” 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.
Certification & Training - Artificial Intelligence
Artificial Intelligence Essentials: AI-based Chatbots: AI Based Chatbots is an alternative way to communicate online. This course will teach you what AI Chatbots are by understanding how they differ from regular chatbots and what are good use cases You have probably come across chatbots when browsing online. You might notice some are a bit more equipped than others. Some of these well-equipped ones might have been an AI-based chatbot. In this course, Artificial Intelligence Essentials: AI-based Chatbots, you will gain the ability to Evaluate AI-based chatbots. First, you will explore what the foundation of AI-based chatbots are and how they perform. Next, you will discover how AI-based chatbots can be used in modern day products by going through different use cases of AI-based chatbots. Finally, you will learn what applications are available to create an AI-based chatbot. By the end of this course, you will have the skills and knowledge of AI-based chatbots and how to use them in your own environment. Training modules include: 1) Evaluating AI Based Chatbots - What Is a Chatbot? Types of Chatbots, Examples of AI Based Chatbots, 2) Understanding AI Based Chatbots - Components of an AI Chatbot, What Is Natural Language Processing?, What Is a Knowledge Base?, Understanding Intent, 3) Describing Applications of AI Based Chatbots - Industry Use of AI Chatbots, Demo: Retail Chatbot, 4) Using Tools to Create an AI based Chatbots - Tools to Built an AI Chatbot, SDK and API for AI Chatbots, Demo: Using ChatGPT with OpenAI API via Code, AI Chatbot Builders, Demo: AI Chatbot with Azure Cognitive Question Answering. {Pluralsight}
AI Programming with Python: Learn Python, NumPy, Pandas, Matplotlib, PyTorch, Calculus, and Linear Algebra—the foundations for building your own neural network. 1) Introduction to Python: Start coding 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 Learn 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). {Udacity}
AI Workflow: Enterprise Model Deployment: This course introduces you to an area that few data scientists are able to experience: Deploying models for use in large enterprises. Apache Spark is a very commonly used framework for running machine learning models. Best practices for using Spark will be covered in this course. Best practices for data manipulation, model training, and model tuning will also be covered. The use case will call for the creation and deployment of a recommender system. The course wraps up with an introduction to model deployment technologies. By the end of this course you will be able to: 1. Use Apache Spark's RDDs, dataframes, and a pipeline 2. Employ spark-submit scripts to interface with Spark environments 3. Explain how collaborative filtering and content-based filtering work 4. Build a data ingestion pipeline using Apache Spark and Apache Spark streaming 5. Analyze hyperparameters in machine learning models on Apache Spark 6. Deploy machine learning algorithms using the Apache Spark machine learning interface 7. Deploy a machine learning model from Watson Studio to Watson Machine Learning Who should take this course? This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses. What skills should you have? It is assumed that you have completed Courses 1 through 4 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process. {Coursera}
Artificial Intelligence: Learn to write programs using the foundational AI algorithms powering everything from NASA’s Mars Rover to DeepMind’s AlphaGo Zero. This program will teach you classical AI algorithms applied to common problem types. You’ll master Bayes Networks and Hidden Markov Models. Training modules with hands-on projects include: 1) Introduction to Artificial Intelligence - configure your programming environment to work on AI problems with Python. At the end of the course you'll build a Sudoku solver and solve constraint satisfaction problems (Project: Build a Sudoku Solver), 2) Classical Search - learn classical graph search algorithms--including uninformed search techniques like breadth-first and depth-first search and informed search with heuristics including A*. These algorithms are at the heart of many classical AI techniques, and have been used for planning, optimization, problem solving, and more. Complete the lesson by teaching PacMan to search with these techniques to solve increasingly complex domains, 3) Automated Planning - represent general problem domains with symbolic logic and use search to find optimal plans for achieving your agent’s goals. Planning & scheduling systems power modern automation & logistics operations, and aerospace applications like the Hubble telescope & NASA Mars rovers (Project: Build a Forward Planning Agent), 4) Optimization Problems - iterative improvement optimization problems and classical algorithms emphasizing gradient-free methods for solving them. These techniques can often be used on intractable problems to find solutions that are "good enough" for practical purposes, and have been used extensively in fields like Operations Research & logistics. You’ll finish the lesson by completing a classroom exercise comparing the different algorithms' performance on a variety of problems., 5) Adversarial Search - search in multi-agent environments (including decision making in competitive environments) using the minimax theorem from game theory. Then build an agent that can play games better than any humans (Project: Build an Adversarial Game Playing Agent), and 6) Fundamentals of Probabilistic Graphical Models - use Bayes Nets to represent complex probability distributions, and algorithms for sampling from those distributions. Then learn the algorithms used to train, predict, and evaluate Hidden Markov Models for pattern recognition. HMMs have been used for gesture recognition in computer vision, gene sequence identification in bioinformatics, speech generation & part of speech tagging in NLP (Project: Part of Speech Tagging). {Udacity}
Artificial Intelligence and Machine Learning: The Python Statistics for Data Science course is designed to provide learners with a comprehensive understanding of how to perform statistical analysis and make data-driven decisions. Skill-based training modules include: 1) Python Training Course online is created by experienced professionals to Data Science with Python Certification Course, 2) Edureka's Data Science with Python Certification Course is accredited by NASSCOM, aligns with industry standards, and is approved by the Government of India, 3) Artificial Intelligence Certification Course helps you master essentials of text processing and classifying texts along with important concepts such as Tokenization, Stemming, 4) ChatGPT-4 Complete Course: Beginners to Advanced - Advance your AI knowledge and expertise with Edureka’s comprehensive ChatGPT certification training program. This comprehensive training covers the fundamentals of ChatGPT, 5) PySpark Certification Training Course - curated by top industry experts to help you master skills that are required to become a successful Spark developer using Python. {Edureka}
Artificial Intelligence for Trading: Program involves real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio. Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization. Training modules - each with a hands-on project - include: 1) Basic Quantitative Trading (Project: Trading with Momentum); 2) Advanced Quantitative Trading (Project: Breakout Strategy); 3) Stocks, Indices,and ETFs (Project: Smart Beta and Portfolio Optimization); 4) Factor Investing and Alpha Research (Project: Alpha Research and Factor Modeling); 5) Sentiment Analysis with Natural Language Processing (Project: Sentiment Analysis Using NLP); 6) Advanced Natural Language Processing with Deep Learning (Project: Deep Neural Network with News Data); 7) Combining Multiple Signals (Project: Combine Signals for Enhanced Alpha); and 8) Simulating Trades with Historical Data (Project: Backtesting). (Udacity)
Artificial Intelligence using IBM Watson: Gain skills in Deep Learning, Application Programming Interfaces (API), Artificial Intelligence (AI), Machine Learning and IBM Watson. Training modules address: 1) Watson Overview, 2) Watson AI Services, 3) More Watson AI Services, and 4) Watson in Action. (IBM)
Building AI Applications on Google Cloud Platform: Cloud AutoML is a suite of machine learning products that allows developers with limited machine learning expertise to train high-quality models specific to their business needs. It relies on Google’s state-of-the-art transfer learning and neural architecture search technology. Developers use Cloud AutoML’s graphical user interface to train, evaluate, improve, and deploy models based on their data. This program covers programming components essential to the development of AI and Analytics applications. The focus is on building real-world software engineering applications on the Google Cloud Platform. Several emerging technologies are used to demonstrate the process, including AutoML and Google BigQuery. The Python language is used throughout the course, as Python is becoming the de facto standard language for AI application development in the cloud. Lesson modules address: 1) Create an Application Skeleton on GCP using Google App Engine, 2) Build ETL (Extract Transform Load) Pipelines, 3) Use ML Prediction on BigQuery, 4) Use AutoML, 5) Use AI Platform, 6) Build an Analytics Application from Scratch, and 7) Using Build Systems and Containers. {Pearson}
Certified Artificial Intelligence Practitioner (CAIP): Apply various approaches and algorithms to solve business problems through AI and ML, use algorithms to solve the two most common supervised problems regression and classification, and a common unsupervised problem: clustering. {CertNexus}
ChatGPT and Generative AI: The Big Picture: ChatGPT has taken the world by storm – but what exactly is it, and how can you take advantage of it? In this short introduction, learn the basics of generative AI, how it works, and how to apply ChatGPT to real-world problems. ChatGPT and generative AI are all the rage, and have the potential to transform how we work, learn, and interact. But how do you cut through all the hype? In this course, ChatGPT and Generative AI: The Big Picture, you’ll get a foundational understanding of these powerful new technologies. First, you'll explore generative AI and how it works to create human-like responses to questions. Next, you’ll discover ChatGPT and see several examples of its capabilities, as well as some limitations. Finally, you’ll learn how ChatGPT can be applied to real-world applications, including day-to-day use cases for data practitioners. When you’re finished with this course, you’ll have a practical understanding of ChatGPT and how you can use it to work smarter, not harder. Training modules address: 1) What Are ChatGPT and Generative AI?, 2) Getting Started with ChatGPT, 3) Applying ChatGPT to the Real World, and 4) ChatGPT Use Cases for Data Practitioners. {Pluralsight}
IBM Applied AI Professional Certificate: IBM Watson AI Services - machine learning, data science, natural language processing, image classification, image processing, IBM Watson AI services, OpenCV, APIs. {IBM}
Introduction to Artificial Intelligence with Python (HarvardX CS50): This program explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own. Enroll now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever, CS50. You’ll learn the theoretical frameworks that enable these new technologies while gaining practical experience in how to apply these powerful techniques in your work. Key topics include: graph search algorithms, adversarial search, knowledge representation, logical inference, probability theory, Bayesian networks, Markov models, constraint satisfaction, machine learning, reinforcement learning, neural networks, and natural language processing. {EDx}
Prompt Engineering for ChatGPT: ChatGPT and other large language models are going to be more important in your life and business than your smartphone, if you use them right. ChatGPT can tutor your child in math, generate a meal plan and recipes, write software applications for your business, help you improve your personal cybersecurity, and that is just in the first hour that you use it. This course will teach you how to be an expert user of these generative AI tools. The course will show amazing examples of how you can tap into these generative AI tools' emergent intelligence and reasoning, how you can use them to be more productive day to day, and give you insight into how they work. Large language models respond to instructions and questions posed by users in natural language statements, known as “prompts”. Although large language models will disrupt many fields, most users lack the skills to write effective prompts. Expert users, who understand how to write good prompts, are orders of magnitude more productive and can unlock significantly more creative uses for these tools. 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. {Coursera}
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)
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