Artificial Intelligence using Python
Machine learning 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. This trainig session provides a deep dive into machine learning, data mining, and statistical pattern recognition. Topics include: Supervised learning (parametric/non- parametric algorithms, support vector machines, kernels, neural networks). Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). 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. Prerequisite: Experience in Programming An understanding of Intro to Statistics would be helpful. A familiarity with Probability Theory, Calculus, Linear Algebra and Statistics is required
What you will learn?
1. Python and Machine Learning introduction 2. A Brief Overview of Machine Learning 3. Python Programming 4. Numpy 5. Matplotlib 6. REGRESSION 6.1. Regression Models 6.2. Simple Linear Regression 6.3. Multiple Linear Regression 6.4. Polynomial Linear Regression 7. CLASSIFICATION 7.1. Logistic Regression 7.2. Support Vector Machines 7.3. Decision Trees 7.4. Random Forest Models 8. CLUSTERING 8.1. k-means clustering 8.2. Agglomerative hierarchical clustering 8.3. Projects
Venue and Schedule
Venue : KIIT UNIVERSITY
Schedule : To be notified