Machine Learning Course
-Build Intelligent Applications
About Machine Learning course: Machine Learning Course is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition.
(i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
Basic programming skills (in Python), algorithm design, basics of probability & statistics
Detailed Contents of Machine Learning Course :
- Machine learning algorithms
- Techniques of Machine Learning
- Applications of Machine Learning
- Review of linear algebra
- Data Preprocessing
- Supervised learning algorithms
- Linear Regression with single Variable
- Linear Regression with Multiple Variables
- Logistic Regression & Random Forest
- Decision Tree Regression
- Classification using KNN
- Classification using GINI
- Classification using SVM(support vector machine)
- Neural Networks: Representation
- Neural Networks: Learning for regression and classification
- Unsupervised learning – Clustering
- Mining of data using LSI
- Large Scale Machine Learning
Machine learning is a branch of science that deals with programming the systems in such a way that they automatically learn and improve with experience. Here, learning means recognizing and understanding the input data and making wise decisions based on the supplied data.
It is very difficult to cater to all the decisions based on all possible inputs. To tackle this problem, algorithms are developed. These algorithms build knowledge from specific data and past experience with the principles of statistics, probability theory, logic, combinatorial optimization, search, reinforcement learning, and control theory.