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Udemy - The Complete Healthcare Artificial Intelligence Course 2021

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Udemy - The Complete Healthcare Artificial Intelligence Course 2021 (Size: 3.9 GB)
  0 0 B
  1. Bonus project.html 102.4 B
  1. Course Structure.mp4 13.9 MB
  1. Course Structure.srt 3.6 KB
  1. Extra Link.html 102.4 B
  1. Introduction to DNA Classifier.mp4 15.7 MB
  1. Introduction to DNA Classifier.srt 1.2 KB
  1. Introduction to the project.srt 1.2 KB
  1. Introduction.mp4 29.4 MB
  1. What is activation function.mp4 7.6 MB
  1. What is activation function.srt 1.5 KB
  1 435.6 KB
  1. Introduction to the project.mp4 16.9 MB
  1. Introduction.srt 4 KB
  2. Bonus project.html 204.8 B
  2. How To Make The Most Out Of This Course.mp4 10 MB
  2. How To Make The Most Out Of This Course.srt 1.5 KB
  2. Importing data and Analysing data.mp4 68.6 MB
  2. Importing data and Analysing data.srt 9 KB
  2. Importing datas and libraries.mp4 52.4 MB
  2. Importing datas and libraries.srt 6 KB
  2. Importing library and data and Preprocessing data.mp4 137.7 MB
  2. Importing library and data and Preprocessing data.srt 16 KB
  2. Importing library and data.mp4 30.8 MB
  2. Thank you.mp4 23.1 MB
  2. Thank you.srt 1.7 KB
  2. What is sigmoid function.mp4 3.3 MB
  2. What is sigmoid function.srt 1.5 KB
  2 283 KB
  10. Feature Scaling.mp4 33.2 MB
  10. Feature Scaling.srt 5.3 KB
  10. How to install Anaconda.mp4 26.2 MB
  10. How to install Anaconda.srt 2.4 KB
  10. Summary of the project.mp4 39 MB
  10. Summary of the project.srt 3.5 KB
  10. Testing accuracy.mp4 31.7 MB
  10. Testing accuracy.srt 4.7 KB
  10.1 Heart_disease_project.ipynb 299.7 KB
  11. Confusion matrix.mp4 69.6 MB
  11. Confusion matrix.srt 8.8 KB
  11. Important terms in Neural Network.mp4 105.9 MB
  11. Important terms in Neural Network.srt 11.1 KB
  11. Model building.mp4 56.1 MB
  11. Model building.srt 7.7 KB
  12. Analysing Results.mp4 108.5 MB
  12. Analysing Results.srt 12 KB
  12. ROC curve.mp4 44.2 MB
  12. ROC curve.srt 6 KB
  12.1 Diabetes_Udemy.ipynb 231 KB
  13. Further improvement.mp4 13.3 MB
  13. Further improvement.srt 4.1 KB
  13. Summary of the project.mp4 10.8 MB
  13. Summary of the project.srt 2.1 KB
  13.1 Taxi_fares_prediction_udemy.ipynb 798.4 KB
  14. Summary of the project.mp4 13.2 MB
  14. Summary of the project.srt 2.1 KB
  2. Important Parameters.mp4 84.4 MB
  2. Important Parameters.srt 6.8 KB
  2. Importing library and data.srt 4.5 KB
  2.1 diabetes.csv 23.3 KB
  3. AI in Healthcare.mp4 6.3 MB
  3. AI in Healthcare.srt 3 KB
  3. Data visualization.mp4 71.2 MB
  3. Data visualization.srt 8.8 KB
  3. Deep feedforward networks.mp4 42.7 MB
  3. Deep feedforward networks.srt 7.4 KB
  3. Fixing missing data.mp4 84.2 MB
  3. Fixing missing data.srt 9.8 KB
  3. Objective of this project.mp4 10.9 MB
  3. Objective of this project.srt 1.8 KB
  3. Showing data.mp4 11.3 MB
  3. Showing data.srt 1.7 KB
  3. Visualizing data.mp4 93 MB
  3. Visualizing data.srt 12.9 KB
  3. What is tanh function.mp4 2.4 MB
  3. What is tanh function.srt 1.1 KB
  3 135 KB
  4. Generating a DNA sequence.mp4 140.9 MB
  4. Generating a DNA sequence.srt 17.2 KB
  4. Handling missing values.mp4 76 MB
  4. Handling missing values.srt 10.4 KB
  4. Importing library and data.mp4 66.5 MB
  4. Importing library and data.srt 7.1 KB
  4. Understanding Machine Learning Algorithm.mp4 84.6 MB
  4. Understanding Machine Learning Algorithm.srt 6.4 KB
  4 273.3 KB
  4. Splitting the dataset into training test and test set.mp4 47.6 MB
  4. Splitting the dataset into training test and test set.srt 5.5 KB
  4. What is Neuron.mp4 7.2 MB
  4. What is Neuron.srt 1.8 KB
  4. What is Rectified Linear Unit function.mp4 4.3 MB
  4. What is Rectified Linear Unit function.srt 1.7 KB
  4.1 Ch3.ClevelandData.xlsx 27.9 KB
  5. Data standardization.mp4 50.2 MB
  5. Data standardization.srt 6.6 KB
  5. Exploratory analysis.mp4 42.1 MB
  5. Exploratory analysis.srt 5.6 KB
  5. Splitting the dataset into training test and test set.mp4 50.4 MB
  5. Splitting the dataset into training test and test set.srt 6.4 KB
  5. Training Neural Network.mp4 45.8 MB
  5. Training Neural Network.srt 5.4 KB
  5. Training model.mp4 102.3 MB
  5. Training model.srt 9.3 KB
  5. Visualizing geolocation data.mp4 151.5 MB
  5. Visualizing geolocation data.srt 16.7 KB
  5. What is Leaky ReLU function.mp4 2.2 MB
  5. What is Leaky ReLU function.srt 819.2 B
  5. What is deep Learning.mp4 18.5 MB
  5 542.1 KB
  5. What is deep Learning.srt 1.4 KB
  6 104.5 KB
  6. A comparison of categorical and binary problem.mp4 93.6 MB
  6. A comparison of categorical and binary problem.srt 9.9 KB
  6. Analysing Data.mp4 41.5 MB
  6. Analysing Data.srt 5.8 KB
  6. Handling missing data in Python.mp4 59.5 MB
  6. Handling missing data in Python.srt 11.2 KB
  6. Make a Prediction.mp4 126.5 MB
  6. Make a Prediction.srt 12.6 KB
  6. Scoring method and results.mp4 146.7 MB
  6. Scoring method and results.srt 12.2 KB
  6. Splitting the data into training, testing, and validation sets.mp4 112.9 MB
  6. Splitting the data into training, testing, and validation sets.srt 10.6 KB
  6. What is ANN.mp4 18.3 MB
  6. What is ANN.srt 4.2 KB
  6. What is The Exponential Linear Unit Function.mp4 1.9 MB
  6. What is The Exponential Linear Unit Function.srt 819.2 B
  7. Data scaling.mp4 65.3 MB
  7. Data scaling.srt 9.8 KB
  7. Handling missing data and anomalies in Python.mp4 111.6 MB
  7. Handling missing data and anomalies in Python.srt 18.5 KB
  7. Model building.srt 5.3 KB
  7. Summary of the project.mp4 8.3 MB
  7 400.8 KB
  7. Model building.mp4 39.1 MB
  7. Summary of the project.srt 1.7 KB
  7. What is The Swish function.mp4 3.7 MB
  7. What is The Swish function.srt 1.9 KB
  7. What is keras.mp4 34.1 MB
  7. What is keras.srt 6.1 KB
  7.1 Breast_cancer_projects.ipynb 369.1 KB
  7.1 DNA CLASSIFICATION.ipynb 41.7 KB
  7.1 Heart Disease Prediction with Neural Networks.ipynb 130.8 KB
  8. Data visualization.mp4 72.5 MB
  8. Data visualization.srt 9.4 KB
  8 481.5 KB
  8. Introduction to Pandas and visualization.mp4 61.1 MB
  8. Introduction to Pandas and visualization.srt 11.3 KB
  8. Model compilation.mp4 30.2 MB
  8. Model compilation.srt 3.8 KB
  8. Temporal features.mp4 30.4 MB
  8. Temporal features.srt 4.4 KB
  8. What is The softmax function.mp4 2.4 MB
  8. What is The softmax function.srt 1.2 KB
  9. Data Preprocessing by Pandas.mp4 60.2 MB
  9. Data Preprocessing by Pandas.srt 9.7 KB
  9. Geolocation features.mp4 92.2 MB
  9. Geolocation features.srt 14.7 KB
  9 66.2 KB
  9. Model training.mp4 14.7 MB
  9. Model training.srt 2 KB
  9. Splitting training set into test set and Evaluating the model.mp4 207.7 MB
  9. Splitting training set into test set and Evaluating the model.srt 24.5 KB
  9. Time to code all the activation functions.mp4 36.9 MB
  9. Time to code all the activation functions.srt 6.8 KB
  9.1 Activation_function.ipynb 2.5 KB
  9.1 Pandas.ipynb 62.3 KB
  TutsNode.com.txt 102.4 B
  [TGx]Downloaded from torrentgalaxy.to .txt 614.4 B
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  ▲ 247 total files

Description


Description

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.

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.

And as a bonus, this course includes one extra big projects for each month.
Who this course is for:

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.

Requirements

There will be no Prerequisites.
Basic knowledge of Python will be good.
But everything will be taught from the round up.

Last Updated 2/2021

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