Course Overview

What are the learning objectives?

Objectives

To understand topics such as basic machine learning terminology and processes
To understand how to implement machine learning to solve your engineering problems using the Python language,

What syllabus are included in this program?

Basic Machine Learning Terminology
Linear Algebra with Python using Numpy and Pandas
Probability Theory and Statistics with Python using Numpy and Pandas
Feature Engineering
Unsupervised Learning
Supervised Learning
Feedforward Neural Networks
Convolutional and Recurrent Neutral Networks
Natural Language Processing, Part 1
Natural Language Processing, Part 2
Practical Applications
Web Development

Which topics are included?

Topics covered are

Machine Learning and Artificial Intelligence
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Building a Machine Learning System
Evaluating a Machine Learning System
Linear Algebra Review
Introduction to Anaconda
Introduction to Pandas
Introduction to Numpy
Linear Algebra using Numpy
Data Plotting in Python
Statistics
Probability and Random Variables
Useful Probability Distributions
Derivatives
Data Loading and Manipulation using Pandas and Numpy
Working on Images
Features and Feature Vectors
One-hot Encoding
Feature Normalization
Clustering using K-means Algorithm
K-Means Implementation
Clustering using Expectation-Maximization
Association Rules and Recommender Systems
Classification
Regrression
The Perceptron
The Gradient Descent Algorithm
Multi-Layer Perceptrol
Deep Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Problems Solved by Natural Language Processing
Text Preprocessing
Regular Expressions
Discrete Features
Word Embeddings
Part of Speech Tagging
Text Classification using Naïve Bayes
Text Classification using Neural Networks
Industrial Knowledge Representation using Decision Trees
Industrial Fault Diagnosis using Feedforward Neural Networks
Sound Classification using Feedforward Neural Networks
Image Classification using Convolutional Neural Networks
Machine Translation and Chatbots using Recurrent Neural Networks
Use of Flask
Integrating machine learning models with Flask
Deploying applications to a Web Server

Machine learning is undoubtedly one of the most exciting technologies in recent times. Both large and small companies are embracing it with tremendous results. If you are keen to harness machine learning technologies in your engineering work, become a better problem solver, or are perhaps considering a career in machine learning, then this program is for you.

There are many programs out there, but this one focuses on engineering and industrial applications.

The best way to learn the technologies is to work through practical examples of machine learning in a systematic way. Two types of machine learning will be tackled in this program: supervised learning and unsupervised learning.

Supervised learning is where we learn the relationship of given inputs to a set of outputs. For example, different sensor inputs for a process plant can predict the likelihood of a breakdown of a pump or the requirement for maintenance on an item of equipment. You already know how to classify the earlier input data to previous breakdowns of the pump; so, you want to use new input data to predict this event so that you can act before it actually happens. Algorithms that you will learn about here include linear regression, logistic regression, discriminant analysis, decision trees, Naïve Bayes, support vector machines, and random forests.

Unsupervised learning occurs when there is no pre-defined relationship between input data and an output variable. An example here would be to take sensor data from hundreds of similar industrial plants, then ask the algorithm to find patterns and classify the data. You want your algorithm to find any patterns and do the classification of the data for you. Algorithms that you will learn about here include K-means clustering and Gaussian mixture models.

Labdox is a platform to help learners acquire core engineering and entrepreneurial skills in emerging technologies. Labdox provides a one-stop place for validated and industry-mapped courses from around the globe. Our mission is to help learners acquire skills that matter.

Online

Rs 0

Online

1. A laptop and internet connection

Duration: 5-8 Hours
Schedule: Online
Level: Beginner
Rs: 0

Practical Machine Learning Using Python for Engineers & Technicians

Thanks for enrolling for the course. We will send the enrollment details shortly to your registered email id.

Thanks for applying for the enrollment. We will get back to you as soon as we receive the confirmation from the course provider
- Disclaimer: The enrollment is subject to confirmation from course provider

Thanks for enrolling for the course. We will send the enrollment details shortly to your registered email id

Thanks for enrolling for the course. We will send the enrollment details shortly to your registered email id

Course Overview

What are the learning objectives?

What syllabus are included in this program?

Which topics are included?

Course URL

Practical Machine Learning Using Python for Engineers & Technicians

Thanks for enrolling for the course. We will send the enrollment details shortly to your registered email id.

Thanks for applying for the enrollment. We will get back to you as soon as we receive the confirmation from the course provider - Disclaimer: The enrollment is subject to confirmation from course provider

Thanks for enrolling for the course. We will send the enrollment details shortly to your registered email id

Thanks for enrolling for the course. We will send the enrollment details shortly to your registered email id

Course Overview

What are the learning objectives?

What syllabus are included in this program?

Which topics are included?

Course URL

Thanks for applying for the enrollment. We will get back to you as soon as we receive the confirmation from the course provider
- Disclaimer: The enrollment is subject to confirmation from course provider