AI for Healthcare Course

Do you want to explore the world of deep learning, machine learning, and artificial intelligence? If Yes, then this Artificial Intelligence course is for you. In this course, you will learn to create AI models for multiple healthcare applications. Furthermore, as part of the AI for Healthcare course online, you will master tools such as Pandas, Matplotlib, Support Vector Machines, Markov Models, NLP, Keras, etc.

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AI for Healthcare Training Course Online – Overview

Learn the concepts of artificial intelligence and know how it can support the healthcare domain. In this AI for healthcare course, you will learn to build, integrate, and evaluate predictive models that enable you to make enhanced medical decisions and transform patient outcomes. If you are willing to enhance your artificial intelligence skills and apply them in the healthcare domain, then register for this course now.

AI Healthcare Course Online – Key Features

  • Trusted content.
  • Re-learn for free anytime in a year.
  • Rigorous assignments and assessments.
  • Learn at your own pace.
  • Mandatory feedback sessions.
  • Mock-interviews.
  • Hands-on real-time experience.
  • Free mentorship.
  • Live chat for instant solutions.
  • Job ready employees post-training.
  • End-to-end training.
  • Download the certificate after the course.

AI in Healthcare Program Course Online – Training Options

With a projected CAGR of 48%, the global market size of AI in the Healthcare domain is likely to grow from USD 6.1 billion to USD 64.11 billion in the period 2021-2027.

Designation
Annual Salary
Hiring Companies
Job Wise Benefits
Designation
Artificial Intelligence Engineer

UK
Hiring Companies

AI in Healthcare Program Course Online – Training Options

Self-Paced Learning

£ 1200

  • 1-year access to the AI in Healthcare course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Access to the class recordings.
  • Assistance and support.
  • Download certification.
  • Free mentorship.

Online Boot Camp

£ 1000

  • Everything in Self-paced learning +
  • On-spot doubt clarification.
  • Interactive training sessions.
  • Sessions on the capstone project.
  • Live, online classroom training.
  • Mock-interviews.

Corporate Training

Customized to your team’s needs

  • 1-year access to the AI in Healthcare course content.
  • 1 capstone project.
  • Multiple assessments.
  • Continuous feedback sessions.
  • Class recordings.
  • Assistance and support.
  • Certification after the course.

AI in Healthcare Training Course Online – Curriculum

Eligibility

Individuals who are interested to make their career in artificial intelligence or working professionals who are willing to switch their career domain or make a career in AI can join this artificial intelligence in healthcare course online.

Pre-requisites

There are no prerequisites for this AI for healthcare training course online. However, it is beneficial to have basic or foundational knowledge in Python programming.

Course Content

  • Course Structure
  • How To Make The Most Out Of This Course
  • AI in Healthcare
  • What is Neuron
  • What is Deep Learning
  • What is ANN
  • What is keras
  • Introduction to Pandas Part 1
  • Introduction to Pandas Part 2
  • Data Visualization with Pandas
  • Data Preprocessing by Pandas
  • How to install Anaconda
  • Important terms in Neural Network
  • What is activation function
  • What is sigmoid function
  • What is tanh function
  • What is Rectified Linear Unit function
  • What is Leaky ReLU function
  • What is The Exponential Linear Unit Function
  • What is The Swish function
  • What is The softmax function
  • Time to code all the activation functions
  • Introduction to DNA Classifier
  • Importing library and data
  • Showing data
  • Generating a DNA sequence
  • Splitting the dataset into training test and test set
  • Scoring method and results
  • Summary of the project
  • Introduction to the project
  • Important Parameters
  • Objective of this project
  • Importing library and data
  • Exploratory analysis
  • Handling missing data in Python
  • Data scaling
  • Data visualization
  • Splitting training set into test set and Evaluating the model
  • Summary of the project
  • Introduction
  • Importing data and Analysing data
  • Fixing missing data
  • Splitting the dataset into training test and test set
  • Training Neural Network
  • A comparison of categorical and binary problem
  • Summary of the project
  • Introduction to the project
  • Importing library and data and Preprocessing data
  • Data visualization
  • Understanding Machine Learning Algorithm
  • Training model
  • Make a Prediction
  • Summary of the project
  • Introduction
  • Importing datas and libraries
  • Visualizing data
  • Handling missing values
  • Data standardization
  • Splitting the data into training, testing, and validation sets
  • Model building
  • Model compilation
  • Model training
  • Testing accuracy
  • Confusion matrix
  • ROC curve
  • Further improvement
  • Summary of the project
  • Introduction to the project
  • Problem Analysis
  • Importing library and data
  • Analysing Data
  • Preprocessing data
  • Removing missing data
  • Creating training, test and validation data
  • Visualizing data
  • Building a random model
  • Confusion, Precision and Recall Matrix
  • One-hot Encoding
  • Response Encoding- Theory
  • Response Encoding- Implementation Part 1
  • Response Encoding- Implementation Part 2
  • Response Encoding- Implementation Part 3
  • Evaluating variation column Part 1
  • Evaluating variation column Part 2
  • Evaluating text column Part 1
  • Evaluating text column Part 2
  • Data Preparation for machine learning model
  • Combine all 3 feature together
  • Naive Bayes- Implementation Part 1
  • Naive Bayes- Implementation Part 2
  • K Nearest Neighbour Classification Implementation
  • Logistic Regression Implementation
  • Logistic Regression Implementation without balancing data Part 1
  • Logistic Regression Implementation without balancing data Part 2
  • Linear Support Vector Machines
  • Fixing mistakes (Please watch)
  • RF with Response Coding Implementation Part 1
  • RF with Response Coding Implementation Part 2
  • Random Forest Classifier Implementation
  • Stacking model Implementation
  • Maximum voting Classifier
  • Extra Link
  • Thank you
  • Introduction to the project
  • Bonus project dataset
  • Deep feedforward networks
  • Importing library and data
  • Visualizing geolocation data
  • Analysing Data
  • Handling missing data and anomalies in Python
  • Temporal features
  • Geolocation features
  • Feature Scaling
  • Model building
  • Analysing Results
  • Summary of the project

Artificial Intelligence in Healthcare Training Program Online – FAQs

The natural language processing part of artificial intelligence is mostly used in the healthcare industry as it enables us to understand and classify clinical documentation. NLP systems allow you to acquire better results for patients through the deep analysis of unstructured clinical notes on patients, improving methods, deriving incredible insights, etc.

AI will play a big role in the healthcare domain by assisting and enhancing the surgery processes, but AI will not take over from surgeons. However, researchers say that the AI-based robotic arm may help in performing an ultrasound that allows surgeons to identify organs or any tumor type during the operation process.

The common applications of artificial intelligence in the healthcare domain are as follows

  • Managing medical records and other data
  • Performing repetitive jobs
  • Treatment design
  • Digital consultation
  • Health monitoring

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