Machine Learning - Big Data Trunk

https://project.bigdatatrunk.com Quality Corporate and Classroom Training in Bay Area CA Thu, 29 May 2025 17:18:04 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 Diving into Claude – Ethical AI for Precision https://project.bigdatatrunk.com/courses/diving-into-claude-ethical-ai-for-precision/ https://project.bigdatatrunk.com/courses/diving-into-claude-ethical-ai-for-precision/#respond Mon, 07 Apr 2025 17:36:07 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=58220 This focused session introduces Claude, the conversational AI developed by Anthropic with a strong emphasis on ethics, safety, and precision.

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Overview
Prerequisites
Audience
Curriculum

Description:

This focused session introduces Claude, the conversational AI developed by Anthropic with a strong emphasis on ethics, safety, and precision. Ideal for professionals who require reliable and transparent outputs, Claude is particularly well-suited for academic, research, and coding contexts. Participants will explore how to use Claude effectively for structured tasks such as code generation, debugging, and complex text analysis, all while upholding ethical standards in AI application.

Duration: 90 min

Course Code: BDT476

Learning Objectives:

By the end of this session, participants will be able to:

Understand Claude’s underlying design principles and ethical priorities
Develop effective prompts to generate accurate and trustworthy outputs
Apply Claude to real-world coding tasks and in-depth text analysis

Basic understanding of AI ethics and natural language processing
Optional: Experience with coding or research tools

Researchers requiring dependable AI for academic or analytical work
Developers looking for accurate and explainable coding support
Tech-savvy professionals exploring ethical AI alternatives

Course Outline:

• Introduction to Claude and Anthropic’s Ethical AI Mission
• How Claude Ensures Safety, Reliability, and Interpretability
• Core Features: Transparent Outputs, Coding Accuracy, and Responsible AI
• Practical Examples: Writing Functions and Analyzing Complex Texts
• Hands-On: Debugging Code and Enhancing Research Outputs
• Live Demonstration and Q&A

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]]> https://project.bigdatatrunk.com/courses/diving-into-claude-ethical-ai-for-precision/feed/ 0 AI-Powered Coding: Enhancing Development with Generative Models https://project.bigdatatrunk.com/courses/ai-powered-coding-enhancing-development-with-generative-models/ https://project.bigdatatrunk.com/courses/ai-powered-coding-enhancing-development-with-generative-models/#respond Fri, 04 Apr 2025 05:06:08 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=58064 AI-powered tools like ChatGPT, Claude, and Cursor.ai are transforming the way people learn and write code, making programming more accessible, efficient, and intuitive.

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Overview
Prerequisites
Audience
Curriculum

Description:

AI-powered tools like ChatGPT, Claude, and Cursor.ai are transforming the way people learn and write code, making programming more accessible, efficient, and intuitive. Whether you're a self-taught programmer or someone looking to enhance your coding efficiency, this course introduces you to how AI can assist in coding, debugging, and learning programming concepts.

Participants will explore how AI can help break down complex coding problems, suggest solutions, and explain programming concepts in a way that speeds up learning and problem-solving. Hands-on exercises will provide a practical understanding of AI-assisted coding.

By the end of this course, attendees will have a solid understanding of how AI can assist in writing, understanding, and improving code—without needing to be an expert programmer.

Duration: 1 Day

Course Code: BDT469

Learning Objectives:

After completing this course, participants will be able to:

Understand how AI can assist in learning and writing code.
Use ChatGPT and Claude to explain coding concepts in an easy-to-understand manner.
Leverage Cursor.ai for smart code completion, debugging, and automation.
Improve problem-solving skills by interacting with AI to break down challenges.
Generate and refine code using AI, making programming more accessible.
Use AI to learn more effective programming and best practices.

Familiarity with AWS Cloud Services (EC2, S3, Lambda, etc.), Basic understanding of machine learning concepts and AI models, Experience with AWS Management Console or equivalent tools, No prior experience with Generative AI is required, but knowledge of basic AI concepts is helpful.

Gen AI enthusiast, Software Developers and Analyst

Course Outline:

Introduction to AI-Assisted Coding

Overview of AI in code editing
Understanding the capabilities and limitations of coding assistants
Walkthrough of ChatGPT Canvas, Claude, and Cursor.ai

Effective Prompt Engineering for Coding

Basics of prompting for coding tasks
Structuring prompts for debugging, optimization, and explanation
Hands-on exercises: Writing prompts for various coding challenges

Using ChatGPT Canvas for Coding

Writing and refining code with ChatGPT
Debugging and troubleshooting code issues
Hands-on: Generating and improving code snippets with ChatGPT Canvas

Leveraging Claude for Coding

Claude’s capabilities in software development
Using Claude for code completion and documentation
Hands-on: AI-powered problem-solving with Claude

Exploring Cursor.ai for AI-Powered Development

ai as an intelligent coding assistant
Automated refactoring and AI-driven suggestions
Hands-on: Using Cursor.ai for live coding and debugging

Real-World Applications and Best Practices

Integrating AI coding assistants into development workflows
Ethical considerations and code quality assurance
Hands-on: Building a mini-project using AI-powered coding tools

Training material provided: Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/ai-powered-coding-enhancing-development-with-generative-models/feed/ 0 Generative AI for AWS – Enhancing Cloud Workflows and Applications https://project.bigdatatrunk.com/courses/generative-ai-for-aws-enhancing-cloud-workflows-and-applications/ https://project.bigdatatrunk.com/courses/generative-ai-for-aws-enhancing-cloud-workflows-and-applications/#respond Thu, 03 Apr 2025 08:50:30 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=58039 This course is designed to introduce participants to Generative AI capabilities within the Amazon Web Services (AWS) ecosystem.

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Overview
Prerequisites
Audience
Curriculum

Description:

This course is designed to introduce participants to Generative AI capabilities within the Amazon Web Services (AWS) ecosystem. The course covers how AWS's AI/ML tools can be leveraged to enhance cloud-based applications, automate workflows, generate content, and streamline data processing. Participants will learn how to integrate Generative AI models into AWS services such as Amazon SageMaker, AWS Lambda, Amazon Polly, Amazon Lex, and AWS AI Services to optimize business processes and create innovative AI-powered solutions

Duration: 1 Day

Course Code: BDT425

Learning Objectives:

By the end of the course, participants will be able to:

Understand the foundational concepts of Generative AI and how they can be applied using AWS
Leverage AWS services like SageMaker, Lambda, Polly, Lex, Rekognition, and Comprehend for Generative AI use cases.
Automate content creation, text summarization, image generation, and voice interaction using AWS-based solutions.
Implement AI-driven automation workflows and improve user experiences with Conversational AI and AI-powered applications.
Scale and optimize Generative AI models for production environments using AWS infrastructure.
Stay up-to-date with future trends and best practices for integrating Generative AI in AWS-based applications.

Familiarity with AWS Cloud Services (EC2, S3, Lambda, etc.), Basic understanding of machine learning concepts and AI models, Experience with AWS Management Console or equivalent tools, No prior experience with Generative AI is required, but knowledge of basic AI concepts is helpful.

Cloud Architects and DevOps Engineers looking to integrate AI into AWS-based applications. Data Scientists and Machine Learning Engineers interested in leveraging AWS tools for Generative AI use cases. Software Developers looking to automate workflows and improve user experiences with AI capabilities.

Course Outline:

Session 1: Introduction to Generative AI & AWS

Overview of Generative AI
- What is Generative AI? (e.g., GPT, BERT, DALL·E, Codex)
- Key use cases for Generative AI in the cloud: Content creation, text generation, image generation, and automated workflows.
- Benefits of using AWS for Generative AI applications: Scalability, flexibility, security, and cost efficiency.
Overview of AWS AI/ML Services
- Introduction to Amazon SageMaker, AWS Lambda, AWS AI Services (Polly, Lex, Rekognition, Comprehend)
- Using AWS infrastructure to run and scale Generative AI models
- AWS tools for training and deploying AI models

Session 2: Implementing Generative AI for Content Generation

Using Amazon Polly for Text-to-Speech
- Overview of Amazon Polly and its capabilities
- Creating natural-sounding speech from text with Amazon Polly
- Hands-on demo: Implementing Polly for creating speech-based content from user input (e.g., news articles, product descriptions)
Using AWS Lambda for AI-Driven Automation
- Introduction to AWS Lambda and serverless computing
- Automating content generation workflows with Lambda (e.g., using GPT-3 to generate content on demand)
- Hands-on demo: Using Lambda to trigger Generative AI models for automated content generation

Session 3: AI for Text, Image, and Voice Applications

AI for Text Generation and Understanding with Amazon Comprehend and GPT Models (45 mins)
- Using Amazon Comprehend for sentiment analysis, entity recognition, and text classification
- Integrating Generative AI models (e.g., GPT-3) for text generation and summarization tasks
- Hands-on demo: Using Comprehend to analyze customer feedback and GPT to generate summarized reports or responses
AI for Image Generation and Recognition with Amazon Rekognition (45 mins)
- Introduction to Amazon Rekognition for image and video analysis
- Generating images with Generative Adversarial Networks (GANs) in the AWS ecosystem (via SageMaker)
- Hands-on demo: Using Rekognition for object detection and GANs for image creation in a cloud-based application

Session 4: Building Conversational AI Solutions with AWS

Amazon Lex: Building Conversational Agents
- Introduction to Amazon Lex for building conversational bots and virtual assistants
- Leveraging Generative AI models (like GPT) to enhance conversation quality and context understanding
- Hands-on demo: Building a simple AI chatbot using Lex for customer service automation
Amazon Polly and Lex for Advanced Voice Interfaces
- Combining Amazon Polly and Amazon Lex for multi-modal voice interactions (e.g., voice-based apps, smart assistants)
- Integrating Generative AI for enhanced user experiences (context-aware interactions)
- Hands-on demo: Building a voice-enabled virtual assistant that generates personalized responses using Lex and Polly

Session 5: Scaling Generative AI Models in AWS

Amazon SageMaker for Model Training and Deployment
- Using Amazon SageMaker for training, tuning, and deploying custom Generative AI models
- Running pre-trained AI models (e.g., GPT, T5, BERT) on SageMaker to generate content or perform tasks
- Hands-on demo: Training a custom text generation model in SageMaker and deploying it as an endpoint
Optimizing Costs and Performance for Generative AI on AWS
- Best practices for cost management when using AWS for Generative AI workloads
- Efficiently scaling models for production using SageMaker, EC2, and Lambda
- Monitoring performance with Amazon CloudWatch

Session 6: Real-World Use Cases and Future Trends

Real-World Use Cases of Generative AI in AWS
- Case studies of AI-powered applications in industries such as e-commerce, customer service, healthcare, and media (e.g., chatbots, content creation, voice assistants)
- Demonstrating the ROI of Generative AI in cloud environments
Future Trends in Generative AI and Cloud Computing
- Upcoming AWS features for AI/ML (e.g., AI model marketplaces, improved inference capabilities)
- The evolution of Generative AI and its role in cloud-native applications
- Best practices for staying up-to-date with AWS AI/ML advancements

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]]> https://project.bigdatatrunk.com/courses/generative-ai-for-aws-enhancing-cloud-workflows-and-applications/feed/ 0 Prep for Tableau Certification Training https://project.bigdatatrunk.com/courses/tableau-certification-training/ https://project.bigdatatrunk.com/courses/tableau-certification-training/#respond Wed, 12 Mar 2025 09:16:50 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=57615 This training program is designed to prepare individuals for the Tableau Desktop Specialist certification exam.

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Overview
Prerequisites
Audience
Curriculum

Description:

This training program is designed to prepare individuals for the Tableau Desktop Specialist certification exam. The course covers the foundational skills needed to work effectively with Tableau Desktop, from connecting to data and creating visualizations to publishing interactive dashboards.

Participants will learn to use Tableau's powerful features to analyze and present data, leveraging Tableau’s drag-and-drop interface and advanced data manipulation capabilities. The course also provides hands-on experience with real-world data to help participants confidently approach the exam.

For Certification based Assistance and Mock quizzes please visit: https://certify360.ai/

Duration: 4 Days

Course Code: BDT416

Learning Objectives:

After completing this course, participants will be able to:

Understand the fundamentals of Tableau Desktop and the certification exam structure.
Connect to and prepare data for analysis using Tableau Desktop.
Create basic and advanced visualizations such as bar charts, pie charts, scatter plots, and maps.
Use filters, groups, sets, and parameters to enhance interactivity.
Build and publish dashboards with advanced interactive elements.
Apply basic calculated fields and aggregations.
Understand Tableau’s core functionality and use it for data exploration and analysis.

Basic knowledge of data concepts
Familiarity with spreadsheets and databases
No prior Tableau experience is required, but familiarity with basic visualization tools is beneficial.

Business Professionals, Data Analysts, BI Professionals, Software Developers, Data Engineers, Data Scientist, Professionals aspiring to earn Tableau certifications.

Course Outline:

Introduction to Tableau and Data Connections

Overview of Tableau Desktop

1. Introduction to Tableau products and certifications
2. Navigating the Tableau Desktop interface

Key concepts of Tableau (data source, worksheets, dashboards, stories).

Connecting to Data
1. Connecting to different data sources (Excel, text files, databases, web data connectors)
2. Data preparation techniques (data types, renaming, data cleaning)

Data structure and relationships.

Data Visualization Basics
- Building Basic Visualizations
  1. Bar charts, line charts, pie charts, and scatter plots
  2. Customizing visualizations (colours, shapes, labels).
- Using Filters and Sorting
  1. Filtering data by dimensions, measures, and relative date filters
  2. Sorting data and visualizations

Creating filter actions for interactivity.

Working with Data
1. Sorting and grouping data
2. Calculations (using basic calculations like SUM, AVG, etc.)

Advanced Visualizations
- Calculated Fields and Aggregations
  1. Introduction to calculated fields (basic calculations, logical calculations)
  2. Aggregations and summary statistics

Creating custom fields for advanced analysis.

Using Level of Detail (LOD) expressions for granular analysis

Data Blending and Joins
1. Combining multiple datasets
2. Understanding relationships, joins, and unions in Tableau
Advanced Visualizations
1. Creating maps and geographic visualizations
2. Bullet charts, heat maps, and treemaps

Dual-axis charts and combination charts.

Creating Dashboards with Multiple Visualizations
- Building Dashboards
  1. Designing dashboards with layout containers and elements
  2. Adding filters, actions, and tooltips for interactive dashboards

Best practices for dashboard design and user experience

Storytelling with Data
1. Combining multiple visualizations on a single dashboard
2. Managing dashboard size, layout, and interactivity.

Building Tableau Stories.

Certification Preparation
- Preparing for the Tableau Desktop Specialist Exam
  1. Review of key exam topics (visualization types, calculations, dashboard creation, etc.)
- Certification Tips
  1. Time management and preparation strategies.
  2. Focus areas for success.

Simulated exams for hands-on experience.

Common mistakes and how to avoid them.

Q&A Session
1. Practice and Mock Tests

Training material provided: Yes

Digital format materials
Tableau workbooks
Practice datasets
Mock exam questions and solutions

Any Additional Information

Software Requirements:
Participants should install Tableau Desktop (either the trial or a licensed version) before the course.
Post-Course Support:
Access to recorded sessions, practice exams, and additional study materials for a set period after course completion.

This course equips participants with the skills to not only pass the Tableau Desktop Specialist exam but also to apply Tableau Desktop effectively in their day-to-day data analysis tasks, making it an essential certification for anyone looking to excel in data visualization.

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]]> https://project.bigdatatrunk.com/courses/tableau-certification-training/feed/ 0 Building AI Agents: A Hands-on Workshop https://project.bigdatatrunk.com/courses/building-ai-agents-a-hands-on-workshop/ https://project.bigdatatrunk.com/courses/building-ai-agents-a-hands-on-workshop/#respond Thu, 27 Feb 2025 04:36:04 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=57016 This full-day hands-on workshop provides a deep dive into Agentic AI, their architecture, and real-world applications. Participants will build AI Agents with advanced capabilities, including multi-agent collaboration, memory, and task automation using open-source tools.

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Overview
Prerequisites
Audience
Curriculum

Description:

This full-day hands-on workshop provides a deep dive into Agentic AI, their architecture, and real-world applications. Participants will build AI Agents with advanced capabilities, including multi-agent collaboration, memory, and task automation using open-source tools.

Duration: 1 Day

Course Code: BDT402

Learning Objectives:

After this course, you will be able to:

Implement complex AI Agent systems with memory management and tool integration
Develop multi-agent systems that can collaborate and communicate effectively
Design and integrate custom tools and capabilities into AI Agent frameworks
Build, test, and deploy production-ready AI Agent applications with appropriate error handling and security considerations

Proficiency in Python
Familiarity with APIs and LLMs (e.g., OpenAI, Hugging Face)
Basic understanding of AI concepts

Developers
AI engineers
Students with Python and LLM experience

Course Outline:

Module 1: Advanced Agent Architectures

Overview of autonomous agents
Agent design patterns and architectures
Architectures: Reflex, Deliberative, Hybrid
Planning systems and decision-making
Advanced memory architectures
State management and persistence
LangChain, CrewAI, AutoGen frameworks

Module 2: Building Agent Infrastructure

Setting up a development environment
Installing necessary libraries (LangChain, AutoGen, ChromaDB, etc.)
Connecting AI Agents to APIs and knowledge bases
Implementing agent framework from scratch
Creating custom tools and capabilities
Advanced prompt engineering techniques

Module 3: Advanced Memory Systems

Vector database implementation
Long-term and working memory
Context window management
Hierarchical memory structures

Module 4: Tool Integration and API Development

Creating custom tools
API integration patterns
Function calling and tool use
Error handling and recovery

Module 5: Multi-Agent Systems

Agent communication protocols
Implementing agent cooperation
Task distribution and management
Conflict resolution

Module 6: Project Implementation

Building a complete agent system
Testing and evaluation
Performance optimization
Deployment considerations

Module 7: Best Practices and Future Directions

Security considerations
Scaling agent systems
Latest research and trends
Resources for continued learning

Optional: Hands-on Project

Building a task management agent
Implementing a research assistant
Creating a multi-agent system

Training material provided: Yes (Digital format)

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]]> https://project.bigdatatrunk.com/courses/building-ai-agents-a-hands-on-workshop/feed/ 0 Machine Learning with Snowflake: Snowpark & Cortex https://project.bigdatatrunk.com/courses/machine-learning-with-snowflake-snowpark-cortex/ https://project.bigdatatrunk.com/courses/machine-learning-with-snowflake-snowpark-cortex/#respond Mon, 03 Feb 2025 07:23:23 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=55377 The rapid evolution of artificial intelligence (AI) and machine learning (ML) is transforming industries worldwide.

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Overview
Prerequisites
Audience
Curriculum

Description:

The rapid evolution of artificial intelligence (AI) and machine learning (ML) is transforming industries worldwide. From personalized customer experiences to real-time fraud detection, organizations are leveraging ML to drive innovation and gain a competitive edge. Snowflake, with its powerful data platform, has emerged as a leader in enabling end-to-end ML pipelines, empowering data professionals to harness the full potential of their data.

This course equips students with the skills to navigate this transformative landscape, combining the scalability of Snowflake with advanced ML capabilities through Snowpark and Cortex. Over three days, students will learn how to build ML workflows that are seamless, scalable, and optimized for modern data engineering and analytics. With Snowflake's recent advancements in Large Language Model (LLM) integration and in-database ML, participants will gain firsthand experience with cutting-edge technologies shaping the future of AI.

Whether you're a data scientist, ML engineer, or analytics professional, this course will help you stay ahead of industry trends and enable you to deploy powerful, real-time ML models in Snowflake's unified platform.

Duration: 3 Day

Course Code: BDT396

Learning Objectives:

After this course, students will be able to:

Understand Snowflake’s architecture for machine learning pipelines and integration with external datasets
Develop ML pipelines using Snowpark for scalable data preparation and modeling
Utilize Cortex for advanced ML pipeline development, including model training and deployment
Implement and execute ML functions and LLM functions in Cortex for real-world use cases
Optimize end-to-end ML workflows using Snowflake’s capabilities

Familiarity with programming language – especially Python
Basics of using Snowflake and SQL
Prior knowledge of Machine Learning will be useful but not required

This course is designed for Software Developers, Data Scientists, Software Architects, Quality Assurance Engineers, Data Analysts to build and implement generative AI models. Familiarity with machine learning concepts (like neural networks) is helpful but not required.

Course Outline:

Snowflake Overview
1. Quick review of Snowflake Architecture
2. Understanding data ingestion from external sources
3. Demos and Labs
Machine Learning Overview
1. Understand the concept of machine learning
2. Learn about data wrangling & preparing data for machine learning
3. Understanding machine learning techniques: Classification & Regression
4. Learn about metrics for validating these techniques
5. Working with hyper parameters, cross validation
6. Build machine learning pipelines
7. Multiple Demos and Labs
Machine Learning pipelines with Snowpark
1. What are Snowpark components?
2. Snowflake Python connector vs Snowpark – what is the difference?
3. Working with UDF, Vectorized UDF, Functions, Procedures
4. Pandas vs Snowpark Dataframes
5. ML Pipelines with Snowpark
6. Multiple Demos and Labs
Machine Learning pipelines with Snowpark ML (Cortex)
1. Introduction to Snowpark ML APIs
2. Data Collections with Filesystem and FileSet
3. Distributed pipelines with Snowpark ML
4. Hyper parameter tuning with Snowpark ML
5. Model predictions with registered models
6. Multiple Demos and Labs
Machine Learning Functions with Cortex
1. What are machine learning functions in cortex
2. Performing Time-series forecasting, anomaly detection, classification with ML Functions
3. Using Snowflake SQL classes and instances
4. Building Classification and Regression Models with Spark machine learning library
5. Understanding the costs involved in using Machine Learning Functions
6. Multiple Demos and Labs
LLM (Large Language Model) Functions with Cortex
1. Understand support for various LLM models on Snowflake
2. Integrate with ChatGPT models
3. Using LLM functions: COMPLETE, SENTIMENT, SUMMARIZE, TRANSLATE, etc
4. Understanding the costs involved in using these LLMs
5. Multiple Demos and Labs

Training material provided: Yes (Digital format)

Hands-on Lab: Students will create a trial Snowflake account for the hands-on labs. If required virtual machines will be provided

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]]> https://project.bigdatatrunk.com/courses/machine-learning-with-snowflake-snowpark-cortex/feed/ 0 Enhanced Machine Learning https://project.bigdatatrunk.com/courses/enhanced-machine-learning/ https://project.bigdatatrunk.com/courses/enhanced-machine-learning/#respond Tue, 06 Aug 2024 06:21:03 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=53241 This intensive one-day workshop is crafted to deepen participants' understanding of advanced machine learning concepts and techniques.

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Overview
Prerequisites
Audience
Curriculum

Description:

This intensive one-day workshop is crafted to deepen participants' understanding of advanced machine learning concepts and techniques. The class will cover crucial topics including handling imbalanced datasets, hyperparameter tuning, dimensionality reduction, TensorFlow tensors, understanding neural networks, and activation functions. Through a blend of theoretical insights and practical exercises, attendees will gain hands-on experience and actionable knowledge to enhance their machine learning proficiency.

NOTE: This course is not meant to cover Generative AI and is not intended to cover unstructured data such as images and text.

Duration: 1 Day

Course Code: BDT364

Learning Objectives:

After this course, you will be able to:

Handling Imbalanced Datasets
Implementing Hyperparameter Tuning
Applying Dimensionality Reduction
Working with TensorFlow Tensors
Comprehend Neural Networks, explore activation functions
Build and Train Neural Networks

Basic understanding of machine learning concepts (or completion of the “Kickstart AI and Machine Learning” course). No prior knowledge of neural networks is required.

This course is designed for Data Scientists, Data Engineers, Software Engineers, Software Architects, and Quality Assurance Engineers who have completed the “Kickstart AI and Machine Learning” course.

Course Outline:

1. Handling Imbalanced Datasets

Understanding challenges for classification problems due to imbalanced target variable
Apply techniques to handle imbalanced datasets
Lab: Visualization and applying different techniques to resolve this problem

2. Implementing Hyper-parameter tuning

Learning importance of hyper parameters on model performance
Use techniques for hyperparameter tuning such as Grid Search and K-Folds
Lab: To apply Grid Search and K-Fold to various machine learning algorithms

3. Applying Dimensionality Reduction

Understand dimensionality reduction on high dimensional data
Implement common dimensionality reduction techniques
Lab: Applying Principal Component Analysis (PCA)

4. Working with TensorFlow Tensors

Gain practical experience with TensorFlow tensors, its creation, manipulating them
Lab: Working with Tensors

5. Comprehend Neural Networks & Activation Functions

Get an overview of neural network architecture and its components
Understand how neural networks learns from data and their role in various machine learning tasks
Understand various activation functions used in neural networks
Lab: Activations functions and basic neural network

6. Build and Train Neural Networks

Learn the process of designing and constructing neural network models
Understand layer configurations, activation functions, optimizers and training procedures
Lab: Implement a neural network using TensorFlow and Keras

Training material provided: Yes (Digital format)

Hands-on Lab: All labs will be conducted using Google Colaboratory. Participants must have a Google Email ID to access the lab environment.

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]]> https://project.bigdatatrunk.com/courses/enhanced-machine-learning/feed/ 0 Machine Learning – Immersive Bootcamp https://project.bigdatatrunk.com/courses/machine-learning-immersive-bootcamp/ https://project.bigdatatrunk.com/courses/machine-learning-immersive-bootcamp/#respond Tue, 02 Jul 2024 07:32:07 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=52684 Join our intensive 5-day Machine Learning Bootcamp designed to equip you with the essential skills and knowledge to excel in the field of data science.

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Overview
Prerequisites
Audience
Curriculum

Description:

Join our intensive 5-day Machine Learning Bootcamp designed to equip you with the essential skills and knowledge to excel in the field of data science. Starting with an introduction to Python and foundational statistical methods, you'll learn to visualize and analyze data effectively. We'll then delve into the core principles of machine learning, covering both theoretical concepts and practical applications. You'll explore key algorithms, including PCA, k-Nearest Neighbors, linear regression, decision trees, and ensemble methods like random forests. By the end of the bootcamp, you'll have a solid understanding of how to formulate and solve real-world problems using advanced machine learning techniques. This bootcamp is perfect for anyone looking to deepen their expertise in data science and machine learning.

Duration: 5 Days

Course Code: BDT347

Learning Objectives

Gain a solid foundation in Python programming and its applications in data science
Develop skills to perform EDA to uncover patterns and insights from data.
Comprehend the significance of machine learning, formulate machine learning problems, and explore supervised and unsupervised learning.
Learn and apply essential machine learning algorithms such as PCA, KNN, linear regression, decision trees, ensemble methods (like Random Forests), SVM, logistic regression, and Naive Bayes.
Understand the concepts of generalization and overfitting, and master the use of training, validation, and testing datasets to develop robust machine learning models.

Basic Programming Knowledge: Familiarity with any programming language like Python is preferred but not mandatory.
Understanding of Mathematics: Basic knowledge of linear algebra, calculus, and probability.
Statistical Concepts: Fundamental understanding of descriptive and inferential statistics.
Eagerness to learn and apply new concepts in data science and machine learning.

This course is suitable for:

Software Developers
Data Scientists
AI/ML Engineers
Tech Enthusiasts

Course Outline:

Data Science Toolkits, Statistical & Exploratory Data Analytics

Introduction to Python
Python for Data science
Math for Machine Learning
Data Visualization in Python
CRISP-DM Framework
Inferential Statistics
Hypothesis Testing
Exploratory Data Analytics

Introduction to Machine Learning

Motivation & Role of Machine learning in computer science & problem solving
Problem Formulation (Classification and Regression)
Paradigms of learning
Supervised Learning
Unsupervised Learning

Fundamentals to Machine Learning

PCA and Dimensionality Reduction
Nearest Neighbours and KNN
Linear Regression
Decision Tree Classifiers
Notion of Generalization and concern of Overfitting
Notion of Training, Validation and Testing

Machine Learning Algorithms

Linear SVM
Logistic Regression
Naive Bayes
Decision Trees
Ensemble Techniques
Random Forests

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]]> https://project.bigdatatrunk.com/courses/machine-learning-immersive-bootcamp/feed/ 0 Apache Airflow For Machine Learning Operations https://project.bigdatatrunk.com/courses/apache-airflow-for-machine-learning-operations/ Wed, 16 Aug 2023 05:07:16 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=42406 The Apache Airflow for Machine Learning Operations course is intended for machine learning engineers interested in leveraging Apache Airflow to generate training, validation, and test sets in a reproducible manner.

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Overview
Prerequisites
Audience
Curriculum

Description:

Empower your machine learning operations with our 'Apache Airflow Training for Machine Learning Operations' course. Tailored for machine learning engineers, this program equips you to create reproducible training sets, build and validate models, and deploy them confidently. Explore the complexities of reproducible CI/CD pipelines in machine learning and how Apache Airflow simplifies batch training workflows using Directed Acyclic Graphs (DAGs). You'll gain a solid understanding of Airflow's foundations, applying them to real-world machine learning challenges, including sentiment prediction in tweet streams. This course offers a hands-on learning approach, with a focus on creating reproducible pipelines with Airflow. Join us to elevate your machine learning operations with Apache Airflow.

Duration: 3 Days

Course Code: BDT291

Learning Objectives:

After this course, you will be able to:

Migrate their Machine Learning training workflows to scalable pipelines in Apache Airflow
Take a raw dataset and a model architecture and be able to take the project to the end deploying it in the cloud
Enforce reusability and modularization of pipelines for easy collaboration.

Although there is no background needed except basic Python knowledge or object-oriented programming experience, any knowledge of Machine Learning can help boost your learning.

People being curious about data engineering.
People who want to learn basic and advanced concepts about Apache Airflow.
People who like hands-on approach.

Course Outline:

The scalable problem of Machine Learning Pipelines

What problems arise when trying to create a Machine Learning model?
The components of a Machine Learning platform
Introducing Apache Airflow
Airflow architecture
How do we represent a Machine Learning Pipeline?
Demo: Our first DAG
Tasks, TaskFlows, and Operators
Demo: First Pipeline
Capstone Lab: Cresting the datasets for training

Creating our Machine Learning Pipeline

Using custom operators
Demo: Creating a Train Operator
Creating TaskGroups vs subDAGs
Sharing data with xCOMs
Branching and Triggers
Sensors and SmartSensors
Demo: Adding a sensor to validate enough new data
Capstone Lab: Adding training, validation and delivery steps to our pipeline

Mastering scheduling

Execution_date, start_date and schedule_interval
Handling non-default schedule_intervals
Demo: Playing with time
Capstone Lab: Using Sensors with a correct schedule_interval

Enabling concurrency and scalability

Abandoning SQLite to PostgreSQL
Executors: Debug, Local, Celery
Concurrency and parallelism
Demo: Concurrency with Celery

Hackathon: Sentiment Prediction from Twitter

Software Required

This Apache Airflow for Machine Learning Operations course is taught using Python > 3.5, Apache Airflow > 2.1, scikit-learn > 1.1, and PyTorch > 1.8. On request, we can provide either a remote VM environment for the class or directions for configuring this environment on your local PCs.

Training material provided: Yes (Digital format)

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]]> Artificial Intelligence And Machine Learning For Non-Programmer https://project.bigdatatrunk.com/courses/artificial-intelligence-and-machine-learning-for-non-programmer/ https://project.bigdatatrunk.com/courses/artificial-intelligence-and-machine-learning-for-non-programmer/#respond Sat, 20 May 2023 06:58:28 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=29509 Embark on an engaging journey into the world of AI and ML basics designed specifically for executives. This course offers a non-technical and enjoyable introduction to Artificial Intelligence and Machine Learning, equipping you with the vocabulary and fundamentals needed to navigate this exciting domain.

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Overview
Prerequisite
Audience
Curriculum

Short Description:

Embark on an engaging journey into the world of AI and ML basics designed specifically for executives. This course offers a non-technical and enjoyable introduction to Artificial Intelligence and Machine Learning, equipping you with the vocabulary and fundamentals needed to navigate this exciting domain. Gain awareness of AI and Machine Learning patterns and their real-world applications. Understand key Machine Learning concepts, including Supervised and Unsupervised learning techniques, and demystify the differences between AI, ML, and DL. Expand your AI vocabulary to grasp techniques such as Classification, Clustering, and Regression. Plus, witness a live ML demo to illustrate tools and next steps. Elevate your executive knowledge with "AI & ML Basics for Executives" training.

Long Description:

This course provides a fun and non-technical introduction to the Artificial Intelligence and Machine Learning. It provides the vocabulary and basics for this exciting new world of Artificial Intelligence and Machine Learning.

Artificial Intelligence and Machine Learning Lecture helps in awareness about AI &Machine Learning patterns and use cases in real world. Along the way, you’ll get anunderstanding of Machine Learning concepts like Supervised and Unsupervised learning techniques and usages. Demystify the difference between AI vs ML vs DL along with usage patterns. You would expand your vocabulary in the AI to understand techniques like Classification, Clustering and Regression. Finally, we would do a ML demo to illustrate few tools and next steps.

Course Code/Duration:

BDT1 / 1 Day

Learning Objectives:

After this course, you will be able to:

Describe Supervised and Unsupervised learning techniques and usages
Compare AI vs ML vs DL
Understand techniques like Classification, Clustering and Regression
Discuss how to identify which kinds of technique to be applied for specific use case
Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python and R etc.
Understand the relation between Data Engineering and Data Science
Understand the Data Science process
Discuss Machine Learning use cases in different domains
Identify when to use or not useMachine Learning
Define how to form a ML team for success
Understand usage of tools through a ML Demo and hands-on labs.

Basic Programming

Anyone interested

Course Outline:

Course Introduction
History and Background of AI and ML
Compare AI vs ML vs DL
Describe Supervised and Unsupervised learning techniques and usages
Machine Learning patterns
- Classification
- Clustering
- Regression
Gartner Hype Cycle for Emerging Technologies Machine Learningofferings in Industry
Discuss Machine Learning use cases in different domains
Understand the Data Science process to apply to ML use cases
Identify the different roles needed for successful ML project
Hands-on: Create account for Microsoft Azure Machine Learning Studio
Demo:ML using Azure ML studio
Demo – ML using Scikit-learn
References and Next steps

Structured Activity/Exercises/Case Studies:

Hands-on 1: Create account for Microsoft Azure Machine Learning Studio
Hands-on 2:ML using Azure ML studio
Hands-on 3:Demo of ML using Scikit-learn

Training material provided:

Yes (Digital format)

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