Introduction to machine learning
- 1. Seminar Presentation on Introduction to Machine Learning Submitted by:- Salman Saifi BCA 5th semester 180733106077 Submitted to:- prof. Anuj pawar Department of computer application Hi-tech Institute of Engineering & Technology, Ghaziabad Session:- 2018-21
- 3. Table of content 1. Introduction to machine learning 2. Types of machine learning Supervised machine learning Unsupervised machine learning Reinforcement machine learning 3. Why machine learning is important 4. Applications of machine learning 5. Conclusion
- 4. Introduction to Machine Learning • Machine learning is the study of computer algorithms that allow computer programs to automatically improves through experience. • 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.
- 5. AI vs MI vs Deep Learning
- 6. AI vs MI vs Deep Learning • AI means getting a computer to mimic human behavior in some way. • Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications. • Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.
- 7. Types of Machine Learning
- 8. Supervised Machine Learning • Supervised learning in simple language means training the machine learning model just like a coach trains a batsman. In Supervised Learning, the machine learns under the guidance of labeled data i.e. known data. This known data is fed to the machine learning model and is used to train it. Once the model is trained with a known set of data, you can go ahead and feed unknown data to the model to get a new response.
- 9. Unsupervised Machine Learning • In unsupervised machine learning, there is no such provision of labeled data. The training data is unknown or unlabeled. This unknown data is fed to the machine learning model and is used to train the model. The model tries to find patterns and relationships in the dataset by creating clusters in it. The thing to be noted here is that unsupervised learning is not able to add labels to the clusters.
- 10. Reinforcement Machine Learning • In this, the machine learns from a hit and trial method. Whenever the model predicts or produces a result, it is penalized if the prediction is wrong or rewarded if the prediction is correct. Based on these actions the model trains itself.
- 11. Why machine learning important? • Machine learning is the core subarea of artificial intelligence. It makes computers get into a self learning mode without explicit programming. When feed new data, these computers learn, grow, change, and develop by themselves. • Machine learning is important because of its wide range of applications and its incredible ability to adapt and provide solutions to complex problems efficiently, effectively and quickly.
- 12. Applications of Machine Learning • Online Fraud Detection • Online Customer Support i.e. Chatbot • Speech Recognition • Product Recommendations • Image Recognition • Automatic Language Translate • Virtual Personal Assistance • Self Driving Car • Email Spam and Malware Filtering
- 13. Conclusion • As we move forward into the digital age, our Technology continues to leaps and strides forward. This incredible form of Artificial Intelligence is already being used in various industries and professions. Form Marketing, to medicine and web security. This Technology can improve our lives in several numerous ways.
- 14. Thank you