About Machine Learning course: Machine Learning Course is that the science of getting computers to act without being explicitly programmed. Within 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 simply probably use it dozens of times each day without knowing it. Many researchers also think it’s the simplest thanks to make progress towards human-level AI. During this class, you’ll study the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll study 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 study a number of Silicon Valley’s best practices in innovation because it pertains to machine learning and AI.
This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.
(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 also will draw from numerous case studies and applications, in order that you’ll also find out how to use learning algorithms to put together smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
PRE-REQUISITES
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 algebra
Data Reprocessing
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 knowledge using LSI
Large Scale Machine Learning
Machine learning may be 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 file and making wise decisions supported the supplied data.
It is very difficult to cater to all or any of the choices supported 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.
