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The Complete Healthcare Artificial Intelligence Course 2024

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The Complete Healthcare Artificial Intelligence Course 2024, available at $69.99, has an average rating of 4.13, with 172 lectures, based on 222 reviews, and has 1754 subscribers.

You will learn about Pandas. Matplotlib. Sigmoid activation function. Tanh activation function. ReLU activation function. Leaky Relu activation function. Exponential Linear Unit activation function. Swish activation function. Markov models. Support Vector Machines Other common classifiers Import data from the UCI repository. Convert text input to numerical data. Build and train classification algorithms. Compare and contrast classification machine learning. Building the AI. Machine learning and deep learning model based on the given data with high accuracy. RF with Response Coding. Maximum voting Classifier. Stacking model. Random Forest Classifier. One-hot Encoding. NLP (Natural Language Processing) NLTK (Natural Language Toolkit) Logistic Regression. Naive Bayes Response Encoding Linear Support Vector Machines Geolocation Features. Handling Missing Data And Anomalies in Python. Data standardization. Temporal Features. Seaborn Deep Learning. Keras. Google Colab . Anaconda. Jupiter Notebook. This course is ideal for individuals who are Anyone interested in Machine Learning. or Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence. or Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or Any people who want to create added value to the local hospital by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. or Any people who want to work in healthcare field as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or Any people who want to work in a Taxi Company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. It is particularly useful for Anyone interested in Machine Learning. or Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence. or Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets. or Any students in college who want to start a career in Data Science or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Any people who are not satisfied with their job and who want to become a Data Scientist. or Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or Any people who want to create added value to the local hospital by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. or Any people who want to work in healthcare field as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer. or Any people who want to work in a Taxi Company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.

Enroll now: The Complete Healthcare Artificial Intelligence Course 2024

Summary

Title: The Complete Healthcare Artificial Intelligence Course 2024

Price: $69.99

Average Rating: 4.13

Number of Lectures: 172

Number of Published Lectures: 172

Number of Curriculum Items: 172

Number of Published Curriculum Objects: 172

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

  • Pandas.
  • Matplotlib.
  • Sigmoid activation function.
  • Tanh activation function.
  • ReLU activation function.
  • Leaky Relu activation function.
  • Exponential Linear Unit activation function.
  • Swish activation function.
  • Markov models.
  • Support Vector Machines
  • Other common classifiers
  • Import data from the UCI repository.
  • Convert text input to numerical data.
  • Build and train classification algorithms.
  • Compare and contrast classification machine learning.
  • Building the AI.
  • Machine learning and deep learning model based on the given data with high accuracy.
  • RF with Response Coding.
  • Maximum voting Classifier.
  • Stacking model.
  • Random Forest Classifier.
  • One-hot Encoding.
  • NLP (Natural Language Processing)
  • NLTK (Natural Language Toolkit)
  • Logistic Regression.
  • Naive Bayes
  • Response Encoding
  • Linear Support Vector Machines
  • Geolocation Features.
  • Handling Missing Data And Anomalies in Python.
  • Data standardization.
  • Temporal Features.
  • Seaborn
  • Deep Learning.
  • Keras.
  • Google Colab .
  • Anaconda.
  • Jupiter Notebook.

Who Should Attend

  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science
  • Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
  • Any people who want to create added value to the local hospital by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools.
  • Any people who want to work in healthcare field as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
  • Any people who want to work in a Taxi Company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.

Target Audiences

  • Anyone interested in Machine Learning.
  • Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
  • Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning, Deep Learning, Artificial Intelligence.
  • Any people who are not that comfortable with coding but who are interested in Machine Learning, Deep Learning, Artificial Intelligence and want to apply it easily on datasets.
  • Any students in college who want to start a career in Data Science
  • Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.
  • Any people who are not satisfied with their job and who want to become a Data Scientist.
  • Any people who want to create added value to their business by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools. Any people who want to work in a Car company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
  • Any people who want to create added value to the local hospital by using powerful Machine Learning, Artificial Intelligence and Deep Learning tools.
  • Any people who want to work in healthcare field as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.
  • Any people who want to work in a Taxi Company as a Data Scientist, Machine Learning, Deep Learning and Artificial Intelligence engineer.

Interested in the field of Machine Learning, Deep Learning and Artificial Intelligence? Then this course is for you!

This course has been designed by a software engineer. I hope with my experience and knowledge I did gain throughout years, I can share my knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

I will walk you step-by-step into the Machine Learning, Artificial Intelligence and Deep Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time, we dive deep into Machine Learning, Deep Learning and Artificial Intelligence . Throughout the brand new version of the course we cover tons of tools and technologies including:

  • Deep Learning.

  • Google Colab

  • Anaconda

  • Jupiter Notebook

  • Artificial Intelligent In Healthcare.

  • Artificial Neural Network.

  • Neuron.

  • Activation Function.

  • Keras.

  • Pandas.

  • Seaborn.

  • Feature scaling.

  • Matplotlib.

  • Generating a DNA Sequence.

  • Data Pre-processing.

  • Sigmoid Function.

  • Tanh Function.

  • ReLU Function.

  • Leaky Relu Function.

  • Exponential Linear Unit Function.

  • Swish function.

  • Markov Models.

  • K-Nearest Neighbors Algorithms (KNN).

  • Support Vector Machines (SVM).

  • Importing library and data.

  • Deep feedforward networks.

  • Analysing Data.

  • Exploratory Analysis.

  • Handling Missing Data And Anomalies in Python.

  • Data standardization.

  • Temporal Features.

  • Geolocation Features.

  • Data Scaling.

  • Data Visualization.

  • Visualizing Geolocation Data.

  • Understanding Machine Learning Algorithm.

  • Splitting Data into Training Set and Test Set.

  • Training Neural Network.

  • Model building.

  • Analysing Results.

  • Model compilation.

  • A Comparison Of Categorical And Binary Problem.

  • Make a Prediction.

  • Testing Accuracy.

  • Confusion Matrix.

  • ROC Curve.

  • One-hot Encoding.

  • NLP (Natural Language Processing).

  • NLTK (Natural Language Toolkit).

  • Logistic Regression.

  • Naive Bayes.

  • Response Encoding.

  • Linear Support Vector Machines.

  • RF with Response Coding.

  • Random Forest Classifier.

  • Stacking model.

  • Maximum voting Classifier.

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are five big projects on healthcare problems and one small project to practice. These projects are listed below:

  • Predicting Taxi Fares in New York City

  • DNA Classification Project.

  • Heart Disease Classification Project.

  • Diagnosing Coronary Artery Disease Project.

  • Breast Cancer Detection Project.

  • Predicting Diabetes with Multilayer Perceptrons Project.

  • Iris Flower.

  • Medical Treatment Project.

Course Curriculum

Chapter 1: Introduction (UPDATED 2024)

Lecture 1: Course structure

Lecture 2: How to make the most out of this course

Lecture 3: Introduction to AI in healthcare

Lecture 4: Basic concept of machine learning

Lecture 5: Basic of supervised learning, unsupervised and reinforcement learning

Lecture 6: What is sklearn?

Lecture 7: What is pandas

Lecture 8: What is matplotlib?

Lecture 9: What is standardization?

Chapter 2: Implementation of breast cancer detection (UPDATED 2024)

Lecture 1: What is breast cancer?

Lecture 2: What is classification report?

Lecture 3: What is logistic regression

Lecture 4: What is confusion matrix

Lecture 5: what is mplot3d

Lecture 6: what is mpatches

Lecture 7: what is train test split?

Lecture 8: Implementation of breast cancer detection Part 1

Lecture 9: Implementation of breast cancer detection Final Part

Chapter 3: Implementation of diabetes detection (UPDATED 2024)

Lecture 1: What is diabetes

Lecture 2: What is artificial neural network?

Lecture 3: What is perceptron?

Lecture 4: what does pre-processing mean?

Lecture 5: How to handle missing values

Lecture 6: What is seaborn?

Lecture 7: Implementation of diabetes detection Part 1

Lecture 8: Implementation of diabetes detection Part 2

Lecture 9: Implementation of diabetes detection Part 3

Lecture 10: Implementation of diabetes detection Part 4

Lecture 11: Implementation of diabetes detection Part 5

Lecture 12: Implementation of diabetes detection Part 6

Lecture 13: Implementation of diabetes detection final Part

Chapter 4: Implementation of DNA classification and Heart disease detection (UPDATED 2024)

Lecture 1: Introduction to DNA sequences

Lecture 2: Introduction to DNA classification

Lecture 3: What is Decision Tree?

Lecture 4: What is support vector machine?

Lecture 5: What is RandomForestClassifier

Lecture 6: What is Radial Basis Function

Lecture 7: What is K-nearest Neighbors

Lecture 8: What is AdaBoostClassifier

Lecture 9: What is MLPClassifier?

Lecture 10: Implementation of DNA classification Part 1

Lecture 11: Implementation of DNA classification Part 2

Lecture 12: Implementation of DNA classification Final Part

Lecture 13: What is Heart disease?

Lecture 14: Implementation of Heart disease detection Part 1

Lecture 15: Implementation of Heart Disease Final Part

Chapter 5: Discharge statuses detection for emergency department patients (UPDATED 2024)

Lecture 1: What is ED (Emergency department ) patients?

Lecture 2: what is discharge statuses for ED patients?

Lecture 3: What is one-hot-encoding

Lecture 4: Discharge statuses detection for ED patients implementation Part 1

Lecture 5: Discharge statuses detection for ED patients implementation Part 2

Lecture 6: Discharge statuses detection for ED patients implementation Part 3

Lecture 7: Discharge statuses detection for ED patients implementation Part 4

Lecture 8: Discharge statuses detection for ED patients implementation Part 5

Lecture 9: Discharge statuses detection for ED patients implementation Part 6

Lecture 10: Discharge statuses detection for ED patients implementation Part 7

Chapter 6: Introduction (OLD Content)

Lecture 1: Course Structure

Lecture 2: How To Make The Most Out Of This Course

Lecture 3: AI in Healthcare

Lecture 4: What is Neuron

Lecture 5: What is Deep Learning

Lecture 6: What is ANN

Lecture 7: What is keras

Lecture 8: Introduction to Pandas Part 1

Lecture 9: Introduction to Pandas Part 2

Lecture 10: Data Visualization with Pandas

Lecture 11: Data Preprocessing by Pandas

Lecture 12: How to install Anaconda

Lecture 13: Important terms in Neural Network

Chapter 7: Activation function (OLD Content)

Lecture 1: What is activation function

Lecture 2: What is sigmoid function

Lecture 3: What is tanh function

Lecture 4: What is Rectified Linear Unit function

Lecture 5: What is Leaky ReLU function

Lecture 6: What is The Exponential Linear Unit Function

Lecture 7: What is The Swish function

Lecture 8: What is The softmax function

Lecture 9: Time to code all the activation functions

Chapter 8: DNA Classification Project (OLD Content)

Lecture 1: Introduction to DNA Classifier

Lecture 2: Importing library and data

Lecture 3: Showing data

Lecture 4: Generating a DNA sequence

Lecture 5: Splitting the dataset into training test and test set

Lecture 6: Scoring method and results

Lecture 7: Summary of the project

Chapter 9: Heart Disease Classification Project (OLD Content)

Lecture 1: Introduction to the project

Lecture 2: Important Parameters

Lecture 3: Objective of this project

Lecture 4: Importing library and data

Lecture 5: Exploratory analysis

Lecture 6: Handling missing data in Python

Instructors

Rating Distribution

  • 1 stars: 19 votes
  • 2 stars: 10 votes
  • 3 stars: 20 votes
  • 4 stars: 53 votes
  • 5 stars: 120 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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