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 

Business Analytics (Nanodegree Program)

Colleagues, in the “Business Analytics - Nanodegree Program” you will master data fundamentals applicable to any industry and learn to make data-driven decisions. From collecting and analyzing data to modeling business scenarios, students will learn Excel, SQL, and Tableau, utilizing data visualization skills to communicate findings. Skill-based training modules include: 1) Introduction to Data - Basic spreadsheet use • Business KPIs • Basic descriptive statistics • Spreadsheet functions • Quantitative data visualization • Forecast modeling in spreadsheets • Data visualization in spreadsheets • Professional presentations • Finance metrics • Marketing metrics • Data validation in spreadsheets • Pivot tables • Categorical data visualization • Inferential statistics • Chart types • Sales metrics • Growth metrics, 2) Using SQL for Data Analysis - SQL aggregations • SQL joins • SQL queries • SQL subqueries • SQL window functions, and 3) Data Visualization in Tableau - Tableau interactive dashboards • Tableau calculated fields • Tableau map-based visualizations • Data visualization design • Tableau story point • Tableau data pane • Tableau field organization and customization • Chart selection • Data storytelling • Tableau interactive dashboards • Tableau visualizations.

Enroll today (teams & executives are welcome): https://tinyurl.com/ycxwdk4k 

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 - Online Learning Central (share with your team)  

TensorFlow Developer Professional Certificate

Colleagues, in the “TensorFlow Developer Professional Certificate” program you will build AI apps with Tensorflow. Build, train, and optimize deep neural networks and dive deep into Computer Vision, Natural Language Processing, and Time Series Analysis, along with best practices and hands-on experience in one of the most in-demand deep learning frameworks. Learn best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for computer vision applications. Handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout. Build natural language processing systems using TensorFlow. Apply RNNs, GRUs, and LSTMs as you train them using text repositories. Acquire highly marketable skills in RNNs, Computer Vision, Convolutional Neural Network, Forecasting, Transfer Learning, Time Series, Machine Learning, Tokenization, Dropouts, Natural Language Processing, TensorFlow and Augmentation. Applied Learning Project: gain hands-on experience through 16 Python programming assignments. By the end of this program, you will be ready to build and train neural networks using TensorFlow, improve your network’s performance using convolutions as you train it to identify real-world images, teach machines to understand, analyze, and respond to human speech with natural language processing systems, process text, represent sentences as vectors, and train a model to create original poetry, and create forecasts for univariate time series using deep neural networks. Training lessons address: 1) Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning, 2) Convolutional Neural Networks in TensorFlow, 3) Natural Language Processing in TensorFlow, and 4) Sequences, Time Series and Prediction. 

Enroll today (teams & executives are welcome): imp.i384100.net/3e5gan 

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)

Google AI Essentials (training)

Colleagues, the Google AI Essentials program is designed to help people across roles and industries get essential AI skills to boost their productivity, zero experience required. The course is taught by AI experts at Google who are working to make the technology helpful for everyone. In under 10 hours, they’ll do more than teach you about AI — they’ll show you how to actually use it in the real world. Stuck at the beginning of a project? You’ll learn how to use AI tools to generate ideas and content. Planning an event? You’ll use AI tools to help research, organize, and make more informed decisions. Drowning in a flooded inbox? You’ll use AI tools to help speed up those daily work tasks, like drafting email responses. You’ll also learn how to write effective prompts and use AI responsibly by identifying AI’s potential biases and avoiding harm. After you complete the course, you’ll earn a certificate from Google to share with your network and potential employers. By using AI as a helpful collaboration tool, you can set yourself up for success in today’s dynamic workplace — and you don’t even need programming skills to use it. Skill-based modules include: 1) Introduction to AI, 2) Maximize Productivity With AI Tools, 3) Discover the Art of Prompt Engineering, 4) Use AI Responsibly, and 5) Stay Ahead of the AI Curve. Learn generative AI tools to help develop ideas and content, make more informed decisions, and speed up daily work tasks. Write clear and specific prompts to get the output you want - you’ll apply prompting techniques to help summarize, create tag lines, and more. Use AI responsibly by identifying AI’s potential biases and avoiding harm. Develop strategies to stay up-to-date in the emerging landscape of AI. Gain high demand and highly marketable skills in Artificial Intelligence (AI), Prompt Engineering, Large Language Models (LLMs) and Generative AI.

Enroll today (teams & executives are welcome): https://tinyurl.com/49f59efr 

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)