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File	Size
1. An Introduction to Machine Learning/1. Introduction.mp4	1.71 MB
1. An Introduction to Machine Learning/1. Introduction.srt	1.63 kB
1. An Introduction to Machine Learning/2. What is Machine Learning.mp4	29.19 MB
1. An Introduction to Machine Learning/2. What is Machine Learning.srt	15.77 kB
1. An Introduction to Machine Learning/3. Types and Applications of ML.mp4	53.05 MB
1. An Introduction to Machine Learning/3. Types and Applications of ML.srt	34.78 kB
1. An Introduction to Machine Learning/4. AI vs ML.mp4	22.88 MB
1. An Introduction to Machine Learning/4. AI vs ML.srt	12.52 kB
1. An Introduction to Machine Learning/5. Essential Math for ML and AI.mp4	35.79 MB
1. An Introduction to Machine Learning/5. Essential Math for ML and AI.srt	23.34 kB
1. An Introduction to Machine Learning/6. Quiz- Questions- Section1.html	65.00 B
1. An Introduction to Machine Learning/6.1 Unit 1 Quiz.pdf.pdf	64.52 kB
1. An Introduction to Machine Learning/7. Quiz- Answers - Section 1.html	53.00 B
1. An Introduction to Machine Learning/7.1 Unit 1 Solutions.pdf.pdf	61.74 kB
10. Project 5 Object Recognition/1. Intro.mp4	7.21 MB
10. Project 5 Object Recognition/1. Intro.srt	1.66 kB
10. Project 5 Object Recognition/1.1 Object Recognition.zip.zip	219.04 kB
10. Project 5 Object Recognition/2. Loading and Preprocessing the CIFAR10 Dataset.mp4	180.54 MB
10. Project 5 Object Recognition/2. Loading and Preprocessing the CIFAR10 Dataset.srt	32.63 kB
10. Project 5 Object Recognition/3. Building and Deploying the All-CNN Network Part 1.mp4	205.60 MB
10. Project 5 Object Recognition/3. Building and Deploying the All-CNN Network Part 1.srt	30.70 kB
10. Project 5 Object Recognition/4. Building and Deploying the All-CNN Network Part 2.mp4	170.79 MB
10. Project 5 Object Recognition/4. Building and Deploying the All-CNN Network Part 2.srt	25.32 kB
11. Project 6 Image Super Resolution/1. Intro.mp4	9.56 MB
11. Project 6 Image Super Resolution/1. Intro.srt	1.51 kB
11. Project 6 Image Super Resolution/1.1 Image Super Resolution.zip.zip	9.41 MB
11. Project 6 Image Super Resolution/2. Quality Metrics and Preprocessing Images.mp4	258.79 MB
11. Project 6 Image Super Resolution/2. Quality Metrics and Preprocessing Images.srt	44.26 kB
11. Project 6 Image Super Resolution/3. Image Super Resolution using Deep Learning.mp4	357.57 MB
11. Project 6 Image Super Resolution/3. Image Super Resolution using Deep Learning.srt	55.11 kB
12. Project 7 Text Classification/1. Intro.mp4	5.00 MB
12. Project 7 Text Classification/1. Intro.srt	1.39 kB
12. Project 7 Text Classification/1.1 Text Classification.zip.zip	55.36 kB
12. Project 7 Text Classification/2. Feature Engineering.mp4	375.39 MB
12. Project 7 Text Classification/2. Feature Engineering.srt	56.48 kB
12. Project 7 Text Classification/3. Deploying Sklearn Classifiers.mp4	204.23 MB
12. Project 7 Text Classification/3. Deploying Sklearn Classifiers.srt	32.28 kB
13. Project 8 - KMeans/1. Intro.mp4	11.21 MB
13. Project 8 - KMeans/1. Intro.srt	1.58 kB
13. Project 8 - KMeans/1.1 KMeans.zip.zip	189.11 kB
13. Project 8 - KMeans/2. Preprocessing Images for Clustering.mp4	230.86 MB
13. Project 8 - KMeans/2. Preprocessing Images for Clustering.srt	45.96 kB
13. Project 8 - KMeans/3. Evaluation and Visualization.mp4	209.48 MB
13. Project 8 - KMeans/3. Evaluation and Visualization.srt	34.79 kB
14. Project 9 PCA/1. Intro.mp4	3.69 MB
14. Project 9 PCA/1. Intro.srt	1.30 kB
14. Project 9 PCA/1.1 PCA.zip.zip	219.64 kB
14. Project 9 PCA/2. The Elbow Method.mp4	114.20 MB
14. Project 9 PCA/2. The Elbow Method.srt	29.11 kB
14. Project 9 PCA/3. PCA Compression and Visualization.mp4	185.00 MB
14. Project 9 PCA/3. PCA Compression and Visualization.srt	36.13 kB
2. Supervised Learning - part 1/1. Introduction to Supervised Learning.mp4	25.47 MB
2. Supervised Learning - part 1/1. Introduction to Supervised Learning.srt	18.30 kB
2. Supervised Learning - part 1/10. Quiz- Answers - Section 2.html	53.00 B
2. Supervised Learning - part 1/10.1 Unit 2 Solutions.pdf.pdf	75.28 kB
2. Supervised Learning - part 1/2. Linear Methods for Classification.mp4	34.14 MB
2. Supervised Learning - part 1/2. Linear Methods for Classification.srt	21.99 kB
2. Supervised Learning - part 1/3. Linear Methods for Regression.mp4	27.01 MB
2. Supervised Learning - part 1/3. Linear Methods for Regression.srt	14.73 kB
2. Supervised Learning - part 1/4. Support Vector Machines.mp4	35.79 MB
2. Supervised Learning - part 1/4. Support Vector Machines.srt	20.46 kB
2. Supervised Learning - part 1/5. Basis Expansions.mp4	21.32 MB
2. Supervised Learning - part 1/5. Basis Expansions.srt	14.06 kB
2. Supervised Learning - part 1/6. Model Selection Procedures.mp4	26.93 MB
2. Supervised Learning - part 1/6. Model Selection Procedures.srt	17.05 kB
2. Supervised Learning - part 1/7. Bonus! Supervised Learning Project in Python Part 1.mp4	31.01 MB
2. Supervised Learning - part 1/7. Bonus! Supervised Learning Project in Python Part 1.srt	18.94 kB
2. Supervised Learning - part 1/7.1 Supervised Learning.zip.zip	167.22 kB
2. Supervised Learning - part 1/8. Bonus! Supervised Learning Project in Python Part 2.mp4	36.22 MB
2. Supervised Learning - part 1/8. Bonus! Supervised Learning Project in Python Part 2.srt	17.81 kB
2. Supervised Learning - part 1/9. Quiz- Questions- Section 2.html	65.00 B
2. Supervised Learning - part 1/9.1 Unit 2 Quiz.pdf.pdf	52.97 kB
3. Unsupervised Learning/1. Introduction to Unsupervised Learning.mp4	31.78 MB
3. Unsupervised Learning/1. Introduction to Unsupervised Learning.srt	15.81 kB
3. Unsupervised Learning/2. Association Rules.mp4	28.50 MB
3. Unsupervised Learning/2. Association Rules.srt	18.71 kB
3. Unsupervised Learning/3. Cluster Analysis.mp4	27.95 MB
3. Unsupervised Learning/3. Cluster Analysis.srt	17.86 kB
3. Unsupervised Learning/4. Reinforcement Learning.mp4	20.96 MB
3. Unsupervised Learning/4. Reinforcement Learning.srt	22.35 kB
3. Unsupervised Learning/5. Bonus! KMeans Clustering Project.mp4	21.95 MB
3. Unsupervised Learning/5. Bonus! KMeans Clustering Project.srt	19.77 kB
3. Unsupervised Learning/5.1 Unsupervised Learning.zip.zip	92.48 kB
3. Unsupervised Learning/6. Quiz- Questions- Section 3.html	65.00 B
3. Unsupervised Learning/6.1 Unit 3 Quiz.pdf.pdf	29.80 kB
3. Unsupervised Learning/7. Quiz- Answers - Section 3.html	53.00 B
3. Unsupervised Learning/7.1 Unit 3 Solutions.pdf.pdf	41.25 kB
4. Neural Networks/1. Introduction to Neural Networks.mp4	22.71 MB
4. Neural Networks/1. Introduction to Neural Networks.srt	17.55 kB
4. Neural Networks/2. The Perceptron.mp4	17.12 MB
4. Neural Networks/2. The Perceptron.srt	13.35 kB
4. Neural Networks/3. The Backpropagation Algorithm.mp4	22.64 MB
4. Neural Networks/3. The Backpropagation Algorithm.srt	16.14 kB
4. Neural Networks/4. Training Procedures.mp4	24.04 MB
4. Neural Networks/4. Training Procedures.srt	18.74 kB
4. Neural Networks/5. Convolutional Neural Networks.mp4	32.04 MB
4. Neural Networks/5. Convolutional Neural Networks.srt	21.82 kB
5. Real World Machine Learning/1. Introduction to Real World ML.mp4	25.58 MB
5. Real World Machine Learning/1. Introduction to Real World ML.srt	16.38 kB
5. Real World Machine Learning/2. Choosing an Algorithm.mp4	19.14 MB
5. Real World Machine Learning/2. Choosing an Algorithm.srt	13.28 kB
5. Real World Machine Learning/3. Design and Analysis of ML Experiments.mp4	19.79 MB
5. Real World Machine Learning/3. Design and Analysis of ML Experiments.srt	14.47 kB
5. Real World Machine Learning/4. Common Software for ML.mp4	31.33 MB
5. Real World Machine Learning/4. Common Software for ML.srt	15.09 kB
5. Real World Machine Learning/5. Quiz- Questions- Section 5.html	65.00 B
5. Real World Machine Learning/5.1 Unit 5 Quiz.pdf.pdf	35.22 kB
5. Real World Machine Learning/6. Quiz- Answers - Section 5.html	53.00 B
5. Real World Machine Learning/6.1 Unit 5 Solutions.pdf.pdf	47.20 kB
6. Warmup Project/1. Setting up OpenAI Gym.mp4	30.12 MB
6. Warmup Project/1. Setting up OpenAI Gym.srt	17.17 kB
6. Warmup Project/1.1 Final Project.zip.zip	47.02 kB
6. Warmup Project/2. Building and Training the Network Part 1.mp4	36.02 MB
6. Warmup Project/2. Building and Training the Network Part 1.srt	20.35 kB
6. Warmup Project/3. Building and Training the Network Part 2.mp4	63.34 MB
6. Warmup Project/3. Building and Training the Network Part 2.srt	26.95 kB
7. Project 1Board Game Review Prediction/1. Intro.mp4	7.55 MB
7. Project 1Board Game Review Prediction/1. Intro.srt	2.02 kB
7. Project 1Board Game Review Prediction/1.1 Board Game Review Predictions.zip.zip	128.63 kB
7. Project 1Board Game Review Prediction/2. Board Game Review Prediction - Building the Dataset Part 1.mp4	17.73 MB
7. Project 1Board Game Review Prediction/2. Board Game Review Prediction - Building the Dataset Part 1.srt	12.67 kB
7. Project 1Board Game Review Prediction/3. Board Game Review Prediction - Building the Dataset Part 2.mp4	35.50 MB
7. Project 1Board Game Review Prediction/3. Board Game Review Prediction - Building the Dataset Part 2.srt	20.37 kB
7. Project 1Board Game Review Prediction/4. Board Game Review Prediction - Training the Models.mp4	35.82 MB
7. Project 1Board Game Review Prediction/4. Board Game Review Prediction - Training the Models.srt	16.81 kB
8. Project 2 Credit Card Fraud Detection/1. Intro.mp4	7.57 MB
8. Project 2 Credit Card Fraud Detection/1. Intro.srt	2.80 kB
8. Project 2 Credit Card Fraud Detection/1.1 Credit Card Fraud Detection.zip.zip	237.75 kB
8. Project 2 Credit Card Fraud Detection/2. Credit Card Fraud Detection - The Dataset.mp4	37.57 MB
8. Project 2 Credit Card Fraud Detection/2. Credit Card Fraud Detection - The Dataset.srt	26.52 kB
8. Project 2 Credit Card Fraud Detection/3. Credit Card Fraud Detection - The Algorithms.mp4	48.91 MB
8. Project 2 Credit Card Fraud Detection/3. Credit Card Fraud Detection - The Algorithms.srt	24.93 kB
9. Project 4 Intro to Natural Language Processing/1. Intro.mp4	16.95 MB
9. Project 4 Intro to Natural Language Processing/1. Intro.srt	1.66 kB
9. Project 4 Intro to Natural Language Processing/1.1 Intro to Natural Language Processing.zip.zip	64.95 kB
9. Project 4 Intro to Natural Language Processing/2. Tokenizing, Stop Words, and Stemming.mp4	198.34 MB
9. Project 4 Intro to Natural Language Processing/2. Tokenizing, Stop Words, and Stemming.srt	30.14 kB
9. Project 4 Intro to Natural Language Processing/3. Tagging, Chunking, and Named Entity Recognition.mp4	323.06 MB
9. Project 4 Intro to Natural Language Processing/3. Tagging, Chunking, and Named Entity Recognition.srt	37.42 kB
9. Project 4 Intro to Natural Language Processing/4. Text Classification.mp4	216.93 MB
9. Project 4 Intro to Natural Language Processing/4. Text Classification.srt	29.66 kB
Udemy Course downloader.txt	94.00 B
udemycoursedownloader.com.url	132.00 B

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.