need help fixing bugs or an experienced developer breaking new ground, Experiment quickly - Using LLMs can speed up your ability to prototype and test new features, allowing you to quickly iterate and ship your code, Learn how LLMs work. By knowing how machine learning systems work, you’ll be able to use them more effectively to support your work as a developer. Gain highly marketable skills involving Prompting best practices for software development, Assigning an LLM a role or persona, Designing data structures for real world deployment at scale, Analyzing code with an LLM, and Pair-coding with an LLM. Skill-based training lessons cover: 1) Introduction to Generative AI, 2) Pair-coding with an LLM, and 3) Leveraging an LLM for code analysis. By the end of this course, you will be able to: Understand the differences between machine learning and traditional software development. Describe how large language models generate text. Prompt an LLM to assist in the tasks that make up the software developer role. Guide an LLM to complete a task in a specific way by writing detailed prompts and iterating to improve output. Leverage the depth of software development knowledge encoded in an LLM by prompting it to assume specific job roles or personas. Write code quickly using an LLM as a pair-coding partner. And Analyze code for efficiency, security, and performance using an LLM. 

Enroll today (teams & execs welcome): http://imp.i384100.net/RGGkj7 

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)

Natural Language Processing with Python (NLP)

Colleagues, in the “Natural Language Processing with Python (NLP)” program you will learn to use Machine Learning, Spacy, NLTK, SciKit-Learn, Deep Learning, and more to conduct Natural Language Processing. [80 lectures - 11+ training hours]. Work with Text Files with Python and PDF files in Python, utilize Regular Expressions for pattern searching in text, use Spacy for ultra fast tokenization, learn about Stemming and Lemmatization, understand Vocabulary Matching with Spacy, use Part of Speech Tagging to automatically process raw text files, understand Named Entity Recognition, visualize POS and NER with Spacy, use SciKit-Learn for Text Classification, use Latent Dirichlet Allocation for Topic Modelling, learn about Non-negative Matrix Factorization, use the Word2Vec algorithm, NLTK for Sentiment Analysis, and Deep Learning to build out your own chat bot. Skill-based training lessons address: 1) Python Text Basics, 2) Natural Language Processing Basics, 3) Part of Speech Tagging and Named Entity Recognition, 4) Text Classification, 5) Semantics and Sentiment Analysis, 6) Topic Modeling, and 7) Deep Learning for NLP.

Enroll today (teams & execs welcome): https://tinyurl.com/yudx985p

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: Advanced Computer Vision (GANs, SSD)

AI Colleagues, in the “Deep Learning: Advanced Computer Vision (GANs, SSD)” program you will learn VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python. [116 lectures - 16+ hours of training]. Understand and apply transfer learning, convolutional neural nets such as VGG, ResNet and Inception, object detection algorithms like SSD, apply neural style transfer, computer vision topic, Class Activation Maps, GANs (Generative Adversarial Networks), Object Localization Implementation Project, and understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion. Skill-based training modules include: 1) Google Colab & Getting Setup, 2) Machine Learning Basics, 3) Artificial Neural Networks (ANN), 4) Convolutional Neural Networks (CNN) Review, 5) VGG and Transfer Learning, 6) ResNet (and Inception), 7) Object Detection (SSD / RetinaNet), 8) Neural Style Transfer, 9) Class Activation Maps, 10) GANs (Generative Adversarial Networks), 11) Object Localization Project, 12) Keras and Tensorflow 2 Basics, 13) With Python Coding for Beginners, and 14) Effective Learning Strategies for Machine Learning.

Enroll today (teams & execs welcome): https://tinyurl.com/36cvnnr4 

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)