Time & Location
Sep 05, 2019, 4:00 PM – 9:00 PM
Z-Park Silicon Valley, 4500 Great America Pkwy, Santa Clara, CA 95054, USA
About the event
Instructors: Dr. Kiran Gunnam
What you will learn / Topics that will be covered:
1. Intuitive Treatment
• Fundamentals of Machine Learning: Traditional machine learning, Machine learning techniques - Supervised, Unsupervised, Reinforcement, Imitation, Parametric/Non-parametric algorithms, Generative models/Discriminative models
• Overview of machine learning algorithms - Linear Regression, Logistic regression, Support Vector Machines, Nearest Neighbors, Decision Trees, Gaussian Mixture Models, Hidden Markov Models, Dimensionality reduction, Recommender system
• How to build an end to end application - Understanding challenges and selecting right machine learning algorithm, Data Preprocessing, Evaluating Model
• Data Pipelines in Various Applications
2. In-depth Treatment
• Support Vector Machines (SVM)
• Recommender System
3. Hands-on Practice
• Linear Regression
• Logistic regression
• K-Means algorithm
• k-Nearest Neighbor
• Decision trees
• Support vector machines
• Dimensionality reduction
• Recommender systems
Target Audiences:
Engineers, researchers, practitioners and students who are interested in classical machine learning and its implementations. This workshop will particularly benefit people who intend to develop classical machine learning techniques and applications and/or want to pursue a career in data science and machine learning.
Prerequisites:
Basic knowledge of Linear algebra, Probability, and familiarity with Python and Tensorflow basics
Upon completion of this course, you’ll be able to start solving problems using Machine Learning such as medical diagnosis, spam detection, customer segmentation, product recommendation.