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[UdemyCourseDownloader] Projects in Machine Learning Beginner To Professional

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Description
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 LanguageProcessing 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 unsupervisedlearning 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 compressour 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.

File list
  • [UdemyCourseDownloader] Projects in Machine Learning Beginner To Professional
  • 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
  • 12. Project 7 Text Classification/2. Feature Engineering.mp4 375.4 MB
  • 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.zip.zip 167.2 KB
  • 2. Supervised Learning - part 1/8. Bonus! Supervised Learning Project in Python Part 2.mp4 36.2 MB
  • 2. Supervised Learning - part 1/8. Bonus! Supervised Learning Project in Python Part 2.srt 17.8 KB
  • 2. Supervised Learning - part 1/9. Quiz- Questions- Section 2.html 65 B
  • 2. Supervised Learning - part 1/9.1 Unit 2 Quiz.pdf.pdf 53 KB
  • 3. Unsupervised Learning/1. Introduction to Unsupervised Learning.mp4 31.8 MB
  • 3. Unsupervised Learning/1. Introduction to Unsupervised Learning.srt 15.8 KB
  • 3. Unsupervised Learning/2. Association Rules.mp4 28.5 MB
  • 3. Unsupervised Learning/2. Association Rules.srt 18.7 KB
  • 3. Unsupervised Learning/3. Cluster Analysis.mp4 28 MB
  • 3. Unsupervised Learning/3. Cluster Analysis.srt 17.9 KB
  • 3. Unsupervised Learning/4. Reinforcement Learning.mp4 21 MB
  • 3. Unsupervised Learning/4. Reinforcement Learning.srt 22.4 KB
  • 3. Unsupervised Learning/5. Bonus! KMeans Clustering Project.mp4 22 MB
  • 3. Unsupervised Learning/5. Bonus! KMeans Clustering Project.srt 19.8 KB
  • 3. Unsupervised Learning/5.1 Unsupervised Learning.zip.zip 92.5 KB
  • 3. Unsupervised Learning/6. Quiz- Questions- Section 3.html 65 B
  • 3. Unsupervised Learning/6.1 Unit 3 Quiz.pdf.pdf 29.8 KB
  • 3. Unsupervised Learning/7. Quiz- Answers - Section 3.html 53 B
  • 3. Unsupervised Learning/7.1 Unit 3 Solutions.pdf.pdf 41.3 KB
  • 4. Neural Networks/1. Introduction to Neural Networks.mp4 22.7 MB
  • 4. Neural Networks/1. Introduction to Neural Networks.srt 17.6 KB
  • 4. Neural Networks/2. The Perceptron.mp4 17.1 MB
  • 4. Neural Networks/2. The Perceptron.srt 13.4 KB
  • 4. Neural Networks/3. The Backpropagation Algorithm.mp4 22.6 MB
  • 4. Neural Networks/3. The Backpropagation Algorithm.srt 16.1 KB
  • 4. Neural Networks/4. Training Procedures.mp4 24 MB
  • 4. Neural Networks/4. Training Procedures.srt 18.7 KB
  • 4. Neural Networks/5. Convolutional Neural Networks.mp4 32 MB
  • 4. Neural Networks/5. Convolutional Neural Networks.srt 21.8 KB
  • 5. Real World Machine Learning/1. Introduction to Real World ML.mp4 25.6 MB
  • 5. Real World Machine Learning/1. Introduction to Real World ML.srt 16.4 KB
  • 5. Real World Machine Learning/2. Choosing an Algorithm.mp4 19.1 MB
  • 5. Real World Machine Learning/2. Choosing an Algorithm.srt 13.3 KB
  • 5. Real World Machine Learning/3. Design and Analysis of ML Experiments.mp4 19.8 MB
  • 5. Real World Machine Learning/3. Design and Analysis of ML Experiments.srt 14.5 KB
  • 5. Real World Machine Learning/4. Common Software for ML.mp4 31.3 MB
  • 5. Real World Machine Learning/4. Common Software for ML.srt 15.1 KB
  • 5. Real World Machine Learning/5. Quiz- Questions- Section 5.html 65 B
  • 5. Real World Machine Learning/5.1 Unit 5 Quiz.pdf.pdf 35.2 KB
  • 5. Real World Machine Learning/6. Quiz- Answers - Section 5.html 53 B
  • 5. Real World Machine Learning/6.1 Unit 5 Solutions.pdf.pdf 47.2 KB
  • 6. Warmup Project/1. Setting up OpenAI Gym.mp4 30.1 MB
  • 6. Warmup Project/1. Setting up OpenAI Gym.srt 17.2 KB
  • 6. Warmup Project/1.1 Final Project.zip.zip 47 KB
  • 6. Warmup Project/2. Building and Training the Network Part 1.mp4 36 MB
  • 6. Warmup Project/2. Building and Training the Network Part 1.srt 20.3 KB
  • 6. Warmup Project/3. Building and Training the Network Part 2.mp4 63.3 MB
  • 6. Warmup Project/3. Building and Training the Network Part 2.srt 26.9 KB
  • 7. Project 1Board Game Review Prediction/1. Intro.mp4 7.6 MB
  • 7. Project 1Board Game Review Prediction/1. Intro.srt 2 KB
  • 7. Project 1Board Game Review Prediction/1.1 Board Game Review Predictions.zip.zip 128.6 KB
  • 7. Project 1Board Game Review Prediction/2. Board Game Review Prediction - Building the Dataset Part 1.mp4 17.7 MB
  • 7. Project 1Board Game Review Prediction/2. Board Game Review Prediction - Building the Dataset Part 1.srt 12.7 KB
  • 7. Project 1Board Game Review Prediction/3. Board Game Review Prediction - Building the Dataset Part 2.mp4 35.5 MB
  • 7. Project 1Board Game Review Prediction/3. Board Game Review Prediction - Building the Dataset Part 2.srt 20.4 KB
  • 7. Project 1Board Game Review Prediction/4. Board Game Review Prediction - Training the Models.mp4 35.8 MB
  • 7. Project 1Board Game Review Prediction/4. Board Game Review Prediction - Training the Models.srt 16.8 KB
  • 8. Project 2 Credit Card Fraud Detection/1. Intro.mp4 7.6 MB
  • 8. Project 2 Credit Card Fraud Detection/1. Intro.srt 2.8 KB
  • 8. Project 2 Credit Card Fraud Detection/1.1 Credit Card Fraud Detection.zip.zip 237.8 KB
  • 8. Project 2 Credit Card Fraud Detection/2. Credit Card Fraud Detection - The Dataset.mp4 37.6 MB
  • 8. Project 2 Credit Card Fraud Detection/2. Credit Card Fraud Detection - The Dataset.srt 26.5 KB
  • 8. Project 2 Credit Card Fraud Detection/3. Credit Card Fraud Detection - The Algorithms.mp4 48.9 MB
  • 8. Project 2 Credit Card Fraud Detection/3. Credit Card Fraud Detection - The Algorithms.srt 24.9 KB
  • 9. Project 4 Intro to Natural Language Processing/1. Intro.mp4 17 MB
  • 9. Project 4 Intro to Natural Language Processing/1. Intro.srt 1.7 KB

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