Deploying AI Machine Learning Models for Business - Python

Deploying AI & Machine Learning Models for Business | Python, available at $74.99, has an average rating of 4.5, with 54 lectures, 1 quizzes, based on 1640 reviews, and has 8694 subscribers.

You will learn about How to synchronize the versatility of DevOps & Machine Learning Master Docker , Docker Files, Docker Applications & Docker Containers (DevOps) Flask Basics & Application Program Interface (API) Build & Deploy a Random Forest Model Build a Text based (Natural Language Processing : NLP ) CLUSTERING (KMeans) Model and expose it as an API Build an API which will run a Deep Learning Model (Convolutional Neural Network : CNN) Model for Image Recognition & Classification This course is ideal for individuals who are Anyone willing to venture into the realm of data science or Anyone who would be interested in deploying a Data Science Solution, can be Regression, NLP or even Deep Learning Models It is particularly useful for Anyone willing to venture into the realm of data science or Anyone who would be interested in deploying a Data Science Solution, can be Regression, NLP or even Deep Learning Models.

Enroll now: Deploying AI & Machine Learning Models for Business | Python

Summary

Title: Deploying AI & Machine Learning Models for Business | Python

Price: $74.99

Average Rating: 4.5

Number of Lectures: 54

Number of Quizzes: 1

Number of Published Lectures: 54

Number of Published Quizzes: 1

Number of Curriculum Items: 57

Number of Published Curriculum Objects: 57

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

How to synchronize the versatility of DevOps & Machine Learning

Master Docker , Docker Files, Docker Applications & Docker Containers (DevOps)

Flask Basics & Application Program Interface (API)

Build & Deploy a Random Forest Model

Build a Text based (Natural Language Processing : NLP ) CLUSTERING (KMeans) Model and expose it as an API

Build an API which will run a Deep Learning Model (Convolutional Neural Network : CNN) Model for Image Recognition & Classification

Who Should Attend

Anyone willing to venture into the realm of data science

Anyone who would be interested in deploying a Data Science Solution, can be Regression, NLP or even Deep Learning Models

Target Audiences

Anyone willing to venture into the realm of data science

Anyone who would be interested in deploying a Data Science Solution, can be Regression, NLP or even Deep Learning Models

Machine Learning, as we know it is the new buzz word in the industry today. This is practiced in every sector of business imaginable to provide data-driven solutions to complex business problems. This poses the challenge of deploying the solution, built by the Machine Learning technique so that it can be used across the intended Business Unit and not operated in silos.

This is an extensive and well-thought course created & designed by UNP’s elite team of Data Scientists from around the world to focus on the challenges that are being faced by Data Scientists and Computational Solution Architects across the industry  which is summarized the below  sentence :

“I HAVE THE MACHINE LEARNING MODEL, IT IS WORKING AS EXPECTED !! NOW, WHAT ?????” 

This course will help you create a solid foundation of the essential topics of data science along with a solid foundation of deploying those created solutions through Docker containers which eventually will expose your model as a service (API) which can be used by all who wish for it.

At the end of this course, you will be able to:

Learn about Docker, Docker Files, Docker Containers

Learn Flask Basics & Application Program Interface (API)

Build a Random Forest Model and deploy it.

Build a Natural Language Processing based Test Clustering Model (K-Means) and visualize it.

Build an API for Image Processing and Recognition with a Deep Learning Model under the hood (Convolutional Neural Network: CNN)

 This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications and most importantly deploying them.

Course Curriculum

Chapter 1: Course Overview

Lecture 1: Introduction

Lecture 2: I have a model. Now what?

Lecture 3: Skills Checklist

Lecture 4: Learning Goals

Chapter 2: Docker basics

Lecture 1: Why docker?

Lecture 2: What are docker containers?

Lecture 3: Importance of docker containers in machine learning

Lecture 4: Where devops meets data science

Lecture 5: Summary

Chapter 3: Flask basics

Lecture 1: Introduction

Lecture 2: Setting up a Flask Project

Lecture 3: Simple Flask API to add two numbers

Lecture 4: Taking user input with GET requests

Lecture 5: POST request with Flask

Lecture 6: Using Flask in the context of Machine Learning

Chapter 4: Exposing a Random Forest Machine Learning service as an API

Lecture 1: Introduction

Lecture 2: API & Dataset Overview

Lecture 3: Training the Random Forest model

Lecture 4: Pickling the Random Forest model

Lecture 5: Exposing the Random Forest model as a Flask API

Lecture 6: Testing the API model

Lecture 7: Providing file input to Flask API

Lecture 8: Flasgger for autogenerating UI

Lecture 9: Summary

Chapter 5: Writing and building the Dockerfile

Lecture 1: Introduction

Lecture 2: Base Image & FROM command

Lecture 3: COPY and EXPOSE commands

Lecture 4: WORKDIR, RUN and CMD commands

Lecture 5: Preparing the flask scripts for dockerizing

Lecture 6: Writing the Dockerfile

Lecture 7: Building the docker image

Lecture 8: Running the Random Forest model on Docker

Chapter 6: Building a production grade Docker application

Lecture 1: Introduction

Lecture 2: Overall Architecture

Lecture 3: Configuring the WSGI file

Lecture 4: Writing a production grade Dockerfile

Lecture 5: Running and debugging a docker container in production

Chapter 7: Building NLP based Text Clustering application

Lecture 1: Introduction

Lecture 2: Stemming & Lemmatization for cleaner text

Lecture 3: Converting unstructured to structured data

Lecture 4: KMeans Clustering

Lecture 5: Preparing the excel output

Lecture 6: Making the output Downloadable

Lecture 7: Finding top keywords for kmeans clusters

Lecture 8: Final output with charts

Lecture 9: Summary

Chapter 8: API for image recognition with deep learning

Lecture 1: Introduction

Lecture 2: Visualizing the input images

Lecture 3: Preparing the input images

Lecture 4: Building the deep learning model

Lecture 5: Training and saving the trained deep learning model

Lecture 6: Generating test images

Lecture 7: Flask API wrapper for making predictions

Lecture 8: Summary

Instructors

UNP United Network of Professionals
Publishing top-notch data science learning materials

Rating Distribution

1 stars: 28 votes

2 stars: 47 votes

3 stars: 220 votes

4 stars: 613 votes

5 stars: 732 votes

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