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| 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 | ||
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| 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 | ||
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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
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 503.7 MB | freecoursewb | 1 week | 20 | 11 | |
| 784.3 MB | freecoursewb | 1 week | 18 | 6 | |
| 2.2 GB | freecoursewb | 1 week | 0 | 0 | |
| 3.7 GB | freecoursewb | 1 week | 0 | 0 | |
| 3.3 GB | freecoursewb | 1 week | 24 | 16 |
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