IBMDeveloperMEA/YPDL-SentimentAnalysis-LR

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IBMDeveloperMEA/YPDL-SentimentAnalysis-LR

IBMDeveloperMEA

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While Deep Learning is a subset of Machine Learning, the prediction methodology in deep learning is different and works similar to how a human brain uses neural pathways to process information & learn from it. In this workshop we will learn about the building blocks of deep learning, neural networks, and how they work. We'll start with Logistic Regression - a simple and basic neural network classification algorithm, having just a one-layer neural network. These are the resources for the first session of Your Path to Deep Learning.

YPDL-Build-a-movie-recommendation-engine-with-TensorFlow

In this tutorial, we are going to build a Restricted Boltzmann Machine using TensorFlow that will give us recommendations based on movies that have been watched already. The datasets we are going to use are acquired from GroupLens and contains movies, users, and movie ratings by these users.

Jupyter Notebook

YPDL-Recurrent-Neural-Networks-using-TensorFlow-Keras

Build a recurrent neural network using TensorFlow and Keras.

Jupyter Notebook

Trusted-AI-Build-Explainable-ML-Models-using-AIX360

Imagine boarding the Titanic in 2021, and you have provided all your details as a passenger to the captain. There is are three people involved, the data scientist, captain and the passenger. Imagine the company who has built Titanic has created a machine ML model to predict the rate of survival of the passengers, in case of a disaster. The job of the data scientist is to make a model that is explainable to the passengers who are not technical, and that they get the answer about the reasons why they may not survive.

Jupyter Notebook

YourPathToDeepLearning

Sequel to our infamous Your Path to AI Series. We are introducing this new advanced level series for audience interested to specialise in Deep Learning. This repository holds all the resources for the series for you to catch up in case you have missed it.

Easily-build-a-custom-language-analysis-model-with-Watson-Knowledge-Studio-NLU

In this tutorial, learn how to use Watson Knowledge Studio to annotate reviews for auto repair facilities. After annotating the reviews, you can then train a machine learning model that can analyze the reviews. The model is able to determine what types of repairs were needed by the vehicle and how satisfied the customer was with the quality of work. By analyzing the reviews associated with a given auto repair shop, you can generate insights about that shop's overall performance to determine what types of repairs they're most (and least) skilled at.

automate-model-building-with-autoai

This repo contains the assets used for a workshop in the MEA Developer Summit 2021 where we use IBM's AutoAI service to build a telco churn predictive machine learning model.

MEA-Regional-Event-Data-AI

These are all the resources for the MEA Regional Event - Data Science & AI for Everyone (https://www.crowdcast.io/e/ddc-mea2021/1) that was a part of the Digital Developer Conference: Data & AI (https://ibm.biz/devcon-ai)

Quick-and-Easy-PredictiveML

In this tutorial, we will use Watson Studio to build a predictive machine learning model with IBM SPSS Modeler and decide whether a bank customer will default on a loan. IBM Cloud Pak® for Data is an interactive, collaborative, cloud-based environment that allows developers and data scientists to work collaboratively, gain insight from data and build machine learning models.

Modernize-ML-Workflow-Integrate-a-Chatbot-Deploy-to-Whatsapp

Now a days businesses also use chatbots to increase productivity and provide a better customer experience. This demo aims to automate the banking experience for customers regarding loan applications using Watson Studio machine learning, Cloud function, and then extend the Watson Assistant chatbot’s capabilities by connecting it to WhatsApp using Twilio. We will be using Twilio’s sandbox to show how this integration works.

Loan-Bank-Transactions-Cognos-Embedded

This repo contains the materials for a workshop that shows how to gains insights and visualize data for a credit score/loan bank transactions use case.

Build-your-AI-Chatbot-with-Zero-Coding

In this workshop, we are going to create a customer service Assistant for a bank. We are looking to automate some of the top questions that are reaching our agents via support chat on our site,in this scenario - people trying to transfer money to friends or family.

Build-your-Machine-Learning-Models-Efficiently-with-SPSS

In this tutorial, we will use IBM Cloud Pak for Data to build a predictive machine learning model with IBM SPSS Modeler and decide whether a bank customer will default on a loan. IBM Cloud Pak for Data is an interactive, collaborative, cloud-based environment that allows developers and data scientists to work collaboratively, gain insight from data and build machine learning models.

Connect-a-secure-Cloudant-database-instance-to-your-web-application

In this workshop you will learn how to connect your IBM Cloudant instance with your application.

DeveloperFestival21

Resources for you from our three-day, virtual conference.

Create-your-first-Smart-Virtual-Assistant-on-IBM-Cloud

In this workshop, we are going to create a customer service Assistant for a bank. We are looking to automate some of the top questions that are reaching our agents via support chat on our site in this scenario - people trying to transfer money to friends or family.

EMEA-Regional-Deploy-your-AI-models-using-Serverless-Containers

This tutorial explains how to use the IBM Cloud Code Engine managed serverless platform system to deploy the Model Asset Exchange (MAX).

Monitor-the-performance-of-your-microservice-application

In this code pattern, we will integrate Instana with a polyglot microservice travel application on OpenShift. Instana is a fully automated Enterprise Observability solution, with infrastructure monitoring and Application Performance Management (APM), designed specifically for the challenges of managing microservice and cloud-native applications. We will generate traffic to the application using Puppeteer and analyze the traffic on the Instana dashboard. The travel application used in this code pattern is a part of the Bee Travels project that focuses on some of the first version services of the application.

Secure-your-single-page-web-app-with-low-code-on-IBM-Cloud

In this Workshop, you’ll learn how with App ID, you can easily protect your Node.js front-end web applications. we will have a simple authentication flow up and running in less than 20 minutes. we will learn how to customise our login page and add several features of App ID.

Logistic-regression-using-TensorFlow

In this tutorial, learn how to create a Jupyter Notebook that contains Python code for defining logistic regression, then use TensorFlow (tf.keras) to implement it. The Notebook runs on IBM Cloud Pak® for Data as a Service on IBM Cloud®. The IBM Cloud Pak for Data platform provides additional support, such as integration with multiple data sources, built-in analytics, Jupyter Notebooks, and machine learning. It also offers scalability by distributing processes across multiple computing resources. You can choose to create assets in Python, Scala, and R, and use open source frameworks (such as TensorFlow) that are already installed on the IBM Cloud Pak for Data as a Service platform.

Jupyter Notebook

Rapid-Machine-Learning-Development-using-Low-Code-Applications

AutoAI is a tool that automates machine learning tasks to ease the tasks of data scientists. It automatically prepares your data for modeling, chooses the best algorithm for your problem, and creates pipelines for the trained models, and it can be run in public clouds and in private clouds, including IBM Cloud Pak for Data. In this workshop we will be focusing on Customer Churn using AutoAI