ML - Big Data Trunk

https://project.bigdatatrunk.com Quality Corporate and Classroom Training in Bay Area CA Thu, 10 Apr 2025 15:25:50 +0000 en-US hourly 1 https://wordpress.org/?v=7.0 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.

The post Enhanced Machine Learning first appeared on Big Data Trunk.

]]>

Deprecated: Creation of dynamic property OMAPI_Elementor_Widget::$base is deprecated in /home2/schooli5/public_html/project/wp-content/plugins/optinmonster/OMAPI/Elementor/Widget.php on line 41

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.

The post Enhanced Machine Learning first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/enhanced-machine-learning/feed/ 0 AI/ML Byte-Sized Series: Machine Learning Introduction https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-machine-learning-introduction/ https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-machine-learning-introduction/#respond Fri, 23 Apr 2021 04:13:32 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22302 A short session indented to understand machine learning, what is involved in doing machine learning development. This session is an initial step to getting started with machine learning development using python.

The post AI/ML Byte-Sized Series: Machine Learning Introduction first appeared on Big Data Trunk.

]]>

Overview
Prerequisites
Audience
Curriculum

Description:

This concise session will provide a clear overview of machine learning. We will discuss various types of machine learning, how machines learn, and the importance of data. We will also examine what is required for effective machine learning development and we’ll explore some interesting use cases. This session is an effective non-technical introduction to machine learning.

Setup:

Because this is an abbreviated session, attendees MUST install Anaconda software https://www.anaconda.com/ and have a basic understanding of using Jupyter Notebook.

Course Code/Duration:

BDT135 / 90 Minutes

Learning Objectives:

Learn about what is machine learning, we will cover the following:

Various types of machine learning
How machines learn
The influence of the quantity and variety of data on machine learning
The application of machine learning to interesting use cases

Basic understanding of python language and pandas library.

This session is designed for anyone wants to learn machine learning.

Course Outline:

Overview of Machine Learning

Types of Machine Learning
How Machines Learn
Importance of Data in Machine Learning
Interesting Use Cases of Machine Learning

Training Material Provided: Yes (Digital Format)

The post AI/ML Byte-Sized Series: Machine Learning Introduction first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-machine-learning-introduction/feed/ 0 AI/ML Byte-Sized Series: Unsupervised Learning https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-unsupervised-learning/ https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-unsupervised-learning/#respond Fri, 23 Apr 2021 03:26:30 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22296 Cluster analysis is a vital component of unsupervised learning and data science. In this short session, we will explore the couple of unsupervised learning techniques – perform data clustering, reduce data dimensionality.

The post AI/ML Byte-Sized Series: Unsupervised Learning first appeared on Big Data Trunk.

]]>

Overview
Prerequisites
Audience
Curriculum

Description:

Cluster analysis is a vital component of unsupervised learning and data science. In this short session, we will explore the couple of unsupervised learning techniques – perform data clustering, reduce data dimensionality.

Setup:

Because this is an abbreviated session, attendees MUST install Anaconda software https://www.anaconda.com/ and have a basic understanding of using Jupyter Notebook.

Course Code/Duration:

BDT114 / 90 Minutes

Learning Objectives:

We will learn the following about the recommendation systems:

Build a model to create clusters from data. Understand the intuition behind what principal component analysis (PCA).
Build a simple PCA model to reduce dimensionality of data

Basic understanding of python language, pandas library and understanding of how to use Juypter Notebook.

This session is designed for anyone who is familiar with basic steps involved in machine learning and are familiar with tools involved in building machine learning models.

Course Outline:

Introduction to Unsupervised Learning

Definition and key concepts of unsupervised learning in machine learning.

Clustering Algorithms

Overview of popular clustering methods (e.g., K-means, Hierarchical clustering) and their applications.

III. Dimensionality Reduction

Techniques such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) for reducing the dimensionality of data.

Real-world Applications and Best Practices

Practical examples of unsupervised learning applications, along with best practices for model evaluation and interpretation.

Training material provided: Yes (Digital format)

The post AI/ML Byte-Sized Series: Unsupervised Learning first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-unsupervised-learning/feed/ 0 AI/ML Byte-Sized Series: Recommendation Systems https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-recommendation-systems/ https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-recommendation-systems/#respond Fri, 23 Apr 2021 01:56:20 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22293 A short course on understanding what are recommendation systems. Understand different types of recommendation systems and the tools used to build them.

The post AI/ML Byte-Sized Series: Recommendation Systems first appeared on Big Data Trunk.

]]>

Overview
Prerequisites
Audience
Curriculum

Description:

A short course on understanding what are recommendation systems. Understand different types of recommendation systems and the tools used to build them.

Setup:

Because this is an abbreviated session, attendees MUST install Anaconda software https://www.anaconda.com/ and have a basic understanding of using Jupyter Notebook.

Course Code/Duration:

BDT120 / 90 Minutes

Learning Objectives:

We will learn the following about the recommendation systems:

Types of recommendation systems
Different metrics that are used to measure recommendation systems
List different ways of creating a recommendation system
Simple example of content-based recommendation system.

Basic understanding of Python

Anyone interested in Machine Learning and Recommendation Systems

Course Outline:

Introduction to Recommendation Systems

Overview of recommendation systems and their role in enhancing user experiences.

Collaborative and Content-Based Filtering

Understanding the principles and implementation of collaborative and content-based recommendation approaches.

III. Hybrid Recommendation Systems

Integration of collaborative and content-based methods for improved recommendation.
accuracy.

Evaluation Metrics and Real-world Applications

Training Material Provided: Yes (Digital Format)

The post AI/ML Byte-Sized Series: Recommendation Systems first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-recommendation-systems/feed/ 0 Creating and Deploying Machine Learning Models on GCP https://project.bigdatatrunk.com/courses/creating-and-deploying-machine-learning-models-on-gcp-scikit-learn-and-tensorflow-models/ https://project.bigdatatrunk.com/courses/creating-and-deploying-machine-learning-models-on-gcp-scikit-learn-and-tensorflow-models/#respond Mon, 21 Dec 2020 02:27:55 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21811 Machine learning has become an integral part of virtually every industry. And being able to create machine learning models and gain insight from data is an invaluable skill. Moreover, being able to deploy these models is imperative.

The post Creating and Deploying Machine Learning Models on GCP first appeared on Big Data Trunk.

]]>

Overview
Prerequisites
Audience
Curriculum

Description:

Unlock the power of machine learning across industries with this comprehensive class. Learn to create machine learning models using Scikit-Learn and delve into deep learning with TensorFlow. Discover the art of model saving and version management, and seamlessly deploy your models using Google Cloud Platform's AI Platform for actionable insights and predictions.

Course Code/Duration:

BDT71 / 1 Day

Learning Objectives:

After this course, you will be able to:

Understand How to Deploy Models on Google Cloud Platform (GCP)
Understand How Machines Learn
Understand Structured and Unstructured Data
Compare AI vs. Machine Learning vs. Deep Learning
Use Common Machine Learning Algorithms
Use Scikit-Learn to Create and Train Machine Learning Models
Use TensorFlow and Keras to Create and Train Deep Learning Models
Use AI Platform on GCP
Use Cloud Storage on GCP
Use AI Platform Notebooks on GCP to Build and Train Scikit-Learn and TensorFlow Machine Learning Models
Use GCP to Deploy Trained Machine Learning Models
Understand the fundamental techniques through Demos and hands-on labs

Python experience
Basic understanding of Machine Learning

This course is designed for Software Architects, Developers, Data Engineer, Data Analyst and Machine Learning Engineer.

Course Outline:

Course Introduction
Compare AI vs ML vs DL
Understanding how machines learn
Structured vs. Unstructured data

Lab:

Installing Anaconda and TensorFlow
Common machine learning algorithms
Using Scikit-Learn to create and train machine learning models
- .fit()
- score()
- .predict()

Lab:

Using scikit-learn to build a linear and a logistic regression model
Saving a Scikit-Learn Model
- pickle (model.pkl)
- joblib (model.joblib)
Using GCP Cloud Storage to Store Saved Models

Lab:

Creating a Cloud Storage bucket on GCP and uploading models
Introducing Keras/TensorFlow
- TensorFlow intro
- Using Keras

Lab:

Using Keras to builda linear regression and a neural network model
Saving a TensorFlow Model
Saving the model in tensorflow format
Storing model in GCP Cloud Storage
Create Different Model Versions for Deployment
Introducing AI Platform
AI Platform Notebooks
AI Platform Models
Deploy Models on GCP

Lab:

Create an AI Platform model resource and version resource Serve Models from GCP

Lab:

Create input data and query deployed model for predictions Next steps

Structured Activity/Exercises/Case Studies:

Installing Anaconda and TensorFlow
Using scikit-learn to build a linear and a logistic regression model
Creating a Cloud Storage bucket on GCP and uploading models
Using Keras to builda linear regression and a neural network model
Create an AI Platform model resource and version resource
Create input data and query deployed model for predictions

Training material provided:

Yes (Digital format)

The post Creating and Deploying Machine Learning Models on GCP first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/creating-and-deploying-machine-learning-models-on-gcp-scikit-learn-and-tensorflow-models/feed/ 0 Machine Learning Is For Everyone (Lecture) https://project.bigdatatrunk.com/courses/machine-learning-is-for-everyone/ https://project.bigdatatrunk.com/courses/machine-learning-is-for-everyone/#respond Sun, 20 Dec 2020 22:27:36 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21770 In this Course understand Easily identifies trends and patterns,No human intervention needed (automation) ,Continuous Improvement,,Handling multi-dimensional and multi-variety data,Wide Applications.

The post Machine Learning Is For Everyone (Lecture) first appeared on Big Data Trunk.

]]>

Overview
Prerequisites
Audience
Curriculum

Description:

In this Course understand Easily identifies trends and patterns,No human intervention needed (automation) ,Continuous Improvement,,Handling multi-dimensional and multi-variety data,Wide Applications.

Long Description:

Unlock the power of Machine Learning in our comprehensive course. Seamlessly identify trends and patterns, all without the need for human intervention through automation. Embrace continuous improvement as you gain proficiency in handling multi-dimensional and multi-variety data, opening doors to a wide array of applications. Our Machine Learning Lecture is your gateway to understanding the real-world applications of AI and Machine Learning, including patterns and use cases. Explore concepts such as Supervised and Unsupervised learning, demystify AI vs ML vs DL, and broaden your AI vocabulary with techniques like Classification, Clustering, and Regression. Join us for a transformative journey that culminates in a hands-on ML demonstration, equipping you with the tools and knowledge for what comes next

Course Code/Duration:

BDT37 / 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 usage of tools through a ML Demo

Familiarity with Java(or a similar object oriented language), XML is required.

Developers,Business Analysts who want to start a career in or wants to learn about the exciting domain of Data Science and Machine Learning, Non-technical professionals who waant to start a career in Machine Learning.

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 Learning offerings in Industry
Demo – ML using Azure ML studio
Demo – ML using Scikit-learn
References and Next steps

Structured Activity/Exercises/Case Studies:

Demo – ML using Azure ML studio
Demo – ML using Scikit-learn

Training material provided:

Yes (Digital format)

The post Machine Learning Is For Everyone (Lecture) first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/machine-learning-is-for-everyone/feed/ 0 How to think like a data scientist https://project.bigdatatrunk.com/courses/how-to-think-like-a-data-scientist/ https://project.bigdatatrunk.com/courses/how-to-think-like-a-data-scientist/#respond Fri, 18 Dec 2020 03:25:35 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21614 This session addresses these questions among others, it discusses the basics of data science and the Data scientist mindset involved in problem solving using data.

The post How to think like a data scientist first appeared on Big Data Trunk.

]]>

Overview
Prerequisites
Audience
Curriculum

Description:

This session addresses these questions among others, it discusses the basics of data science and the Data scientist mindset involved in problem solving using data.

Long Description:

Data scientists are quickly becoming some of the most sought-after professionals. What makes a data scientist’s approach to things so special and why are organizations willing to pay unusually high salaries for their services. What is it about data scientists thinking that is so valuable? What problems lend themselves to data science and how does a data scientist tackle them? This session addresses these questions among others, it discusses the basics of data science and the mindset involved in problem solving using data.

Course Code/Duration:

BDT50 / 2 Days

Learning Objectives:

After this course, you will understand:

What is Data Science?
What does a data scientist do exactly?
Can anyone become a data scientist?
How does an organization derive value from Data Science?
What does a Data Science problem look like? (+ examples)
What tools does a data scientist use to tackle such a problem?
What Data Science methodologies come into play?
What questions does a data scientist ask of the data?
How does a data scientist collaborate with other professionals?
How does a data scientist communicate the results of a project?
How does a data scientist evolve as a professional?
How can one learn more about Data Science work and the Data Science mindset.

Basic Programming knowledge preferred

This course is designed for anyone interested to get started with the domain of Machine Learning and Artificial Intelligence including Data Analysts, Data Engineers, DevOps Engineer, Database Professional, Software Engineers, or Quality Assurance Engineers.

Course Outline:

Course Introduction
Installing Anaconda
Overview of Data Science
The Difference Between Business Analytics (BI), Data Analytics and Data Science
Data Scientist and other related roles
The Data Science Process
Understand the Data Science process to apply to ML use cases
Understand the relation between Data Engineering and Data Science
Data Science use cases in Industry
Identifying a problem and asking good questions
Data Science Toolkit
Essential Python Data Science Libraries
- Numpy
- Pandas
- Matplotlib
Data Exploration
- Describe
- Merging
- Grouping
- Evaluating Features
Effective communication for Data Scientist
Advance as Senior Data Scientist
Do and Don’t for a successful Data Scientist

Structured Activity/Exercises/Case Studies:

Milestone Project 1: Perform Exploratory Data Analysis
Milestone Project 2: Apply machine learning algorithms, select and refine the best model.

Training material provided:

Yes (Digital format)

The post How to think like a data scientist first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/how-to-think-like-a-data-scientist/feed/ 0 AI/ML Byte-Sized Series: Machine Learning Model Optimization https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-machine-learning-model-optimization/ https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-machine-learning-model-optimization/#respond Thu, 17 Dec 2020 07:34:03 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=22144 A short session indented to get started with techniques to optimize machine learning model performance.

The post AI/ML Byte-Sized Series: Machine Learning Model Optimization first appeared on Big Data Trunk.

]]>

Overview
Prerequisite
Audience
Curriculum

Description:

A short session indented to get started with techniques to optimize machine learning model performance.

Setup:

Because this is an abbreviated session, attendees MUST install Anaconda software https://www.anaconda.com/ and have a basic understanding of using Jupyter Notebook.

Course Code/Duration:

BDT104 / 90 Minutes

Learning Objectives:

Learn about what are hyper parameters and how to tune them. We will then build a model and tune the parameters:

Use K-Fold to create diverse test buckets while building the model.
Perform grid search to find the best parameters.

Training material provided: Yes (Digital format)

Learn basic understanding of python language, pandas library and understanding of how to use Juypter Notebook. Also, understanding how to build either a classification models or regression models.

This session is designed for anyone who is familiar with machine learning model development. Understanding of building Classification and/or Regression models will be helpful.

Course Outline:

ML Models
Classification
Regression
Hyperparameters
Optimization of a Model

The post AI/ML Byte-Sized Series: Machine Learning Model Optimization first appeared on Big Data Trunk.

]]> https://project.bigdatatrunk.com/courses/ai-ml-byte-sized-series-machine-learning-model-optimization/feed/ 0 AI & ML Basics For Executives https://project.bigdatatrunk.com/courses/ai-ml-basics-for-executives/ Sat, 12 Dec 2020 04:20:10 +0000 https://www.bigdatatrunk.com/?post_type=lp_course&p=21398 This course provides a fun and non-technical introduction to the Artificial Intelligence and Machine Learning.

The post AI & ML Basics For Executives first appeared on Big Data Trunk.

]]>

Overview
Prerequisite
Audience
Curriculum

Course Code/Duration:

BDT1 / 1 Day

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.

Long Description:

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.

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 knowledge preferred

Developers, Analyst, Managers, Executives

Topic 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)

The post AI & ML Basics For Executives first appeared on Big Data Trunk.

]]>