Machine learning is a branch of computer science that enables programs to learn and improve from data without explicit programming, with applications in areas such as fraud detection, sentiment analysis, and equipment failure prediction. The document outlines different types of machine learning, including supervised, unsupervised, and reinforcement learning, and distinguishes it from data mining. Additionally, it details a course that offers hands-on projects to implement various machine learning algorithms and techniques.

2. Machine Learning is the branch of computer science that deals with the development of computer programs
that teach and grow themselves. According to Arthur Samuel, an American pioneer in computer gaming,
Machine Learning is the subfield of computer science that "gives the computer ability to learn without being
explicitly programmed." Machine Learning allows developers to build algorithms that automatically
improve themselves by finding patterns in the existing data without explicit instructions from a human or
developer. Machine Learning relies entirely on the data; the more the data, the more efficient Machine
Learning is.
The Evolution of Machine Learning
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3. How is Machine Learning used today?
 Fraud detection.
 Web search results.
 Real-time ads.
 Text-based sentiment analysis.
 Credit scoring and next-best offers.
 Prediction of equipment failures.
 New pricing models
 Network intrusion detection
 Pattern and image recognition
 Email spam filtering
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4. Difference between Data Mining, Machine Learning &
Deep Learning?
The difference between machine learning and other
statistical and mathematical approaches, such as data
mining, is another popular subject of debate. In simple
terms, while machine learning uses many of the same
algorithms and techniques as data mining, one difference
lies in what the two disciplines predict.
 Data mining discovers previously unknown patterns and knowledge.
 Machine learning is used to reproduce known patterns and knowledge, automatically
apply that to other data, and then automatically apply those results to decision making
and actions.
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5. 1. Supervised learning:
Computer is presented with inputs and their desired outputs.
The goal is to learn a general rule to map inputs to the output.
2. Unsupervised learning:
Computer is presented with inputs without desired outputs, the
goal is to find structure in inputs.
3. Reinforcement learning:
Computer program interacts with a dynamic environment, and it
must perform a certain goal without guide or teacher.
Machine Learning Classification
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7. Machine Learning Engineer Salary
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8. Machine Learning Jobs on the rise
Machine Learning Engineer Jobs Positions on Glassdoor.com – 12000+
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9. Machine Learning Jobs on Dice.com – 8000+
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10. A complete course where you will learn to
implement cutting edge machine learning
algorithms to solve real world problems. We have
carefully selected the projects which will cover
important aspect of Machine learning such as
Supervised Learning, Unsupervised learning and
Neural network with deep learning. You will start
with real world data available publicly to create
these Machine Learnings Projects. It will be a
course for serious developers but will be fun and
engaging. You will learn step by step
implementation and can be a professional ML
developer after completing this course.
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Course Overview

11. Grateful to each and every one of the people who backed our work and helped us spread the word.
Thank you once again for your immense support which have encouraged us to work even harder on
this project by adding 5 new projects along with the existing 5 projects.
A lot more to come! So if you haven't backed us yet, please do.
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12. 5 New projects added in this course
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1. Markov Models and K-Nearest Neighbor Approaches to Classifying DNA Sequences
2. Getting Started with Natural Language Processing In Python –
3. Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning
4. Image Super Resolution with the SRCNN
5. Natural Language Processing: Text Classification

13. Explore the world of bioinformatics by using Markov models and K-nearest neighbor (KNN) algorithms to
classify E. Coli DNA sequences. This project will use a dataset from the UCI Machine Learning Repository
that has 106 DNA sequences, with 57 sequential nucleotides (“base-pairs”) each.
Project 6 -Markov Models and K-Nearest Neighbor Approaches to Classifying DNA
Sequences
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14. Learn the basics of Natural Language Processing (NLP) methodology, including tokenizing words and
sentences, part of speech identification and tagging, and phrase chunking. After this project, the student
should have the necessary foundation to begin building and deploying machine learning algorithms for
natural language processing.
Project 7 - Getting Started with Natural Language Processing In Python
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15. Using the CIFAR-10 object recognition dataset as a benchmark, we will implement a recently published deep
neural network that can obtain similar results to state-of-the-art networks, despite having less parameters
and smaller computational requirements.
Project 8 - Obtaining Near State-of-the-Art Performance on Object Recognition
Tasks Using Deep Learning -
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16. Learn implementing and using a Tensorflow version of the Super Resolution Convolutional Neural
Network (SRCNN) to improve the image quality of degraded images.
Project 9 - Image Super Resolution with the SRCNN
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17. Building on the foundation developed in the previous project, this tutorial will dive deeper into Natural
Language Processing. We will solve a text classification task using multiple classification algorithms,
including a Naïve Bayes classifier, SGD classifier, and linear support vector classifier (SVC).
Project 10 - Natural Language Processing: Text Classification
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18. Hurry! Only few days left to grab some mouth watering discounts.
Offer Valid Till 28th Feb. 2018
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