This Machine Learning course in Bay Area offers an in-depth overview of Machine Learning topics including working with real-time data, developing algorithms using supervised & unsupervised learning, regression, classification, and time series modeling. Learn how to use Python in this Machine Learning training to draw predictions from data.
The Machine Learning market is expected to reach USD $8.81 Billion by 2022, at a growth rate of 44.1-percent, indicating the increased adoption of Machine Learning among companies. By 2020, the demand for Machine Learning engineers is expected to grow by 60-percent.
This course in San Francisco Bay Area will give you a complete overview of Machine Learning methodologies, enough to prepare you to excel in your next role as a Machine Learning Engineer. You will earn Simplilearn’s Machine Learning certification that will attest to your new skills and on-the-job expertise. Get familiar with regression, classification, time series modelling, and clustering.
Online Classroom:
Online Self-Learning:
Yes, we provide 1 practice test as part of our course to help you prepare for the actual certification exam. You can try this Machine Learning Multiple Choice Questions - Free Practice Test to understand the type of tests that are part of the course curriculum.
There is an increasing demand for skilled machine learning engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning training course in San Francisco for the following professionals in particular:
Simplilearn’s Machine Learning course in San Francisco is a hands-on, code-driven training that will help you apply your machine learning knowledge. You will work on 4 projects that encompass 25+ ancillary exercises and 17 machine learning algorithms.
Project 1: Fare Prediction for Uber
Domain: Delivery (Commerce)
Uber, one of the largest US-based taxi cab provider, wants to improve the accuracy of fare predicted for any of the trips. Help Uber by building and choosing the right model.
Project 2: Test bench time reduction for Mercedes-Benz
Domain: Automobile
Mercedes-Benz, a global Germany based automobile manufacturer, wants to reduce the time it spends on the test bench for any car. Faster testing will reduce the time to hit the market. Build and optimize the algorithm by performing dimensionality reduction and various techniques including xgboost to achieve the said objective.
Project 3: Income qualification prediction for Inter-American Development bank
Many social programs have a hard time making sure the right people are given enough aid. It’s tricky when a program focuses on the poorest segment of the population. This segment of the population can’t provide the necessary income and expense records to prove that they qualify. Predicting the right set of people to be included for the aid remains a big challenge for Inter-American Development Bank. Help the bank by building and improving the accuracy of the model using a random forest classifier.
Project 4: Access privileges prediction for Amazon.com employees
There is a considerable amount of data regarding employees’ roles within an organization and the resources to which they have access. Given the data related to current employees and their provisioned access, models can be built that automatically determine access privileges as employees enter and leave roles within a company. These auto-access models seek to minimize the human involvement required to grant or revoke employee access. Help Amazon.com to build such a model and suggest the one with maximum accuracy.
Participants in this Machine Learning online course should have:
201 Spear Street, Suite 1100 San Francisco, CA 94105 United States