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[FreeTutorials.Eu] [UDEMY] Projects in Machine Learning Beginner To Professional - FTU]

[FreeTutorials.Eu] [UDEMY] Projects in Machine Learning Beginner To Professional - FTU]

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Description




A complete guide to master machine learning concepts and create real world ML solutions



Created by Eduonix Learning Solutions, Eduonix-Tech ., Samy

Last updated 10/2018

English

Subtitle: English

Source: https://www.udemy.com/machine-learning-for-absolute-beginners/



What you'll learn



-Learn core concepts of Machine Learning

-Learn about differnt types of machine learning algorithms

-Build real world projects using Supervised and Unsupervised learning algorithms

-Learn to implement neural networks



Requirements



-Basic knolwedge of Python is required to compile and run the examples

-Basic knolwedge of mathematics is assumed



Description



Update: This course has been updated to include 9 projects that will give you a real-world experience with different concepts of Machine Learning. Keep an eye out for more projects that will be added to this course in the future!



If you’ve ever wanted Jetsons to be real, well we aren’t that far off from a future like that. If you’ve ever chatted with automated robots, then you’ve definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading it’s reach and making our devices smarter.



Artificial intelligence is the future of computers, where your devices will be able to decide what is right for you. Machine learning is the core for having a futuristic reality where robot maids and robodogs exist. Machine learning includes the algorithms that allow the computers to think and respond, as well as manipulate the data depending on the scenario that’s placed before them.



So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because machine learning is complex and tough, we’ve designed a course to help break it down into more simple concepts that are easier to understand.



This course covers the basic concepts of machine learning that are crucial to get started on the journey of becoming a developer for machine learning. This course covers all the different algorithms that are required to simulate the right environment for your computer.



The course will start at the very beginning and delve right into machine learning, before breaking down the most important concepts principles. However, the course does require you to have a mathematical background as machine learning relies heavily on mathematical concepts. It also requires you to have some experience with Python principles which will be required when we put the algorithms to test in actual real-world Python projects.



The course covers a number of different machine learning algorithms such as supervised learning, unsupervised learning, reinforced learning and even neural networks. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 6 brand new projects that can help you experience the power of Machine Learning using real-world examples!



9 Projects That Are Included in This Course:



Project 1 -Board Game Review Prediction – In this project, you’ll see how to perform a linear regression analysis by predicting the average reviews on a board game in this project.



Project 2 – Credit Card Fraud Detection – In this project, you’ll learn to focus on anomaly detection by using probability densities to detect credit card fraud.



Project 3 – Stock Market Clustering – Learn how to use the K-means clustering algorithm to find related companies by finding correlations among stock market movements over a given time span.



Project 4 – Getting Started with Natural Language Processing In Python – This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking.



Project 5– Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning – In this project, will use the CIFAR-10 object recognition dataset as a benchmark to implement a recently published deep neural network.



Project 6 – Image Super Resolution with the SRCNN – Learn how to implement and use a Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) for improving image quality.



Project 7 – Natural Language Processing: Text Classification – In this project, you’ll learn an advanced approach to Natural Language



Processing by solving a text classification task using multiple classification algorithms.



Project 8 – K-Means Clustering For Image Analysis – In this project, you’ll learn how to use K-Means clustering in an unsupervised



learning method to analyze and classify 28 x 28 pixel images from the MNIST dataset.



Project 9 – Data Compression & Visualization Using Principle Component Analysis – This project will show you how to compress



our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering.



All of this and so much more is included in this course. So, what are you waiting for?



Get started in machine learning with this epic course that makes machine learning simpler and easy to understand! Enroll now to step into the future of programming.



Who is the target audience?



-Students who will like to understand and use Machine learning in real world projects will find this course very useful.



For More Udemy Free Courses >>> http://www.freetutorials.eu

For more Lynda and other Courses >>> https://www.freecoursesonline.me/

Our Forum for discussion >>> https://discuss.freetutorials.us/








File list
  • [FreeTutorials.Eu] [UDEMY] Projects in Machine Learning Beginner To Professional - FTU]
  • 1. An Introduction to Machine Learning/1. Introduction.mp4 1.7 MB
  • 1. An Introduction to Machine Learning/1. Introduction.srt 1.6 KB
  • 1. An Introduction to Machine Learning/2. What is Machine Learning.mp4 29.2 MB
  • 1. An Introduction to Machine Learning/2. What is Machine Learning.srt 15.8 KB
  • 1. An Introduction to Machine Learning/3. Types and Applications of ML.mp4 53.1 MB
  • 1. An Introduction to Machine Learning/3. Types and Applications of ML.srt 34.8 KB
  • 1. An Introduction to Machine Learning/4. AI vs ML.mp4 22.9 MB
  • 1. An Introduction to Machine Learning/4. AI vs ML.srt 12.5 KB
  • 1. An Introduction to Machine Learning/5. Essential Math for ML and AI.mp4 35.8 MB
  • 1. An Introduction to Machine Learning/5. Essential Math for ML and AI.srt 23.3 KB
  • 1. An Introduction to Machine Learning/6. Quiz- Questions- Section1.html 65 B
  • 1. An Introduction to Machine Learning/6.1 Unit 1 Quiz.pdf.pdf 64.5 KB
  • 1. An Introduction to Machine Learning/7. Quiz- Answers - Section 1.html 53 B
  • 1. An Introduction to Machine Learning/7.1 Unit 1 Solutions.pdf.pdf 61.7 KB
  • 10. Project 5 Object Recognition/1. Intro.mp4 7.2 MB
  • 10. Project 5 Object Recognition/1. Intro.srt 1.7 KB
  • 10. Project 5 Object Recognition/1.1 Object Recognition/Object Recognition/Object Recognition (HTML Notebook).html 398.6 KB
  • 10. Project 5 Object Recognition/1.1 Object Recognition/Object Recognition/Object Recognition (Jupyter Notebook).ipynb 132.3 KB
  • 10. Project 5 Object Recognition/2. Loading and Preprocessing the CIFAR10 Dataset.mp4 180.5 MB
  • 10. Project 5 Object Recognition/2. Loading and Preprocessing the CIFAR10 Dataset.srt 32.6 KB
  • 10. Project 5 Object Recognition/3. Building and Deploying the All-CNN Network Part 1.mp4 205.6 MB
  • 10. Project 5 Object Recognition/3. Building and Deploying the All-CNN Network Part 1.srt 30.7 KB
  • 10. Project 5 Object Recognition/4. Building and Deploying the All-CNN Network Part 2.mp4 170.8 MB
  • 10. Project 5 Object Recognition/4. Building and Deploying the All-CNN Network Part 2.srt 25.3 KB
  • 11. Project 6 Image Super Resolution/1. Intro.mp4 9.6 MB
  • 11. Project 6 Image Super Resolution/1. Intro.srt 1.5 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/Image Super Resolution with the SRCNN (HTML Notebook).html 967.4 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/Image Super Resolution with the SRCNN (Jupyter Notebook).ipynb 701.2 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/baboon.png 789.3 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/baby_GT.png 574 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/barbara.png 629.5 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/bird_GT.png 421.8 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/butterfly_GT.png 440.9 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/coastguard.png 402.7 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/comic.png 617.9 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/face.png 380.3 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/flowers.png 515 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/foreman.png 372.8 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/head_GT.png 385.3 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/lenna.png 598.3 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/monarch.png 459.7 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/pepper.png 654 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/ppt3.png 432.8 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/woman_GT.png 415.7 KB
  • 11. Project 6 Image Super Resolution/1.1 Image Super Resolution/results/zebra.png 531.3 KB
  • 11. Project 6 Image Super Resolution/2. Quality Metrics and Preprocessing Images.mp4 258.8 MB
  • 11. Project 6 Image Super Resolution/2. Quality Metrics and Preprocessing Images.srt 44.3 KB
  • 11. Project 6 Image Super Resolution/3. Image Super Resolution using Deep Learning.mp4 357.6 MB
  • 11. Project 6 Image Super Resolution/3. Image Super Resolution using Deep Learning.srt 55.1 KB
  • 12. Project 7 Text Classification/1. Intro.mp4 5 MB
  • 12. Project 7 Text Classification/1. Intro.srt 1.4 KB
  • 12. Project 7 Text Classification/1.1 Text Classification/Text Classification/NLP for Text Classification (HTML).html 293.9 KB
  • 12. Project 7 Text Classification/1.1 Text Classification/Text Classification/NLP for Text Classification (Jupyter Notebook).ipynb 26.9 KB
  • 12. Project 7 Text Classification/2. Feature Engineering.mp4 375.4 MB
  • 12. Project 7 Text Classification/2. Feature Engineering.srt 56.5 KB
  • 12. Project 7 Text Classification/3. Deploying Sklearn Classifiers.mp4 204.2 MB
  • 12. Project 7 Text Classification/3. Deploying Sklearn Classifiers.srt 32.3 KB
  • 13. Project 8 - KMeans/1. Intro.mp4 11.2 MB
  • 13. Project 8 - KMeans/1. Intro.srt 1.6 KB
  • 13. Project 8 - KMeans/1.1 KMeans/KMeans/KMeans Clustering for Imagery Analysis (HTML).html 386.2 KB
  • 13. Project 8 - KMeans/1.1 KMeans/KMeans/KMeans Clustering for Imagery Analysis (Jupyter Notebook).ipynb 122.7 KB
  • 13. Project 8 - KMeans/2. Preprocessing Images for Clustering.mp4 230.9 MB
  • 13. Project 8 - KMeans/2. Preprocessing Images for Clustering.srt 46 KB
  • 13. Project 8 - KMeans/3. Evaluation and Visualization.mp4 209.5 MB
  • 13. Project 8 - KMeans/3. Evaluation and Visualization.srt 34.8 KB
  • 14. Project 9 PCA/1. Intro.mp4 3.7 MB
  • 14. Project 9 PCA/1. Intro.srt 1.3 KB
  • 14. Project 9 PCA/1.1 PCA/PCA/PCA Compression and Visualization (HTML).html 394.1 KB
  • 14. Project 9 PCA/1.1 PCA/PCA/PCA Compression and Visualization (Jupyter Notebook).ipynb 132.1 KB
  • 14. Project 9 PCA/2. The Elbow Method.mp4 114.2 MB
  • 14. Project 9 PCA/2. The Elbow Method.srt 29.1 KB
  • 14. Project 9 PCA/3. PCA Compression and Visualization.mp4 185 MB
  • 14. Project 9 PCA/3. PCA Compression and Visualization.srt 36.1 KB
  • 2. Supervised Learning - part 1/1. Introduction to Supervised Learning.mp4 25.5 MB
  • 2. Supervised Learning - part 1/1. Introduction to Supervised Learning.srt 18.3 KB
  • 2. Supervised Learning - part 1/10. Quiz- Answers - Section 2.html 53 B
  • 2. Supervised Learning - part 1/10.1 Unit 2 Solutions.pdf.pdf 75.3 KB
  • 2. Supervised Learning - part 1/2. Linear Methods for Classification.mp4 34.1 MB
  • 2. Supervised Learning - part 1/2. Linear Methods for Classification.srt 22 KB
  • 2. Supervised Learning - part 1/3. Linear Methods for Regression.mp4 27 MB
  • 2. Supervised Learning - part 1/3. Linear Methods for Regression.srt 14.7 KB
  • 2. Supervised Learning - part 1/4. Support Vector Machines.mp4 35.8 MB
  • 2. Supervised Learning - part 1/4. Support Vector Machines.srt 20.5 KB
  • 2. Supervised Learning - part 1/5. Basis Expansions.mp4 21.3 MB
  • 2. Supervised Learning - part 1/5. Basis Expansions.srt 14.1 KB
  • 2. Supervised Learning - part 1/6. Model Selection Procedures.mp4 26.9 MB
  • 2. Supervised Learning - part 1/6. Model Selection Procedures.srt 17.1 KB
  • 2. Supervised Learning - part 1/7. Bonus! Supervised Learning Project in Python Part 1.mp4 31 MB
  • 2. Supervised Learning - part 1/7. Bonus! Supervised Learning Project in Python Part 1.srt 18.9 KB
  • 2. Supervised Learning - part 1/7.1 Supervised Learning/Supervised Learning/Unit 2 Project - Supervised Learning in Python (HTML Document).html 347.1 KB
  • 2. Supervised Learning - part 1/7.1 Supervised Learning/Supervised Learning/Unit 2 Project - Supervised Learning in Python (Jupyter Notebook).ipynb 92.8 KB
  • 3. Unsupervised Learning/5.1 Unsupervised Learning/Unsupervised Learning/Unit 3 Project - KMeans Clustering (HTML).html 290.4 KB
  • 3. Unsupervised Learning/5.1 Unsupervised Learning/Unsupervised Learning/Unit 3 Project - KMeans Clustering (Jupyter Notebook).ipynb 39.2 KB
  • 6. Warmup Project/1.1 Final Project/Final Project/Final Project (HTML).html 289.2 KB
  • 6. Warmup Project/1.1 Final Project/Final Project/Final Project (Jupyter Notebook).ipynb 15.4 KB
  • 7. Project 1Board Game Review Prediction/1.1 Board Game Review Predictions/Board Game Review Predictions/Board Game Review Prediction (HTML).html 318.9 KB
  • 7. Project 1Board Game Review Prediction/1.1 Board Game Review Predictions/Board Game Review Predictions/Board Game Review Prediction (Jupyter Notebook).ipynb 67.5 KB
  • 8. Project 2 Credit Card Fraud Detection/1.1 Credit Card Fraud Detection/Credit Card Fraud Detection/Credit Card Fraud Detection (HTML).html 399 KB
  • 8. Project 2 Credit Card Fraud Detection/1.1 Credit Card Fraud Detection/Credit Card Fraud Detection/Credit Card Fraud Detection (Jupyter Notebook).ipynb 145 KB

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