About Applied Machine Learning - Beginner to Professional Course
Course Curriculm
1. Welcome to the Applied Machine Learning Course
2. Introduction to Data Science and Machine Learning
3. Introduction to the Course
4. Setting up your system
5. Python for Data Science
6. Statistics For Data Science
7. Basics Steps of Machine Learning and EDA
8. Data Manipulation and Visualization
9. Project: EDA - Customer Churn Analysis
10. Share your Learnings
11. Build Your First Predictive Model
12. Evaluation Metrics
13. Build Your First ML Model: k-NN
14. Selecting the Right Model
15. Linear Models
16. Project: Customer Churn Prediction
17. Dimensionality Reduction (Part I)
18. Decision Tree
19. Feature Engineering
20. Share your Learnings
21. Project: NYC Taxi Trip Duration prediction
22. Working with Text Data
23. Naïve Bayes
24. Multiclass and Multilabel
25. Project: Web Page Classification
26. Basics of Ensemble Techniques
27. Advance Ensemble Techniques
28. Project: Ensemble Model on NYC Taxi Trip Duration Prediction
29. Share your Learnings
30. Advance Dimensionality Reduction
31. Support Vector Machine
32. Unsupervised Machine Learning Methods
33 AutoML and Dask
34. Neural Network
35. Model Deployment
36. Interpretability of Machine Learning Models
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Course Curriculm
1. Welcome to the Applied Machine Learning Course
2. Introduction to Data Science and Machine Learning
3. Introduction to the Course
4. Setting up your system
5. Python for Data Science
6. Statistics For Data Science
7. Basics Steps of Machine Learning and EDA
8. Data Manipulation and Visualization
9. Project: EDA - Customer Churn Analysis
10. Share your Learnings
11. Build Your First Predictive Model
12. Evaluation Metrics
13. Build Your First ML Model: k-NN
14. Selecting the Right Model
15. Linear Models
16. Project: Customer Churn Prediction
17. Dimensionality Reduction (Part I)
18. Decision Tree
19. Feature Engineering
20. Share your Learnings
21. Project: NYC Taxi Trip Duration prediction
22. Working with Text Data
23. Naïve Bayes
24. Multiclass and Multilabel
25. Project: Web Page Classification
26. Basics of Ensemble Techniques
27. Advance Ensemble Techniques
28. Project: Ensemble Model on NYC Taxi Trip Duration Prediction
29. Share your Learnings
30. Advance Dimensionality Reduction
31. Support Vector Machine
32. Unsupervised Machine Learning Methods
33 AutoML and Dask
34. Neural Network
35. Model Deployment
36. Interpretability of Machine Learning Models
join👇👇👇
@DeepLearning_AI
.https://courses.analyticsvidhya.com/courses/applied-machine-learning-beginner-to-professional?utm_source=sendinblue&utm_campaign=July_Newsletter_2019&utm_medium=email
666 Free Online Programming & Computer Science Courses You Can Start This July
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.https://www.freecodecamp.org/news/free-coding-courses-july-2019/
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freeCodeCamp.org
660+ Free Online Programming & Computer Science Courses You Can Start This July
By Dhawal Shah Seven years ago, universities like MIT and Stanford first opened up free online courses to the public. Today, more than 900 schools around the world have created thousands of free online courses, popularly known as Massive Open Online ...
Object Detection on Thermal Images
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Medium
Object Detection on Thermal Images
FLIR - thermal dataset
AI Scholar: Human vs Machine Attention in Neural Networks
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Medium
AI Scholar: Human vs Machine Attention in Neural Networks
This research summary is just one of many that are distributed weekly on the AI scholar newsletter. To start receiving the weekly…
Computer Vision: A Study On Different CNN Architectures and their Applications
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.https://medium.com/alumnaiacademy/introduction-to-computer-vision-4fc2a2ba9dc
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Medium
Computer Vision: A Study On Different CNN Architectures and their Applications
Humans are heavily dependent on five senses to interpret the ongoing activities in the world around us. Though each of our senses is…
500+ awesome bookmarks for Data Science & Machine Learning
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Medium
500+ awesome bookmarks for Data Science & Machine Learning
What this i
Landmark Assisted CycleGAN for Cartoon Face Generation
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.https://deepai.org/publication/landmark-assisted-cyclegan-for-cartoon-face-generation
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DeepAI
Landmark Assisted CycleGAN for Cartoon Face Generation
07/02/19 - In this paper, we are interested in generating an cartoon face of a person by
using unpaired training data between real faces and ...
using unpaired training data between real faces and ...
FREE COURSE Intro to TensorFlow for Deep Learning
This course is a practical approach to deep learning for software developers
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.https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187
This course is a practical approach to deep learning for software developers
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.https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187
Udacity
TensorFlow for Deep Learning Training Course | Udacity
Learn online and advance your career with courses in programming, data science, artificial intelligence, digital marketing, and more. Gain in-demand technical skills. Join today!
Advancing Semi-supervised Learning with Unsupervised Data Augmentation
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.https://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html
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.https://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html
Googleblog
Advancing Semi-supervised Learning with Unsupervised Data Augmentation
Video classification with Keras and Deep Learning
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.https://www.pyimagesearch.com/2019/07/15/video-classification-with-keras-and-deep-learning/
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PyImageSearch
Video classification with Keras and Deep Learning - PyImageSearch
In this tutorial, you will learn how to perform video classification using Keras, Python, and Deep Learning.
Adrian_Kaehler,_Gary_Bradski_Learning.pdf
20.9 MB
Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library 1st Edition
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Who This Book Is For
This book contains descriptions, working code examples, and explanations of the
C++ computer vision tools contained in the OpenCV 3.x library. Thus, it should be
helpful to many different kinds of users:
Professionals and entrepreneurs
For practicing professionals who need to rapidly prototype or professionally
implement computer vision systems, the sample code provides a quick frame‐
work with which to start. Our descriptions of the algorithms can quickly teach or
remind the reader how they work. OpenCV 3.x sits on top of a hardware acceler‐
ation layer (HAL) so that implemented algorithms can run efficiently, seamlessly
taking advantage of a variety of hardware platforms.
Students....
Teachers....
Hobbyist....
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@DeepLearning_AI
Who This Book Is For
This book contains descriptions, working code examples, and explanations of the
C++ computer vision tools contained in the OpenCV 3.x library. Thus, it should be
helpful to many different kinds of users:
Professionals and entrepreneurs
For practicing professionals who need to rapidly prototype or professionally
implement computer vision systems, the sample code provides a quick frame‐
work with which to start. Our descriptions of the algorithms can quickly teach or
remind the reader how they work. OpenCV 3.x sits on top of a hardware acceler‐
ation layer (HAL) so that implemented algorithms can run efficiently, seamlessly
taking advantage of a variety of hardware platforms.
Students....
Teachers....
Hobbyist....
The Best Machine Learning Research of 2019 So Far - ODSC - Open Data Science - Medium
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.https://medium.com/@ODSC/the-best-machine-learning-research-of-2019-so-far-954120947794
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Medium
The Best Machine Learning Research of 2019 So Far
The uses of machine learning are expanding rapidly. Already in 2019, significant research has been done in exploring new vistas for the use…
Everything You Need to Know About Autoencoders in TensorFlow
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Medium
Everything You Need to Know About Autoencoders in TensorFlow
From theory to implementation in TensorFlow
2D or 3D? A Simple Comparison of Convolutional Neural Networks for Automatic Segmentation of Cardiac Imaging
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Medium
2D or 3D? A Simple Comparison of Convolutional Neural Networks for Automatic Segmentation of Cardiac Imaging
Convolutional neural networks (CNNs) have shown promise for a multitude of computer vision tasks. Among these applications is automatic…
Deepfakes, FaceGANS, and Synthetic Data: Welcome to the Reality Illusion of 2020
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Medium
Deepfakes, FaceGANS, and Synthetic Data: Welcome to the Reality Illusion of 2020
Two weeks ago, I attended CVPR, the world’s largest international artificial intelligence conference on computer vision to date. Aside…
The 5 Feature Selection Algorithms every Data Scientist should know
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