AI Software Engineer: ChatGPT, Bard and Beyond (Amazon - Audible & Kindle)

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 

 “Interview Prodigy” audio & ebook series on Amazon for your reading-listening pleasure (https://tinyurl.com/57ehhjb2). 

1 - AI Software Engineer: ChatGPT, Bard & Beyond (Audible) (Kindle) 

2 - JavaScript Full Stack Developer: Capture the Job Offer and Advance Your Career  (Audible) (Kindle)

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

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

Deep Learning Specialization

AI colleagues, in the “Deep Learning Specialization” from DeepLearning.AI you will master the fundamentals of deep learning and break into AI. Recently updated with cutting-edge techniques, would will gain highly marketable skills in Recurrent Neural Networks, Tensorflow, Convolutional Neural Networks, Artificial Neural Network and Transformers. You will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. In the Applied Learning Project you Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications, Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow, Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning, Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data, and Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering. Skill-based lessons address: 1) Neural Networks and Deep Learning, 2) Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization, 3) Structuring Machine Learning Projects, 4) Convolutional Neural Networks, and 5) Sequence Models.

Enroll today (teams & execs welcome): https://imp.i384100.net/EK3BjP 

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)

Programming for Data Science with Python (Nanodegree Program)

DS colleagues, in the “Programming for Data Science with Python - Nanodegree Program” you will learn programming skills needed to uncover patterns and insights in large data sets, running queries with relational databases and working with Unix shell and Git. Skill-based training modules include: 1) Introduction to SQL - Learn SQL language fundamentals such as building basic queries and advanced functions like Window Functions, Subqueries and Common Table Expressions. Shell Workshop - a powerful tool for developers of all sorts. In this lesson, you'll get a quick introduction to the very basics of using it on your own computer, 2) Introduction to Python - programming fundamentals such as data types and structures, variables, loops, and functions, Why Python Programming?, Data Types and Operators, data types and operators, built-in functions, type conversion, whitespace, and style guidelines, 3) Data Structures in Python - use data structures to order and group different data types together! Learn about the types of data structures in Python, along with more useful built-in functions and operators, 4) Control Flow - build logic into your code with control flow tools! Learn about conditional statements, repeating code with loops and useful built-in functions, and list comprehensions, 5) Functions - use functions to improve and reuse your code. Learn about functions, variable scope, documentation, lambda expressions, iterators, and generators, 6) Scripting - set up your own programming environment to write and run Python scripts locally. Learn good scripting practices, interact with different inputs, and discover awesome tools, 7) NumPy - learn the basics of NumPy and how to use it to create and manipulate arrays, 8) Pandas - learn the basics of Pandas Series and DataFrames and how to use them to load and process data, 9) Advanced Topics - iterators and generators. Project 1 - Explore US Bikeshare Data: Use Python to understand U.S. bikeshare data. Calculate statistics and build an interactive environment where a user chooses the data and filter for a dataset to analyze, 10) Introduction to Version Control - use version control to save and share your projects with others, 11) Create a Git Repo - learn how to create a repository, 12) Commits, Tags, Conflicts - review an existing Git repository's history of commits is extremely important, 13) Remotes and Developer Repos - learn how to fork another developer's project. Collaborating with other developers can be a tricky process, so you'll learn how to contribute to a public project, 14) Writing READMEs for Repos

Learn the importance of well documented code and see how to craft meaningful READMEs. Project 2: Post Your Work on GitHub - use your local git repository and your GitHub repository. Fork a repository, work on files, stage files and commit them to GitHub. You will also demonstrate how to hide files using .gitignore files.

Enroll today (teams & executives are welcome): https://imp.i115008.net/rQ7N6B 

Download your free Data Science  - Career Transformation Guide.

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

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

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

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

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

Data Engineering Foundations Part 2: Building Data Pipelines with Kafka and Nifi

Colleagues, the “Data Engineering Foundations Part 2: Building Data Pipelines with Kafka and NiFi” program introduces you to creating data pipelines at scale with Kafka and NiFi. You learn to work with the Kafka message broker and discover how to establish NiFi dataflow. You also learn about data movement and storage. All software used in videos is open source and freely available for your use and experimentation on the included virtual machine. Learn Kafka topics, brokers, and partitions, implement basic Kafka usage modes, Kafka producers and consumers with Python, KafkaEsque graphical user interface, core concepts of NiFi, NiFi flow and web UI components, direct data movement with HDFS, HBase with Python Happybase and Sqoop for database movement. Skill-based lessons address: 1) Working with the Kafka Message Broker - Kafka message broker concept and describes the producer-consumer model that enables input data to be reliably decoupled from output requests. Kafka producers and consumers are developed using Python, and internal broker operations are displayed using the Kafkaesque graphical user interface, 2) Working with NiFi Dataflow - Lesson 8 begins with a description of NiFi flow-based programming and then provides several examples that include writing pipeline data to the local file system, then to the Hadoop Distributed File System, and finally to Hadoop Hive tables. The entire flow process is constructed using the NiFi web Graphical User Interface. The creation of portable flow templates for all examples is also presented, 3) Big Data Movement and Storage - moving data to and from the Hadoop Distributed File System. Hands-on examples include direct web downloads and using Python Pydoop to move data. Basic data movement between Apache HBase, Hive, and Spark using Python Happybase and Hive-SQL. Finally, movement of relational data to and from the Hadoop Distributed File System is demonstrated using Apache Sqoop.

Enroll today (teams & executives are welcome): https://tinyurl.com/bdpz4vf 

Download your free Data Science  - Career Transformation Guide.

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

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

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

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

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

Implementing Data Science Methodology - From Data Wrangling to Data Viz and Everything in Between (Audible & Kindle Book)

Colleagues, the book entitled “Implementing Data Science Methodology … From Data Wrangling to Data Viz and Everything in Between” will enable you to lead data science initiatives within your organization. They allow organizations to make informed decisions based on data, which can lead to better results and increased competitiveness. Data-driven methodologies also help organizations to identify areas for improvement and to make more accurate predictions about future outcomes and improve the accuracy of decision-making. By analyzing large amounts of data, organizations can identify patterns and trends that would not be apparent otherwise. This allows them to make more informed decisions, reducing the risk of making decisions based on inaccurate or incomplete information. The implementation of data-driven methodologies is critical for organizations looking to remain competitive in today’s data-driven world. By leveraging data to make informed decisions, organizations can improve their performance, reduce risks, and provide better experiences for their customers. It is important for organizations to invest in the resources and processes needed to implement these methodologies effectively, and to stay up-to-date with the latest trends and technologies in data science. Key topics addressed include: 1) Data-Driven Methodologies, 2) The Role of the Data Scientist, 3) Stakeholders and Buy-in, 4) Key Concepts of Data-Driven Methodology, 5) Data-Driven Lifecycle, 6) Data-driven Roadmapping, 6) Planning and Implementing a Data-Driven Strategy, 7) Data-Driven Predictive Modeling, 8) Business Transformation, and 9) Examples of Data-Driven Organizations.

Access on Amazon today!  

(Audible) Listen

(Kindle) Read

And download your free Data Science - Career Transformation Guide.

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

Share Data Through the Art of Visualization (Google)

Colleagues, Share Data Through the Art of Visualization is the sixth course in the Google Data Analytics Certificate. You’ll learn how to visualize and present your data findings as you complete the data analysis process. This course will show you how data visualizations, such as visual dashboards, can help bring your data to life. You’ll also explore Tableau, a data visualization platform that will help you create effective visualizations for your presentations. Current Google Data Analysts will continue to instruct and provide you with hands-on ways to accomplish common data analyst tasks with the best tools and resources. Learners who complete this certificate program will be equipped to apply for introductory-level jobs as data analysts. No previous experience is necessary. By the end of this course you will - Understand the importance of data visualization. - Learn how to form a compelling narrative through data stories. - Gain an understanding of how to use Tableau to create dashboards and dashboard filters. - Discover how to use Tableau to create effective visualizations. - Explore the principles and practices involved with effective presentations. - Learn how to consider potential limitations associated with the data in your presentations. - Understand how to apply best practices to a Q&A with your audience.

Enroll today (teams & executives are welcome): imp.i384100.net/vN7W4A 

Download your free Data Science  - Career Transformation Guide.

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

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

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

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

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

Python: Data Science and Machine Learning Recipes

Dev colleagues, the Python: Data Science and Machine Learning Recipes training offers a hands-on approach to building your Python skills through a series of practical projects from scratch. Hone your expertise in areas such as data analysis, machine learning, web scraping, and more. Skill-based lessons include: 1)  Manipulate and Visualize Data in Jupyter - Get started with Jupyter Notebooks, Load data into Jupyter, Manipulate data with Pandas, Visualize data; 2) Perform Sentiment Analysis - Sentiment Analysis tools in Python, Learn the basics of NLTK, Incorporate Sentiment Analysis into an application, Analyze with real-world data; 3) Work with Image Recognition - Learn about image recognition tools in Python, Learn the basics of OpenCV, Incorporate image recognition into an application; 4) Scrape Data from the Internet - Learn about web-scraping tools in Python and the basics of the Beautiful Soup Library, Format and use scraped data, and Modify web-scraping logic for other websites. 

Enroll today (teams & executives are welcome): https://tinyurl.com/2brapsfz 

Download your free Data Science  - Career Transformation Guide.

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

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

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

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

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