Machine Learning with Python for Everyone (Part 1)

Colleagues, the Machine Learning with Python for Everyone Part 1 Learning Foundations focus is on showing you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends. Our focus is on stories, graphics, and code that build your understanding of machine learning; while minimizing pure mathematics. You learn how to load and explore simple datasets; build, train, and perform basic learning evaluation for a few models; compare the resource usage of different models in code snippets and scripts; and briefly explore some of the software and mathematics behind these techniques. Learn how to Build and apply simple classification and regression models, Evaluate learning performance with train-test splits, Assess learning performance with metrics tailored to classification and regression, and measure the resource usage of your learning models. Skill-based training modules include: 1) Software Background - the environment used to run the code and several of the fundamental software packages used throughout the lessons. Mark discusses scikit-learn, seaborn, and pandas--high-level packages that have many powerful features. Mark also introduces numpy and matplotlib--more foundational packages, 2) Mathematical Background - mathematical ideas: probability, linear combinations, and geometry. He approaches these concepts from a practical and computational viewpoint. He introduces them but shies away from theory, 3) Beginning Classification (Part I) - focuses on building, training, and evaluating simple classification models. He starts by introducing you to a practice dataset. It also covers train-test splits, accuracy, and two models: k-nearest neighbors and naive Bayes, 4) Beginning Classification (Part II) - two ways to evaluate classifiers. He shows you how to evaluate learning performance with accuracy and how to evaluate resource utilization for memory and time, 5) Beginning Regression (Part I) - demonstrates building, training, and basic evaluation of simple regression models. He starts with a practice dataset. It discusses different ways of measuring the center of numerical data, and then he discusses two models: k-nearest neighbors and linear regression, and 6) Beginning Regression (Part II) - how to select good models from a basket of possible models. Then, it covers how to evaluate learning and resource consumption of regressors in notebook and standalone scenarios.

Enroll today (teams & executives are welcome): https://tinyurl.com/ya5rrrpf 

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” (Kindle) or (Audible - coming soon!)

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

AI and Machine Learning (Masters Program)

Colleagues, the AI and Machine Learning (Masters Program) is developed by industry professionals, imparts a deep understanding of principles and practices. The average salary for a Machine Learning Engineer is $136,047+ per year in the US. With an intensive curriculum and hands-on projects, participants gain experience in model design, AI/ML solutions, feature engineering, big data handling, and data-driven decision-making. Acquire skills to develop cutting-edge solutions tailored to organizational needs. Skill-based training lessons include: 1) Python Programming Certification - this Python Bootcamp Course will help you master Python programming concepts such as Sequences and File Operations, Conditional statements, Functions, Loops, OOPs, Modules and Handling Exceptions, various libraries such as NumPy, Pandas, Matplotlib, and also focuses on GUI Programming, Web Maps, Data Operations in python and more. Throughout this online Python Course, you will be working on real-time projects and this course will prepare you to clear PCEP, PCAP and PCPP Professional Certification Exams to become a certified programmer; 2) Data Science with Python Certification is accredited by NASSCOM - it will help you master important Python programming concepts such as data operations, file operations, object-oriented programming, and various Python libraries such as Pandas, Numpy, and Matplotlib essential for Data Science. This course is well-suited for professionals and beginners. This training will introduce you to different types of Machine Learning, Recommendation Systems; 3) Artificial Intelligence Certification - you will master essentials of text processing and classifying texts along with important concepts such as Tokenization, Stemming, Lemmatization, POS tagging and many more. You will learn to perform image pre-processing, image classification, transfer learning, object detection, computer vision and also be able implement popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. This course is curated by the industry experts after extensive research to meet the latest industry requirements and demands; 4) ChatGPT Complete Course: Beginners to Advanced - will empower you to engage with the latest in generative AI – ChatGPT. Enhance prompt engineering, integrate plugins, and leverage ChatGPT APIs for increased efficiency. Build a personalized chatbot, drawing insights from real-life applications and projects. Gain a glimpse into the future with GPT-4 and ChatGPT Plus; and 5) PySpark Course Online Training - teaches you how to leverage Python's functionality as you deploy it in the Spark ecosystem.Our online training is instructor-led & enables you to master key PySpark concepts with hands-on demonstrations. Enroll now with this course to learn from top-rated instructors. Electives include: 1) Python Scripting Certification Training, 2) Reinforcement Learning, 3) Graphical Models Certification, and 4) Sequence Learning Certification Training.

Enroll today (teams & executives are welcome): https://tinyurl.com/63huj9fc 

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” (Kindle) or (Audible - coming soon!)

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

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

Colleagues, the Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs) training includes a quick-start guide for the use and launch of 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 GPT, 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 how to: Launch an application using proprietary models with an example of an information retrieval system with OpenAI embeddings and GPT for, Question/Answering, Fine-tune GPT with custom examples using their API to get better results, Learn the basics of prompt engineering with GPT to get more nuanced examples by building a chatbot with persona style depending on who they are talking to using the information retrieval system, 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, 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/2s3prbuv 

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)

Artificial Intelligence Certification

Colleagues, this Artificial Intelligence Certification program helps you master essentials of text processing and classifying texts along with important concepts such as Tokenization, Stemming, Lemmatization, POS tagging and many more. You will learn to perform image pre-processing, image classification, transfer learning, object detection, computer vision and also be able implement popular algorithms like CNN, RCNN, RNN, LSTM, RBM using the latest TensorFlow 2.0 package in Python. This course is curated by the industry experts after extensive research to meet the latest industry requirements and demands. Gain an understanding of Text Mining, Natural Language Processing (NLP) in Text Mining, Applications of Text Mining, OS Module, Reading, Writing to text and word files, Setting the NLTK Environment, and Extracting, Cleaning and Preprocessing Text. Skill-based training modules include: 1) Analyzing Sentence Structure, 2) Text Classification-I, 3) Introduction to Deep Learning, 4) Getting Started with TensorFlow 2.0, 5) Convolution Neural Networks, 6) Regional CNN, 7) Boltzmann Machine & Autoencoder, 8) Generative Adversarial Networks (GAN), 9) LSTM, 10) Auto Image Captioning Using CNN LSTM, 11) Developing a Criminal Identification and Detection Application Using OpenCV, 12) TensorFlow for Deployment, 13) Text Classification-II, and 14) In Class Projects: Converting text to features and labels, Multinomial Naive Bayes Classifier, Leveraging Confusion Matrix, Converting text to features and labels, Multinomial Naive Bayes Classifier, and Leveraging Confusion Matrix. 

Enroll today (teams & executives are welcome): https://fxo.co/HPFx 

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” (Kindle) or (Audible - coming soon!)

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

AI Software Engineer: ChatGPT, Bard and Beyond (audio & ebook)

Colleagues, the purpose of the “AI Software Engineer: ChatGPT, Bard and Beyond” (Interview Prodigy series) help software engineers and developers capture their ideal job offer and manage their medium-to-long-term career growth in the global artificial intelligence arena. We will focus on artificial intelligence software engineers and developers in this series. Artificial intelligence has proven to be a revolutionary part of the digital era. As a result, top tech giants like Amazon, Google, Apple, Facebook, Microsoft, and International Business Machines Corporation have been investing significantly in the research and development of artificial intelligence. As a result, these companies are contributing well to making A.I. more accessible for businesses. In addition, different companies have adopted A.I. technology for improved customer experience. For example, in March 2020, McDonald's invested $300 million to acquire an A.I. startup in Tel Aviv to provide a personalized experience for its customers using artificial intelligence. This was its most significant tech investment.

AI Engineers have many opportunities, which will only grow with time. After reading this book, I hope you can identify your ideal job offer and manage your short- and long-term career growth plan, especially in artificial intelligence. The world of artificial intelligence is vast. As I stated earlier in this book, it has a current market size of $136.55 billion based on a 2022 report by CAGR, and it will likely reach a growth rate of 37.3% from 2023 to 2030. You can study artificial intelligence from three aspects. First, the narrow artificial intelligence: this is where you learn about strong AI, artificial general intelligence, and narrow artificial intelligence, also known as weak AI. As I mentioned earlier in this book, the tech we use daily is known as narrow artificial intelligence mainly because it focuses on one narrow task. An example is a chess computer, Siri, or Alexa. Artificial narrow intelligence generally operates within a limited predetermined range. Then there is machine learning, an application that allows systems and computers to learn and improve without being programmed. This idea aims to enable systems to learn and adapt automatically without human involvement or support. Deep learning enables inventors to enhance technology such as self-driving vehicles, speech recognition, and facial identification.

Order today: 

(Audible) https://tinyurl.com/mae9ku3b

 (Kindle) https://tinyurl.com/27jux34w 

Transformative Innovation (book series):

 

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) 

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

Explore the “Data Driven Organizations” Amazon Audible & Kindle Book Series

Data-Driven Organizations: 

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

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

Much success. Order today, Genesys Digital (Amazon Author Page) https://tinyurl.com/hh7bf4m9 

Deep Learning: From Perceptron to Large Language Models (training)

Colleagues, the Deep Learning: From Perceptron to Large Language Models training program will introduce you to the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers. It describes how these concepts are used to build modern networks for computer vision and natural language processing (NLP), including large language models and multimodal networks. Skill-based training modules address: 1) Deep Learning Introduction - Deep Learning and Its History, Prerequisites, 2) Neural Network Fundamentals - The Perceptron and Its Learning Algorithm, Programming Example: Perceptron, Understanding the Bias Term, Matrix and Vector Notation for Neural Networks, Perceptron Limitations, Solving Learning Problem with Gradient Descent,  Computing Gradient with the Chain Rule, The Backpropagation Algorithm, Programming Example: Learning the XOR Function, What Activation Function to Use, 3) Neural Network Fundamentals II - Datasets and Generalization, Multiclass Classification, Programming Example: Digit Classification with Python, DL Frameworks, Programming Example: Digit Classification with TensorFlow, Programming Example: Digit Classification with PyTorch, Avoiding Saturated Neurons and Vanishing Gradients - Part I, Avoiding Saturated Neurons and Vanishing Gradients - Part II, Variations on Gradient Descent, Programming Example: Improved Digit Classification with TensorFlow, Programming Example: Improved Digit Classification with PyTorch, Problem Types, Output Units, and Loss Functions, Regularization Techniques, Programming Example: Regression Problem with TensorFlow, Programming Example: Regression Problem with PyTorch, 4) Convolutional Neural Networks (CNN) and Image Classification - The CIFAR-10 Dataset, Convolutional Layer, Building a Convolutional Neural Network, Programming Example: Image Classification Using CNN with TensorFlow & PyTorch, AlexNet, VGGNet, GoogLeNet, ResNet, Amplifying Your Data, Efficient CNNs, 5) Recurrent Neural Networks (RNN) and Time Series Prediction - Problem Types Involving Sequential Data, Recurrent Neural Networks, Programming Example: Forecasting Book Sales with TensorFlow & PyTorch, Backpropagation Through Time and Keeping Gradients Healthy, Long Short-Term Memory, Autoregression and Beam Search, Programming Example: Text Autocompletion with TensorFlow & PyTorch, 6) Neural Language Models and Word Embeddings - Language Models, Word Embeddings, Programming Example: Language Model and Word Embeddings with TensorFlow & PyTorch, Word2vec, Programming Example: Using Pre Trained GloVe Embeddings, Handling Out-of-Vocabulary Words with Wordpieces, 7) Encoder-Decoder Networks, Attention, Transformers, and Neural Machine Translation - EncoderDecoder Network for Neural Machine Translation, Programming Example: Neural Machine Translation with TensorFlow & PyTorch, Attention, The Transformer, 8) Large Language Models - Overview of BERT & GPT, From GPT to GPT4, Handling Chat History, Prompt Tuning, Retrieving Data and Using Tools, Open Datasets and Models, Demo: Large Language Model Prompting, 9) Multimodal Networks and Image Captioning - Multimodal learning, Programming Example: Multimodal Classification with TensorFlow & PyTorch, Programming Example: Multimodal Classification with PyTorch, Image Captioning with Attention, Programming Example: Image Captioning with TensorFlow & PyTorch, , Multimodal Large Language Models, 10) Multi-Task Learning and Computer Vision Beyond Classification - Multitask Learning, Programming Example: Multitask Learning with TensorFlow & PyTorch, Programming Example: Multitask Learning with PyTorch, Object Detection with R-CNN, Improved Object Detection with Fast and Faster R-CNN, Segmentation with Deconvolution Network and U-Net, Instance Segmentation with Mask R-CNN, and 11) Applying Deep Learning - Ethical AI and Data Ethics, Process for Tuning a Network. 

Enroll today (teams & executives are welcome): https://tinyurl.com/yckrv8xj 

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” (Kindle) or (Audible - coming soon!)

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