Supervised Learning (training)

AI-ML Colleagues, the “Supervised Learning” program you will learn to apply a wide range of supervised-learning techniques — from simple linear regression to support vector machines (SVM). Machine learning” sounds intimidating, but in reality it is far more accessible than people think. This course is tailored for both students and professionals looking to improve their understanding of supervised machine learning methods (i.e. regression and classification techniques) so they can run their own predictive algorithms, as well as contribute meaningfully to other teams’ ML projects. In addition to working through a range of hands-on exercises, you’ll also apply what you’ve learned to predict potential donors for a fictional charity based on census data. Training modules & hands-on projects involve: 1) Regression - learn the difference between Regression and Classification, train a Linear Regression model to predict values, and learn to predict states using Logistic Regression, 2) Perceptron Algorithms - learn the definition of a perceptron as a building block for neural networks and the perceptron algorithm for classification, 3) Decision Trees - train Decision Trees to predict states and use Entropy to build decision trees, recursively, 4) Naive Bayes - learn the Bayes’ rule, and apply it to predict cases of spam messages using the Naive Bayes algorithm. Train models using Bayesian Learning and complete an exercise that uses Bayesian Learning for natural language processing, 5) Support Vector Machines - train a Support Vector Machines to separate data, linearly. Use Kernel Methods in order to train SVMs on data that is not linearly separable, 6) Ensemble of Learners - build professional presentations and data visualizations for quantitative and categorical data. Create pie, bar, line, scatter, histogram, and boxplot charts, 7) Evaluation Metrics - calculate accuracy, precision and recall to measure the performance of your models, and 8) Training and Tuning Models - train and test models with Scikit-learn. Choose the best model using evaluation techniques such as cross-validation and grid search. Course Project: “Find Donors for CharityML” - your goal will be to evaluate and optimize several different supervised learning algorithms to determine which algorithm will provide the highest donation yield while under some marketing constraints.

Enroll today (teams & executives are welcome): https://tinyurl.com/mryzxu45 

Download your free AI-ML-DL - Career Transformation Guide.

Listen to the ““ChatGPT - The Era of Generative Conversational AI Has Begun” audiobook on Audible (https://tinyurl.com/bdfrtyj2) or 

Read the ebook today on Kindle (https://tinyurl.com/jfntsyj2) 

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

Become a “Deep Learning” Pro

AI Colleagues, this “Deep Learning” program will equip you with leading-edge and high-demand skills to boost your career. Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Learn to leverage the capabilities of deep learning tools to fix complex problems and unlock next-level results for enterprises (4 months to complete).  Join the next generation of deep learning talent that will help define a highly beneficial AI-powered future for our world. In this program, you’ll study cutting-edge topics such as neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. Training modules and hands-on projects will cover: 1) Introduction to Deep Learning - grasp the fundamentals of deep learning. Then examine the foundational algorithms underpinning modern deep learning: gradient descent and backpropagation. Once those foundations are established, explore design constructs of neural networks and the impact of these design decisions. Finally, the course explores how neural network training can be optimized for accuracy and robustness (Project: Developing a Handwritten Digits Classifier with PyTorch), 2) Convolutional Neural Networks - the most widely used type of neural networks specialized in image processing. You will learn the main characteristics of CNNs that make them better than standard neural networks for image processing. Then you’ll examine the inner workings of CNNs and apply the architectures to custom datasets using transfer learning. Finally, you will learn how to use CNNs for object detection and semantic segmentation (Projects: Landmark Classification and Tagging for Social Media), 3) RNNs & Transformers - learn multiple RNN architectures and design patterns for those models. Additionally, you’ll focus on the latest transformer architectures (Project: Text Translation and Sentiment Analysis using Transformers), 3) Building Generative Adversarial Networks - build your expertise with generative adversarial networks (GANs) by learning how to build and train different GANs architectures to generate new images. Discover, build, and train architectures such as DCGAN, CycleGAN, ProGAN, and StyleGAN on diverse datasets including the MNIST dataset, Summer2Winter Yosemite dataset, or CelebA dataset (Project: Facial Generation). This program includes real-world projects from industry experts, real-time support, career services and a flexible learning program

Enroll today (teams & executives are welcome): https://tinyurl.com/6xz839fk 

Download your free AI-ML-DL - Career Transformation Guide.

Listen to the ““ChatGPT - The Era of Generative Conversational AI Has Begun” audiobook on Audible. (https://tinyurl.com/bdfrtyj2) or 

Read the ebook today on Amazon Kindle (https://tinyurl.com/jfntsyj2) 

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

“ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” (new ebook on Amazon Kindle)

Friends, the new “ChatGPT, Gemini and Llama - The Journey from AI to AGI, ASI and Singularity” ebook is the latest entry to the “Transformative Innovation” series. AI in all of its manifestations represents a bona fide “generational change” of human society as we know it. AI, like many of the historic innovations of the past, has the unsurpassed potential to impact the human race for both good and evil.  All humanity has a vested interest in ensuring the impact of AI is positive. The alternative is unfathomable: A dystopian environment leading to the destruction of mankind in a Noahic antediluvian episode of human culture by the hands of man himself or the divine, omnipotent hand of our sovereign Creator.

Our journey will examine the birth of AI, the pending transition to Artificial General Intelligence, the era of Artificial Super Intelligence and thoughts on the possibility of AI (or “Technological”) Singularity. Although some of these topics have verifiable, concrete answers, overall, the pendulum rapidly swings from the domain of the known to the domain of the unknown and speculative in the concluding chapters. Our commitment is to amplify the known (factual) elements of Artificial Intelligence and only when necessary delve into the realm of the unknown (subjective) aspects of AI Singularity … and beyond.

Join us for a journey that will transform your thinking and possibly your life!

Kindle (https://www.amazon.com/dp/B0CG536WDN)   

Audible (coming soon!) 

3 Book Series: “Transformative Innovation”

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

AI Programming with Python (training)

AI Colleagues, the “AI Programming with Python” program will equip you in Python, NumPy, Pandas, Matplotlib, PyTorch, Calculus, and Linear Algebra;the foundations for building your own neural network. Understand the essential foundations of AI: the programming tools (Python, NumPy, PyTorch), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation). Formal prerequisites include basic knowledge of algebra, and basic programming in any language. Skill-based training modules include: 1) Introduction to Python - code with Python, drawing upon libraries and automation scripts to solve complex problems quickly (Project: Use a Pre-trained Image Classifier to Identify Dog Breeds), 2) Jupyter Notebooks, NumPy, Anaconda, pandas, and Matplotlib - learn how to use all the key tools for working with data in Python: Jupyter Notebooks, NumPy, Anaconda, pandas, and Matplotlib, 3) Linear Algebra Essentials - learn the foundational linear algebra you need for AI success: vectors, linear transformations, and matrices—as well as the linear algebra behind neural networks, 4) Calculus Essentials - master the foundations of calculus to understand how to train a neural network: plotting, derivatives, the chain rule, and more. See how these mathematical skills visually come to life with a neural network example, and 5) Neural Networks - gain a solid foundation in the hottest fields in AI: neural networks, deep learning, and PyTorch (Project: Create Your Own Image Classifier).

Enroll today (teams & executives are welcome): https://tinyurl.com/547yrs8f 

Download your free AI-ML-DL - Career Transformation Guide.

Listen to the ChatGPT audiobook on Audible. (https://tinyurl.com/bdfrtyj2) or read the ebook today on Amazon Kindle. (https://tinyurl.com/jfntsyj2) 

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

Artificial Intelligence Certification Program

AI Colleagues, in the advanced “Artificial Intelligence Certification” program you will master the essentials of text processing and classifying texts along with important concepts of Tokenization, Stemming, Lemmatization, POS tagging, Named Entity Recognition, Syntax Tree Parsing using Python’s NLTK package, CNN, RCNN, RNN, LSTM, RBM, and their implementation using TensorFlow 2.0 package. You will learn to build real-time projects on NLP and Deep Learning, to make you industry-ready and help you to kickstart your career in this domain. Training modules address: 1) Text Mining and NLPExtracting, 2) Cleaning and Preprocessing Text, 3) Analyzing Sentence Structure, 4) Text Classification, 5) Deep Learning, 6) TensorFlow 2.0, 7) Convolution Neural Networks, 8) Regional CNN, 9) Boltzmann Machine & Autoencoder, 10) Generative Adversarial Network(GAN), 11) Emotion and Gender Detection (Self-paced), 12) RNN and GRU, 13) LSTM, 14) Auto Image Captioning Using CNN LSTM, and 15) TensorFlow for Deployment. Plus an in-class hands-on project.

Enroll today (teams & execs welcome): https://tinyurl.com/4zmhbk3b

Download your free AI-ML-DL - Career Transformation Guide (2022 v2).

Much career success, Lawrence E. Wilson - AI Academy (share & subscribe) 

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

Colleagues, the purpose of the “AI Software Engineer: ChatGPT, Bard and Beyond” (Interview Prodigy book 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.

Audible (https://tinyurl.com/28pjupkb)  (Audible)

Kindle eBook: (https://tinyurl.com/2juy37n4)  (Kindle) 

Series: “The Interview Prodigy”

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

Become a Natural Language Processing Expert (training)

AI Colleagues, in this “Natural Language Processing” program you will master the skills to get computers to understand, process, and manipulate human language. Build models on real data, and get hands-on experience with sentiment analysis and machine translation. Learn cutting-edge natural language processing techniques to process speech and analyze text. Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to teach the computer to do tasks such as speech recognition, machine translation. The three training modules - each with a hands-on project, include: 1) Introduction to Natural Language Processing - text processing fundamentals, including stemming and lemmatization. Explore machine learning methods in sentiment analysis. Build a speech tagging model (Project: Part of Speech Tagging - Use several techniques, including table lookups, n-grams, and hidden Markov models, to tag parts of speech in sentences, and compare their performance), 2) Computing with Natural Language - use advanced techniques like word embeddings, deep learning attention, and more. Build a machine translation model using recurrent neural network architectures (Project: Machine Translation - Build a deep neural network that functions as part of an end-to-end machine translation pipeline. Your completed pipeline will accept English text as input and return the French translation. You’ll be able to explore several recurrent neural network architectures and compare their performance), and 3) Communicating with Natural Language - learn voice user interface techniques that turn speech into text and vice versa. Build a speech recognition model using deep neural networks (Project: Speech Recognizer - Build a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR) pipeline. The model will convert raw audio into feature representations, which will then turn them into transcribed text). 

Enroll today (teams & execs are welcome): https://tinyurl.com/mvyv33n5 

Download your free AI-ML-DL - Career Transformation Guide.

Access the our new book “ChatGPT” on Amazon: 

Audible. (https://tinyurl.com/bdfrtyj2) or

Kindle (https://tinyurl.com/4pmh669p)

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

Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide (2023 V1) (Week #6 - article series: “Certification & Training - Deep Learning”)

Colleagues, our excerpt this week (#6) examines “Certification & Training - Deep Learning” in the global AI arena. The new Artificial Intelligence-Machine Learning-Deep Learning - Career Transformation Guide includes valuable information that enables you to accelerate your career growth and income potential - Career opportunities, Salaries (demand and growth), Certifications and Training programs, Publications and Portals along with Professional Forums and Communities.

Deep Learning

Build Basic Generative Adversarial Networks (GANs): In this course, you will: - Learn about GANs and their applications - Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of generating examples from determined categories The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research. {DeepLearning.AI}

Deep Learning with TensorFlow & PyTorch: Deep Learning and Artificial Intelligence, TensorFlow Playground, weight initialization, unstable gradients, batch normalization, Convolutional Neural Networks, Keras, PyTorch. {Inform IT}

Deep Learning with Tensorflow, Keras and PyTorch: Intuitive, application-focused introduction to deep learning and TensorFlow, Keras, and PyTorch Overview Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons is an introduction to deep learning that brings the revolutionary machine-learning approach to life with interactive demos from the most popular deep learning library, TensorFlow, and its high-level API, Keras, as well as the hot new library PyTorch. (InformIT)

Image Recognition with a Convolutional Neural Network: Image recognition is used in a wide variety of ways in our daily lives. This course will teach you how to tune and implement convolutional neural networks in order to implement image recognition and classification on a business case. In this course, Implement Image Recognition with a Convolutional Neural Network, you’ll understand how to implement image recognition and classification on your very own dataset. First, you’ll be introduced to the problem and dataset. Then, you’ll learn how to explore and prepare the dataset for the next step. Next, you’ll see how to build, train, and test a neural network on the dataset. Finally, you’ll explore how image augmentation and transfer learning help to lift the performance metrics involved in your solution. When you’re finished with this course, you’ll have the knowledge required to implement image recognition on any dataset of your choice. Training modules address: 1) Exploring and Preparing a Dataset for Image Recognition - What Are We Trying to Solve? Demos: Setting up Your Environment, Organizing the Dataset, Exploring the Dataset, Preprocessing and Preparing the Dataset, 2) Training a Convolutional Neural Network to Classify Images - What Are Convolutional Neural Networks? CNN: Convolutions, Activation, Pooling, Classification, Creating the CNN Architecture, Training the Model, Performance Metrics – How Well Did Your Model Do?, 3) Improving Performance of the Convolutional Neural Network - Better Performance – When and How?, Procuring Additional Training Data – Image Augmentation, Hyperparameter Tuning, Overfitting and Underfitting, Demo: Image Augmentation and Hyperparameter Tuning,  What Is Transfer Learning?, and  Transfer Learning – When and How?, and  Demo: Improving Performance through Transfer Learning. {Pluralsight}

Introduction to Deep Learning: Demystify the models that underpin the recent AI revolution and provide a solid foundation for further learning. Skill-based training modules include: 1) Fundamentals, 2) Perceptron: Weights, Biases, Activation Functions, 3) Multi-neuron Networks: XOR and nonlinearity, and 4)  Learning: Gradient Descent. After taking this course you will understand What deep learning is and how it  differs from other types of machine learning and artificial intelligence, How deep learning models use neural networks to make computations, What types of problems deep learning models can be used to solve, Types of data needed to train deep learning models, Variety of inputs deep  learning models receive and solutions they produce, Advantages that deep learning can offer over traditional machine learning, Why multi-neuron networks are able to solve complex problems, How neural networks use gradient descent and back-propagation to learn to make predictions. (Experfy)

Machine Vision, GANs, and Deep Reinforcement Learning: Modern machine vision involves automated systems outperforming humans on image recognition, object detection, and image segmentation tasks. Generative Adversarial Networks cast two Deep Learning networks against each other in a “forger-detective” relationship, enabling the fabrication of stunning, photorealistic images with flexible, user-specifiable elements. Deep Reinforcement Learning has produced equally surprising advances, including the bulk of the most widely-publicized “artificial intelligence” breakthroughs. Deep RL involves training an “agent” to become adept in given “environments,” enabling algorithms to meet or surpass human-level performance on a diverse range of complex challenges, including Atari video games, the board game Go, and subtle hand-manipulation tasks. Throughout these lessons, essential theory is brought to life with intuitive explanations and interactive, hands-on Jupyter notebook demos. Examples feature Python and straightforward Keras layers in TensorFlow 2, the most popular Deep Learning library. (InformIT)

Neural Networks and Deep Learning: Study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. {DeepLearning.AI}

Probabilistic Deep Learning with TensorFlow 2: This is an increasingly important area of deep learning that aims to quantify the noise and uncertainty that is often present in real world datasets. This is a crucial aspect when using deep learning models in applications such as autonomous vehicles or medical diagnoses; we need the model to know what it doesn't know. You will learn how to develop probabilistic models with TensorFlow, making particular use of the TensorFlow Probability library, which is designed to make it easy to combine probabilistic models with deep learning. As such, this course can also be viewed as an introduction to the TensorFlow Probability library. You will learn how probability distributions can be represented and incorporated into deep learning models in TensorFlow, including Bayesian neural networks, normalizing flows and variational autoencoders. You will learn how to develop models for uncertainty quantification, as well as generative models that can create new samples similar to those in the dataset, such as images of celebrity faces. You will put concepts that you learn about into practice straight away in practical, hands-on coding tutorials, which you will be guided through by a graduate teaching assistant. In addition there is a series of automatically graded programming assignments for you to consolidate your skills. At the end of the course, you will bring many of the concepts together in a Capstone Project, where you will develop a variational autoencoder algorithm to produce a generative model of a synthetic image dataset that you will create yourself. This course follows on from the previous two courses in the specialization, Getting Started with TensorFlow 2 and Customizing Your Models with TensorFlow 2. The additional prerequisite knowledge required in order to be successful in this course is a solid foundation in probability and statistics. In particular, it is assumed that you are familiar with standard probability distributions, probability density functions, and concepts such as maximum likelihood estimation, change of variables formula for random variables, and the evidence lower bound (ELBO) used in variational inference. {Imperial College London}

Apply Generative Adversarial Networks (GANs): Explore the applications of GANs and examine them wrt data augmentation, privacy, and anonymity - Leverage the image-to-image translation framework and identify applications to modalities beyond images - Implement Pix2Pix, a paired image-to-image translation GAN, to adapt satellite images into map routes (and vice versa) - Compare paired image-to-image translation to unpaired image-to-image translation and identify how their key difference necessitates different GAN architectures - Implement CycleGAN, an unpaired image-to-image translation model, to adapt horses to zebras (and vice versa) with two GANs in one The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more. Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.

Generative Deep Learning with TensorFlow: Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image. b) Build simple AutoEncoders on the familiar MNIST dataset, and more complex deep and convolutional architectures on the Fashion MNIST dataset, understand the difference in results of the DNN and CNN AutoEncoder models, identify ways to de-noise noisy images, and build a CNN AutoEncoder using TensorFlow to output a clean image from a noisy one. c) Explore Variational AutoEncoders (VAEs) to generate entirely new data, and generate anime faces to compare them against reference images. d) Learn about GANs; their invention, properties, architecture, and how they vary from VAEs, understand the function of the generator and the discriminator within the model, the concept of 2 training phases and the role of introduced noise, and build your own GAN that can generate faces. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. (DeepLearning.AI}

Recommended Reading: 

“AI Software Engineer” - The Interview Prodigy book series (Audible) (Kindle) 

Download your free AI-ML-DL - Career Transformation Guide (2023 v1). [https://lnkd.in/gZNSGaEM]

New audio & ebook: “ChatGPT - The Era of Generative Conversational AI Has Begun” (Audible) (Kindle) 

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