AI for Everyone

Colleagues, the AI for Everyone course is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. Learn the meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science, What AI realistically can--and cannot--do, How to spot opportunities to apply AI to problems in your own organization, What it feels like to build machine learning and data science projects, How to work with an AI team and build an AI strategy in your company and How to navigate ethical and societal discussions surrounding AI. Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI. Skill-based lessons include: 1) What is AI? - Machine Learning, What is data?, The terminology of AI, What makes an AI company?, What machine learning can and cannot dos, More examples of what machine learning can and cannot dos, Non-technical explanation of deep learning, 2) Building AI Projects - Workflow of a machine learning project, Workflow of a data science project, Every job function needs to learn how to use data, How to choose an AI project, How to choose an AI project, Working with an AI team, Technical tools for AI teams, 3) Building AI In Your Company - Case study: Smart speaker, Case study: Self-driving car, Example roles of an AI team, AI Transformation Playbook, AI Transformation Playbook, AI pitfalls to avoid, Taking your first step in AI, Survey of major AI application areas, Survey of major AI techniques, and 4) AI and Society - A realistic view of AI, Discrimination/Bias, Adversarial attacks on AI, Adverse uses of AI, AI and developing economies, and AI and jobs.  

Enroll today (teams & execs welcome): https://imp.i384100.net/4P4qr9 

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)

#coursera #AndrewNg #deeplearning #machinelearning #ai #artificialintelligence #Applications #Usecases #ai #artificialintelligence #machinelearning #PromptEngineering #ChatGPTPlayGround #DataPrivacy #datamasking #dataleakage #aiacademy #career #chatgpt #llama #gemini #AGI #ASI #singularity #aiacademy

Gen AI for Data Privacy & Protection (training)

Colleagues, in the Gen AI for Data Privacy & Protection program you will grasp the significance of data privacy, navigate AI ethics, and safeguard data with the help of Generative AI. Gain high-demand and highly marketable skills in Security Awareness, Information Technology Security Fundamentals and Generative AI. This course offers an exploration into using Generative AI for Data Privacy & Protection, designed for learners keen on advancing their expertise in this critical area. Through a curriculum that blends theoretical foundations with practical applications, participants delve into the core aspects of Generative AI for safeguarding data, and the essential considerations of ethics and compliance. The short course aims to equip learners with the skills to adeptly navigate the complexities of data protection, ensuring ethical integrity and regulatory adherence, thus helping them to understand the challenges of implementing cutting-edge data privacy solutions in a rapidly evolving technological landscape. Training videos address: 1) Overview of Data Privacy, 2) Understand the Role of Gen AI in Data Privacy, 3) Privacy Challenges with Generative AI, 4) Diving Deep into Privacy Compliance Laws, 5) Tips to Safeguard your Organization, 6) Importance of Ethical and Legal Considerations, 7) AI Specific Laws and Governing Bodies, and 8) Gen AI Responsibility for Protecting Data. Reading materials include: 1) Empowering Data Privacy: How Generative AI Enhances Security and Confidentiality, 2) How to Use Discussion Forums, 3) In-Depth Analysis of Global Privacy Compliance Regulations, 4) Mastering the Complexities of GDPR and CCPA, 5) Addressing Privacy Concerns in Generative AI Applications, 6) Role of Gen AI in Ensuring Data Privacy: Safeguarding Your Information, 7) Navigating the CPRA and the EU Artificial Intelligence Act, and 8) Understanding AI-Specific Legislation and Regulatory Frameworks. Knowledge Checks cover Getting Started with Generative AI, Generative AI's Privacy Challenges and Regulations, and Ethical and Legal Consideration. 

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

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)

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

Colleagues, the new ebook 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 

Data Structures and Algorithms (training)

Colleagues, the Data Structures and Algorithms (training) provides you with hands-on practice with over 100 data structures and algorithm exercises and guidance from a dedicated mentor to help prepare you for interviews and on-the-job scenarios. Gain skills including Basic Algorithms - Python data structures • Basic algorithms • Python arrays • Python lists • Python trees • Breadth-first search • Tree search • Recursive algorithms • Hash maps • Call stacks • Sorting algorithms • Hashing • Depth-first search • Divide and conquer algorithms • Tree algorithms, and Advanced Algorithms - A search algorithm • Graph algorithms • Greedy algorithms • Dynamic programming • Graph data structure. Lessons cover: 1) Introduction - refreshing your Python skills and learning about problem solving and efficiency, 2) Get Help with Your Account, 3) Getting Help, 3) Data Structures and Algorithms, Python Refresher - A quick refresh on Python basics, How to Solve Problems, A systematic way of approaching and breaking down problems, 4) Understanding the importance of efficiency when working with data structures and algorithms, 5) Unscramble Computer Science Problems, Deconstruct a series of open-ended problems into smaller components (e.g, inputs, outputs, series of functions), 6) Data Structures - core data structures used in programming - Introduction to Data Structures, Arrays and Linked Lists, Stacks and Queues, Apply Recursion to Problems, Trees - basic tree's, tree traversal and binary search trees, 7) Maps and Hashing, 8) Show Me the Data Structures - solve a open-ended practice problems. Hone your skills to identify and implement appropriate data structures and corresponding methods that meet given constraints, 8) Basic Algorithms - learn about the basic algorithms used in programming, Sorting - Faster Divide & Conquer Algorithms, 9) Problems vs. Algorithms - apply real-world open ended problems which train you to implement suitable data structures and algorithms under different context, Advanced Algorithms - learn the basic algorithms used in programming, Greedy Algorithms - Get familiar with and practice greedy algorithms, Graph Algorithms, 10) Dynamic Programming - apply your learnings to challenging exercises, 11) Route Planner - build a route-planning algorithm like the one used in Google Maps to calculate the shortest path between two points on a map.

Enroll today (teams & executives are welcome): https://tinyurl.com/35rrzjj9  

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)

Much career success, Lawrence E. Wilson - AI Academy (share with your team) https://tinyurl.com/hh7bf4m9 

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

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 

Data Engineering with AWS (Nanodegree Program)

Colleagues, in the Data Engineering with AWS - Nanodegree Program you will learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets. Skill-based courses include: 1) Data Modeling - create relational and NoSQL data models to fit the diverse needs of data consumers. Use ETL to build databases in PostgreSQL and Apache Cassandra, Introduction to Data Modeling - understand the purpose of data modeling, the strengths and weaknesses of relational databases, and create schemas and tables in Postgres, 3) NoSQL Data Models - when to use non-relational databases based on the data business needs, their strengths and weaknesses, and how to creates tables in Apache Cassandra (Project: Data Modeling with Apache Cassandra); 4) Cloud Data Warehouses - create cloud-based data warehouses. You’ll sharpen your data warehousing skills, deepen your understanding of data infrastructure, and be introduced to data engineering on the cloud using Amazon Web Services (AWS) - Introduction to Cloud Data Warehouses, Introduction to Data Warehouses, you'll be introduced to the business case for data warehouses as well as architecture, extracting, transforming, and loading data, data modeling, and data warehouse technologies, 5) ELT and Data Warehouse Technology in the Cloud - learn about ELT, the differences between ETL and ELT, and general cloud data warehouse technologies, 6) AWS Data Warehouse Technologies - to set up Amazon S3, IAM, VPC, EC2, and RDS. You'll build a Redshift data warehouse cluster and learn how to interact with it, 6) Implementing a Data Warehouse on AWS - implement a data warehouse on AWS (Project: Data Warehouse. You will build an ETL pipeline that extracts data from S3, stages data in Redshift, and transforms data into a set of dimensional tables for an analytics team); 7) Spark and Data Lakes - learn about the big data ecosystem and how to use Spark to work with massive datasets. You’ll also learn about how to store big data in a data lake and query it with Spark. Introduction to Spark and Data Lakes - learn how Spark evaluates code and uses distributed computing to process and transform data. You'll work in the big data ecosystem to build data lakes and data lake houses, 8) Big Data Ecosystem, Data Lakes, and Spark - learn about the problems that Apache Spark is designed to solve. You'll also learn about the greater Big Data ecosystem and how Spark fits into it, 9) Spark Essentials - use Spark for wrangling, filtering, and transforming distributed data with PySpark and Spark SQL - Using Spark in AWS, learn to use Spark and work with data lakes with Amazon Web Services using S3, AWS Glue, and AWS Glue Studio, 10) Ingesting and Organizing Data in a Lakehouse. In this lesson you'll work with Lakehouse zones. You will build and configure these zones in AWS (Project: STEDI Human Balance Analytics - work with sensor data that trains a machine learning model. You'll load S3 JSON data from a data lake into Athena tables using Spark and AWS Glue, 11) Automate Data Pipelines. In this course, you'll build pipelines leveraging Airflow DAGs to organize your tasks along with AWS resources such as S3 and Redshift, 12) Automating Data Pipelines - build data pipelines, 13) Data Pipelines. In this lesson, you'll learn about the components of a data pipeline including Directed Acyclic Graphs (DAGs). You'll practice creating data pipelines with DAGs and Apache Airflow, 14) Airflow and AWS - create connections between Airflow and AWS first by creating credentials, then copying S3 data, leveraging connections and hooks, and building S3 data to the Redshift DAG, 15) Data Quality - track data lineage and set up data pipeline schedules, partition data to optimize pipelines, investigating Data Quality issues, and write tests to ensure data quality, 16) Production Data Pipelines - build Pipelines with maintainability, reusability and monitoring,  in mind. They will also learn about pipeline monitoring (Project: Data Pipelines - work on a music streaming company’s data infrastructure by creating and automating a set of data pipelines with Airflow, monitoring and debugging production pipelines. 

Enroll today (teams & executives are welcome): https://tinyurl.com/4pxyw9vt  

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)

Much career success, Lawrence E. Wilson - AI Academy (share with your team) https://tinyurl.com/hh7bf4m9 

Data Analyst (Nanodegree Program)

Colleagues, in the Data Analyst (Nanodegree Program) you will learn to clean up messy data, uncover patterns and insights, make predictions using machine learning, and clearly communicate your findings. Skill-based training modules include: 1) Introduction to Data Analysis with Pandas and NumPy - Pandas • Exploratory data analysis • Basic data visualizations • Jupyter notebooks • Data storytelling • Data analysis process • NumPy • Data manipulation, 2) Advanced Data Wrangling - Data cleaning • Data storage • Data Tidiness Assessment • Data gathering • Pandas • Data quality assessment • File i/o, and 3) Data Visualization with Matplotlib and Seaborn - Latent variables • Data visualization design • Data fluency • Exploratory data analysis • Professional presentations • Data limitations and biases • Data storytelling • Jupyter notebooks • Quantitative data visualization. 

Enroll today (teams & executives are welcome): https://tinyurl.com/mr9ec5rj  

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)

Much career success, Lawrence E. Wilson - AI Academy (share with your team) https://tinyurl.com/hh7bf4m9