MACHINE LEARNING modified

1. MACHINE LEARNING Presented by M. Kishore Kumar

2. What is Machine Learning? Computational learning using algorithms to learn from and make predictions on data.

3. Definition of Machine Learning • Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. • A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.

4. Why Machine Learning? • Machine learning focuses on the development of computer programs. ... Google says” Machine Learning is the future”, so future of machine learning is going to be very bright

5. AI vs ML vs DL • Artificial intelligence is a science like mathematics or biology. It studies ways to build intelligent programs and machines that can creatively solve problems, which has always been considered a human prerogative. • Machine learning is a subset of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. In ML, there are different algorithms (e.g. neural networks) that help to solve problems. • Deep learning, or deep neural learning, is a subset of machine learning, which uses the neural networks to analyze different factors with a structure that is similar to the human neural system.

6. The 7 Steps of Machine Learning • 1. Gathering Data:Quality and quantity of data gathered will directly determine how good the predictive model will turn out to be. • 2. Data Preparation:Data is loaded into a suitable place and then prepared for use in machine learning training. • 3. Choosing a model:workflow is choosing a model among the many that researchers and data scientists have created over the years.  • 4. Training: The data is used to incrementally improve the model’s ability to predict. • 5. Evaluation: The testing of the model against data that has never been seen and used for training  • 6. Parameter Tuning: Further improvement in training can be possible by tuning the parameters. • 7. Prediction: This is the point where the value of machine learning is realized.

7. Terminology • Dataset: A set of data examples, that contain features important to solving the problem. • Features: Important pieces of data that help us understand a problem. These are fed in to a Machine Learning algorithm to help it learn. • Model: The representation (internal model) of a phenomenon that a Machine Learning algorithm has learnt. It learns this from the data it is shown during training. The model is the output you get after training an algorithm. For example, a decision tree algorithm would be trained and produce a decision tree model.

8. Three types of Machine Learning Supervised Learning: This algorithm consist of a target / outcome variable which is to be predicted from a given set of predictors. Examples: Regression,  Decision Tree, Random Forest, KNN, Logistic Regression etc. Unsupervised Learning: In this algorithm, we do not have any target or outcome variable to predict / estimate. It is used for clustering population in different groups. Examples: Apriori algorithm, K-means. Reinforcement Learning:Using this algorithm, the machine is trained to make specific decisions. The machine is exposed to an environment where it trains itself continually using trial and error. Example: Markov Decision Process 01 02 03

9. Three types of Machine Learning

12. Types of Machine Learning Algorithms

13. Popular Machine Learning Algorithms K-Nearest Neighbors Support Vector Machine Logistic Regression LinearRegression Random Forest K-Means Decision Trees Naïve Bayes Supervised Learning Classification algorithm Regression algorithm Collection of Decision Trees Un Supervised Learning Classification algorithm K-User specified Finds K-number of Clusters Supervised Learning Regression Algorithm Y=mX+C M-axis Intercept Supervised Learning Classification algorithm Regression algorithm K-number –User specified Supervised Learning Classification algorithm Lines are classifiers Maximise Distance to Lines Supervised Learning Classification algorithm Bayes theorem Probability Based Supervised Learning Classification Algorithm S-Curve Binary Classification Supervised Learning Classification algorithm Regression algorithm Easy to interpret 05 01 02 06 Size,Quality & Nature of Data Available Computational Time Urgency of the task Data Analysis requirements 03 07 08 04

14. Top 10 Applications of Machine Learning • Virtual Personal Assistants • Traffic Predictions • Social Media Personalization • Email Spam Filtering • Chatbots • Search Engine Result Refining • Online Fraud Detection • Stock Market Trading • Assistive Medical Technology • Automatic Translation

15. How to Become A Machine Learning Engineer  • Improve your Math skills Develop good Programming skills Data Engineering skills Skill - Set Learn Machine Learning Algorithms Learn Machine Learning Frameworks

17. Job Opportunities in Machine Learning •  In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According to Indeed, Machine Learning Engineer Is The Best Job of 2020 with a 344% growth and an average base salary of $146,085 per year. There are many career paths in Machine Learning that are popular and well-paying such as Machine Learning Engineer, Data Scientist, NLP Scientist, etc.

18. Future of Machine Learning • Machine Learning can be a competitive advantage to any company as things that are currently being done manually will be done tomorrow by machines. Machine Learning revolution will stay with us for long and so will be the future of Machine Learning. • https://www.cnbc.com/video/2018/04/05/why-this-researcher-is-enthusiastic-about-machine-learning.html