Deep Learning and Reinforcement Learning

Colleagues, in the “Deep Learning and Reinforcement Learning” training program you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few  Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future. After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. Gain high-demand skills involving Machine Learning Methods, Deep Learning, Unsupervised Learning, Artificial Neural Networks, Machine Learning, Convolutional Neural Networks, Artificial Intelligence, Image Analysis, Transfer Learning, Fine-tuning, Reinforcement Learning, Model Optimization, Computer Vision, Generative Model Architectures, and Recurrent Neural Networks (RNNs). You will obtain hands-on experience with Generative Adversarial Networks (GANs), Generative AI, Autoencoders, and Keras (Neural Network Library). Training modules include: Introduction to Neural Networks, 2) Back Propagation and Keras, 3) Neural Network Optimizers, 4) Convolutional Neural Networks, 5) Transfer Learning, 6) Neural Networks (RNNs) and Long-Short Term Memory Networks (LSTM), 7) Autoencoders, and 8) Generative Models and Applications of Deep Learning.

Enroll today (teams and execs are welcome): https://imp.i384100.net/RG3Ed7

Recommended Reading:

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) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Unsupervised Machine Learning: Unlocking the Potential of Data (MIT)

Colleagues, the “Unsupervised Machine Learning: Unlocking the Potential of Data” from the MIT Sloan School of Management is designed to help business decision makers leverage opportunities created by unsupervised ML. Understanding how to leverage uncurated data, managers and technology leads will be able to plan new data acquisition protocols. IT and tech professionals looking for up-to-date developments in unsupervised learning will benefit from this program material, as will data scientists and analysts looking to strengthen their knowledge of the application of computer vision technologies. Skills-based training modules include: 1) The potential of data, 2) Learning and leveraging representations, 3) Generative models, 4) AI building blocks, 5) Adapting AI tools, and 6) Challenges and the future. 

Enroll today (teams and execs are welcome): https://edx.sjv.io/L0R3xj 

Recommended Reading:

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) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Build AI Agents with GitHub Copilot (training)

Colleagues, the “Build AI Agents with GitHub Copilot” program is designed to help developers harness the power of GitHub Copilot to build AI agents more efficiently and effectively. By combining foundational knowledge of AI agents with hands-on practice, the course takes a practical, project-based approach--guiding learners from understanding core concepts and setting up their environment to building, testing, and refining real AI agents with Copilot's assistance. Along the way, participants will explore best practices, compare Copilot with other tools, and reflect on responsible AI development, ensuring they leave with both the technical skills and strategic insight to accelerate their productivity with AI-assisted coding. In this Beginner to Intermediate level course you will grasp the fundamentals of AI agents and their components, configure GitHub Copilot for your development environment, write, test, and refine AI agent code with Copilot's assistance, build a complete working AI agent step by step, extend AI agents with external APIs and libraries, use Copilot to generate, refactor, and improve code quality, compare Copilot with other AI coding tools for a broader perspective, and apply ethical and responsible practices in AI-assisted development. Skill-based training modules include: 1) Understand AI Agents and GitHub Copilot - Understand what an AI agent is, Explore key components of code-based AI agents, Recognize the role of GitHub Copilot in AI agent development, Compare GitHub Copilot with other AI coding tools, Identify use cases for AI agents with Copilot, 2) Set Up Your Development Environment - Install GitHub Copilot, Configure Copilot settings and preferences, Use advanced configurations, Set up an AI agent project, Write and run, 3) Build and Extend AI Agents with Copilot - Implement a simple rule-based agent, Make and execute decisions, Control agent behavior with generative AI, Create effective AI agent prompts, Integrate multiple components, and 4) Apply Best Practices and Next Steps - Make AI agent code reusable, Using structured outputs, and Learn best practices for creating safe agents.

Enroll today (teams and execs are welcome): https://tinyurl.com/yc6swmwu

Recommended Reading:

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) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Advanced Learning Algorithms (training)

Colleagues, in the “Advanced Learning Algorithms” program you will learn to build and train a neural network with TensorFlow to perform multi-class classification. Apply best practices for machine learning development so that your models generalize to data and tasks in the real world. And use decision trees and tree ensemble methods, including random forests and boosted trees. Gain highly marketable skills in Decision Tree Learning, Applied Machine Learning, Machine Learning Algorithms, Logistic Regression, Deep Learning, Model Evaluation, Model Training, Transfer Learning, Model Optimization,  Random Forest Algorithm, Data Ethics, Regression Analysis, Responsible AI, Supervised Learning, and Artificial Neural Networks. You will also acquire hands-on experience with Tensorflow and Classification Algorithms. 

Skill-based training modules address: 1) Neural Networks - learn about neural networks and how to use them for classification tasks. You'll use the TensorFlow framework to build a neural network with just a few lines of code. Then, dive deeper by learning how to code up your own neural network in Python, "from scratch". Optionally, you can learn more about how neural network computations are implemented efficiently using parallel processing (vectorization); 2) Neural Networks - you'll learn how to train your model in TensorFlow, and also learn about other important activation functions (besides the sigmoid function), and where to use each type in a neural network. You'll also learn how to go beyond binary classification to multiclass classification (3 or more categories). Multiclass classification will introduce you to a new activation function and a new loss function. Optionally, you can also learn about the difference between multiclass classification and multi-label classification. You'll learn about the Adam optimizer, and why it's an improvement upon regular gradient descent for neural network training, 3) Advice for Applying Machine Learning -  learn best practices for training and evaluating your learning algorithms to improve performance. This will cover a wide range of useful advice about the machine learning lifecycle, tuning your model, and also improving your training data, and 4) Decision Trees -  you'll learn about a practical and very commonly used learning algorithm the decision tree. You'll also learn about variations of the decision tree, including random forests and boosted trees (XGBoost). 

Enroll today (teams and execs are welcome): https://imp.i384100.net/POKMKj

Recommended Reading:

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) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Data Structures and Algorithms (training)

Colleagues, in the “Data Structures and Algorithms” program you will sharpen your problem-solving skills in this Nanodegree program. Practice over 100 algorithm and data structure challenges, learn Python-based techniques, and prepare for interviews with mentor guidance and real coding scenarios. Key learning modules include: 1) Data Structures, 2) Arrays and linked lists to trees and hash maps. Strengthen your coding logic, recursion skills, and ability to solve real programming - Recursive algorithmsPython treesPython arraysData structuresHash maps, 3) Basic Algorithms -  basic algorithms used in programming, Tree searchSorting algorithmsDivide and conquer algorithmsTree algorithms, 4) Advanced Algorithms - Graph algorithmsDepth-first searchGraph data structureA, 5) Search Algorithm - Python programming syntax and concepts, Breadth-first searchGreedy algorithmsDynamic programming, 6) Introduction to Python Programming, and 7) Basic Python.

Enroll today (teams and execs are welcome): https://tinyurl.com/3wxmn5k5

Recommended Reading:

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) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

IBM AI Engineering Professional Certificate

Colleagues, in the “IBM AI Engineering Professional Certificate” program you will learn to describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction. Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn. Deploy machine learning algorithms and pipelines on Apache Spark. Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow. Acquire high-demand skills in Computer Vision, Data Science, Fine-tuning, Generative AI Agents, Generative Model Architectures, Large Language Modeling, LLM Application, Machine Learning, Prompt Patterns, Retrieval-Augmented Generation, Supervised Learning, and Unsupervised Learning. You will also gain hands-on experience with Apache Spark, Generative AI, Keras (Neural Network Library), Prompt Engineering, PySpark, Python Programming, PyTorch (Machine Learning Library), and Vector Databases. Training modules cover: 1) Machine Learning with Python, 2) Introduction to Deep Learning & Neural Networks with Keras, 3) Deep Learning with Keras and Tensorflow, 4) Introduction to Neural Networks and PyTorch, 5) Deep Learning with PyTorch, 6) AI Capstone Project with Deep Learning, 7) Generative AI and LLMs: Architecture and Data Preparation, 8) Gen AI Foundational Models for NLP & Language Understanding, 9) Generative AI Language Modeling with Transformers, 10) Generative AI Engineering and Fine-Tuning Transformers, 11) Generative AI Advanced Fine-Tuning for LLMs, 12) Fundamentals of AI Agents Using RAG and LangChain, and 13) Project: Generative AI Applications with RAG and LangChain.

Enroll today (teams and executives are welcome): https://imp.i384100.net/nXRPy6

Recommended Reading:

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) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

NVIDIA AI Infrastructure and Operations Fundamentals

Colleagues, in the “NVIDIA AI Infrastructure and Operations Fundamentals” program you will explore diverse applications of AI across various industries, understand concepts like Machine Learning, Deep Learning, training and inference, and trace the evolution of AI Technologies. From its inception to the revolutionary advances brought by Generative AI, and the role of GPUs. Gain high-demand skills involving Cloud Infrastructure, Machine Learning, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Infrastructure Architecture, Large Language Modeling, MLOps (Machine Learning Operations), Information Technology Operations, Cloud Deployment and Management, Data Centers, Devops Tools, Network Infrastructure, AI literacy, Cloud Computing, and Data Infrastructure. You will learn Generative AI, AI Orchestration and Workflows. Skill-based training modules include: 1 - Introduction to AI - NVIDIA Training: AI, Machine Learning (ML), and Deep Learning (DL). Additionally, the course will introduce you to Generative AI, how Large Language Models (LLMs) work and new business opportunities being unlocked with this new technology. You will understand what a GPU is, distinguish the key differences between GPUs and CPUs, 2) AI Infrastructure: Multi-system AI clusters, such as the capabilities of NVIDIA GPUs and CPUs to address the requirements of AI workloads, storage, and networking considerations, energy efficient computing practices help data centers lower their carbon footprint, and how recommended design documents, or Reference Architectures (RAs), 3) AI Operations: Provisioning, managing, and monitoring AI infrastructure, and describe the value and tools for cluster management. Finally, you will learn the key differences and common tools used for orchestration, and 4) Course Completion Quiz. 

Enrol today (teams and executives are welcome): https://imp.i384100.net/B5brb4

Recommended Reading:

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) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Data Science: Building Machine Learning Models (HarvardX)

Colleagues, in the “Data Science: Building Machine Learning Models” program from HarvardX you will build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques. You will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. In addition, you will gain high-demand skills in training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

Enroll today (teams and executives are welcome): https://edx.sjv.io/GbGeGB

Recommended Reading:

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) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Colleagues, in the “Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization” program you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. Gain high-demand skills in Artificial Intelligence and Machine Learning (AI/ML), Performance Tuning, Model Optimization, Deep Learning, Machine Learning Methods, Artificial Neural Networks, Model Training, Debugging, Model Evaluation, Verification And Validation, and Applied Machine Learning.

Training modules include: 1) Practical Aspects of Deep Learning: Discover and experiment with a variety of different initialization methods, apply L2 regularization and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model, 2) Optimization Algorithms: Develop your deep learning toolbox by adding more advanced optimizations, random minibatching, and learning rate decay scheduling to speed up your models, and 3) HyperparameterTuning, Batch Normalization and Processing: Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily, then train a neural network on a TensorFlow dataset.

Enroll today (teams and executives are welcome): https://imp.i384100.net/LKZ95a 

Recommended Reading:

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) (Kindle)

3 - “The Upskill Gambit - Discover the 5 Keys to Your Career and Income Security in the Digital Age” (Audible) (Kindle)

Much success in your AI career, AI Academy (please subscribe and share with you colleagues)

Certified Agentic AI Developer™

Colleagues, in the “Certified Agentic AI Developer™” program you will unlock the power of Agentic AI in your career with the Certified Agentic AI Developer program. [14 modules · 56 lessons · 13 Hours] Agentic AI is transforming the way industries operate, offering advanced capabilities in automation, decision-making, and data-driven solutions. As businesses increasingly adopt this cutting-edge technology, the demand for skilled professionals who can develop and manage Agentic AI systems is skyrocketing. By becoming a Certified Agentic AI Developer, you position yourself at the forefront of this rapidly evolving field. With expertise in designing and deploying Agentic AI solutions, you’ll be equipped to drive innovation and efficiency across industries such as logistics, finance, healthcare, and beyond. Learn: AI Agents - Introduction, Agentic AI Paradigm, Agent Capabilities, Automation and Workflow Optimization, Frameworks, Post Deployment, AI Agents Security, Ethical Design of AI Agents, Technology Stack, and Use Cases.

Skill based training modules include: 1) AI Agents - Introduction, 2) Agentic AI Paradigm, 3) Agent Capabilities, 4) Automation and Workflow Optimization, 5) Frameworks, 6) Post Deployment, 7) AI Agents Security, 8) Ethical Design of AI Agents, 9) Technology Stack, 10) Use Cases, 11) Step-by-Step Building AI Agents, 12) Capstone Project, 13) Recommended Learning Methodology, and 14) Exam.

Enroll today - teams and executives are welcome: https://tinyurl.com/wcnrfv3k

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)