Learn Real World Machine Learning By Building Projects
- 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 Eduonix Learning Solutions
- 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 Eduonix Learning Solutions
- 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. Eduonix Learning Solutions
- 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 Eduonix Learning Solutions
- 7. Machine Learning Engineer Salary Eduonix Learning Solutions
- 8. Machine Learning Jobs on the rise Machine Learning Engineer Jobs Positions on Glassdoor.com – 12000+ Eduonix Learning Solutions
- 9. Machine Learning Jobs on Dice.com – 8000+ Eduonix Learning Solutions
- 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. Eduonix Learning Solutions 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. Eduonix Learning Solutions
- 12. 5 New projects added in this course Eduonix Learning Solutions 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 Eduonix Learning Solutions
- 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 Eduonix Learning Solutions
- 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 - Eduonix Learning Solutions
- 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 Eduonix Learning Solutions
- 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 Eduonix Learning Solutions
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