Data Science Specialization (John Hopkins University)

DS colleagues, in the “Data Science Specialization” 10 course program you will learn Use R to clean, analyze, and visualize data, Navigate the entire data science pipeline from data acquisition to publication, Use GitHub to manage data science projects and Perform regression analysis, least squares and inference using regression models. Gain high-demand and highly marketable skills with GitHub, Machine Learning, R Programming and Regression Analysis. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material. Skill-based training modules include: 1) The Data Scientist’s Toolbox, 2) R Programming, 3) Getting and Cleaning Data, 4) Exploratory Data Analysis, 5) Reproducible Research, 6) Statistical Inference, 7) Regression Models, 8) Practical Machine Learning, 9) Developing Data Products, and 10) Data Science Capstone.

Enroll today (teams & executives are welcome): https://imp.i384100.net/y20m12

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)

What is Data Science? (training)

Colleagues, after taking the “What is Data Science?” program you will be able to answer this question, understand what data science is and what data scientists do, and learn about career paths in the field. The art of uncovering insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and accurately predicted the Nile River's flooding every year. Recently, they have carved out a unique and distinct field for the work they do. This field is data science. In today's world, we use Data Science to find patterns in data and make meaningful, data-driven conclusions and predictions. This course is for everyone and teaches concepts like how data scientists use machine learning and deep learning and how companies apply data science in business. You will meet several data scientists, who will share their insights and experiences in data science. Learn to define data science and its importance in today’s data-driven world, describe the various paths that can lead to a career in data science, summarize  advice given by seasoned data science professionals to data scientists who are just starting out, and explain why data science is considered the most in-demand job in the 21st century. Gain high-demand skills in Data Science, Big Data, Machine Learning, Deep Learning and Data Mining. Training modules include: 1) Defining Data Science and What Data Scientists Do, 2) Data Science Topics, 3) Applications and Careers in Data Science, and 4) Data Literacy for Data Science. 

Enroll today (teams & executives are welcome): imp.i384100.net/JzGm0R

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)

TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning

AI colleagues, in the “Introduction to TensorFlow for Artificial Intelligence, Machine Learning and Deep Learning” program you will learn best practices for using TensorFlow, a popular open-source machine learning framework, build a basic neural network in TensorFlow, train a neural network for a computer vision application and understand how to use convolutions to improve your neural network. Gain highly marketable skills in Computer Vision, Tensorflow and Machine Learning. Training modules address: 1) A New Programming Paradigm - introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, 2) Introduction to Computer Vision - solve problems of computer vision with just a few lines of code, 3) Enhancing Vision with Convolutional Neural Networks, and 4) Using Real-World Images - handling complex images. 

Enroll today (teams & execs welcome): https://imp.i384100.net/21ZjKD 

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)

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)