Certified LLM Developer™

Colleagues, in the era of advanced artificial intelligence, large language models (LLMs) are transforming the landscape of technology and innovation. The Certified LLM Developer Certification program is meticulously designed to equip you with the comprehensive knowledge and cutting-edge skills needed to develop, fine-tune, and deploy large language models. This program offers an in-depth understanding of LLM architectures, tools, and best practices, providing hands-on experience in building AI models that can understand and generate human-like text. Through immersive learning experiences, you’ll master the art of working with LLMs to create intelligent, context-aware applications that push the boundaries of AI-driven solutions. Join us and emerge as a Certified LLM Developer™, ready to lead the field in developing sophisticated AI-driven language models. Be at the forefront of a world where language models enhance communication, decision-making, and innovation, and your expertise becomes a key driver of technological advancement. Skill-based lessons include: 1) Introduction to Large Language Models, 2) Core LLM Technologies, 3) Advanced LLM Techniques, 4) Computer Vision, 5) Audio/Video Coding, 6) LLM Frameworks and Tools, 7) Projects Text Classification Model, Text Generation Model, Designing a conversational agent architecture, 8) Deployment and MLOps, 9) Recommended Learning Methodology, and 10 Certification Exam.

Enroll today (teams & executives are welcome): https://tinyurl.com/58s3ytbw 

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) (Kindle)

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

Certified Google Gemini Professional - Blockchain Council

Colleagues, the “Blockchain Council - Certified Google Gemini Professional” program is an immersive journey that goes beyond merely keeping up with this trend – it empowers you to become a trailblazer. Blockchain Council Certified Google Gemini Professional certification program has been meticulously crafted to provide you with a comprehensive understanding of Gemini AI, Prompts, and their versatile applications. Through this program you will effortlessly acquire the expertise needed to bring imaginative and unparalleled creations to life. Embark on this exhilarating expedition with us, and emerge as a Blockchain Council Certified Google Gemini Professional prepared to shape the future of AI-enhanced content creation. Be at the forefront of a world where technology and limitless imagination seamlessly merge to unlock endless possibilities. Skill-based training lessons include: 1) Introduction to Gemini AI, 2) AI and Machine Learning in Gemini AI, 3) Gemini AI Fundamentals, 4) Gemini AI Prompt Engineering, 5) Gemini AI for Creative Content Generation, 6) Gemini AI for Productivity, 7) Gemini AI for Code Generation, 8) Gemini AI for Other Applications, 9) Data Privacy in Gemini AI, 10) The Future of Gemini AI, and 11) Certification Exam.

Enroll today (teams & executives are welcome): https://tinyurl.com/kwb5wn53 

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) (Kindle)

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

Introduction to Data Visualization with Python

Colleagues, the “Introduction to Data Visualization with Python” will teach you several essential data visualization techniques, when to use them, and how to implement them with Python and Matplotlib. you'll learn how to use several essential data visualization techniques to answer real-world questions. First, you'll explore techniques including scatter plots. Next, you'll discover line charts and time series. Finally, you'll learn what to do when your data is too big. When you're finished with this course, you'll have a foundational knowledge of data visualization that will help you as you move forward to analyze your own data. Skill-based training modules include: 1) Introduction to Jupyter, Pandas, and Matplotlib, 2) Finding Distribution of Data with Histograms, 3) Creating Time Series with Line Charts, 4) Examining Relationships in Data with Scatter Plots, 5) Comparing Data with Bar Graphs, 6) What to Do When Your Data Is Too Big, and 7) Solving Real-world Problems with Visualization.

Enroll today (teams & executives are welcome): https://pluralsight.pxf.io/Ke5bQy 

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 Science Course: Complete Data Science Bootcamp 2024

Colleagues, in the “Data Science Course: Complete Data Science Bootcamp” you will learn how to pre-process data, understand the mathematics behind Machine Learning, start coding in Python and learn how to use it for statistical analysis, perform linear and logistic regressions in Python, carry out cluster and factor analysis, create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn, apply your skills to real-life business cases, use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data, unfold the power of deep neural networks, and improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance [66 sections • 520 lectures • 31h 46m total length]. Skill-based lessons include: 1) The Various Data Science Disciplines, 2) Popular Data Science Techniques, 3) Probability - Combinatorics, Bayesian Inference, Distributions, 4) Statistics - Descriptive Statistics, Inferential Statistics Fundamentals, Hypothesis testing, 5) Python - Variables, Data Types, Syntax, Operator, Conditional Statements, Functions, Sequences, Iterations, 6) Deep Learning - How to Build a Neural Network from Scratch with NumPy, TensorFlow, NNs, Deep Neural Networks, Overfitting, Initialization, and 7 Case studies.

Enroll today (teams & executives are welcome): https://tinyurl.com/3fuaecaj 

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 

Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs)

Colleagues, the “Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs)” program is a quick-start guide to help people use and launch LLMs like GPT, T5, and BERT at scale. It shows a step-by-step approach to building and deploying LLMs, with real-world case studies to illustrate the concepts. The video covers topics such as building recommendation engines with siamese BERT architectures, launching an information retrieval system with OpenAI embeddings and GPT3, and building an image captioning system with the vision transformer and GPT-J. This guide provides clear instructions and best practices for using LLMs. It fills a gap in the market by providing a guide to using LLMs and will be a valuable resource for anyone looking to use LLMs in their projects. Learn to Launch an application using proprietary models with an example of an information retrieval system with OpenAI embeddings and GPT3 for Question/Answering, Fine-tune GPT3 with custom examples using their API to get better results, basics of prompt engineering with GPT3 to get more nuanced examples by building a chatbot with persona style depending on who they are talking to using the information retrieval system and Deploy custom LLMs to the cloud. Skill-based lessons address: 1) Introduction to Large Language Models - Overview of Large Language Models, Semantic Search with LLMs, First Steps with Prompt Engineering, 2) Getting the Most Out of LLMs - Optimizing LLMs with Fine-Tuning, Advanced Prompt Engineering, Customizing Embeddings + Model Architectures, and 3) Advanced LLM Usage - Moving Beyond Foundation Models, Advanced Open-source LLM Fine-Tuning and Moving LLMs into Production. 

Enroll today (teams & executives are welcome): https://tinyurl.com/3m7ccp3k 

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) (Kindle)

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

Certified Artificial Intelligence (AI) Developer™

AI Colleagues, becoming a certified AI developer by earning the “Certified Artificial Intelligence (AI) Developer™” credential is the key to a gratifying career in the AI sphere. This certification is specially crafted by experts in AI and covers questions from basic concepts to the very core. Certified AI Developer training is an expertly curated and excellently designed Course, rendering profound knowledge on various aspects of AI. Getting certified as an AI developer will uplift your career. Skill-based training lessons include: 1) Introduction to AI - Intelligent Agents, Advantages and Disadvantages of AI, Challenges of AI, 2) Problem Solving - Uninformed Search Algorithm, Informed Search Algorithm, Adversarial Search, Constraint satisfaction problems, 3) Knowledge Representation And Planning - Knowledge Representation and Techniques, Propositional Logic, First Order Logic, Rule-Based Systems, 4) Probabilistic Reasoning - Basic Probability Concepts, Markov and Hidden Markov Model, Association rules, Dimensionality reduction, Feature Selection and Feature Extraction, 5) Machine Learning - Types of Learning, Clustering, Classification, Decision Tree, Regression, Support Vector Machine, Reinforcement learning, 6) Communication and Perceiving - Natural Language Processing, Perception, 7) Neural Networks - What is a Neural Network?, Types of Neural Network, Neural Network Components, 8) Data Mining Tools - RapidMiner, Working with RapidMiner, Weka, Working with Weka, Orange, R/RStudio, KNIME, 9) Projects - Installing Prerequisites, Clustering using K-means in Python, and 10) Exam - You need to acquire 60+ marks to clear the exam. If you fail to acquire 60+marks, you can retake the exam after one day.

Enroll today (teams & executives are welcome): https://tinyurl.com/2jj6e92r 

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) (Kindle)

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

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