Machine Learning Seminars: Machine learning is an application of Artificial Intelligence based around the idea that we should be able to give machines access to data and let them learn for themselves.
Machine learning makes it possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.
Machine learning applications can pretty much be found everywhere now, including:
- Online fraud detection
- Online recommendation offers such as those from TripAdvisor, Netflix and Amazon
- Knowing what customers are saying about you on Twitter
- Traffic predictions
- Video surveillance
- Email spam and malware filtering
- Online customer support
In the future, machine learning will be used to achieve higher levels of efficiency, particularly when applied to the Internet of Things (IoT).
The financial and business sectors will also benefit. For example, take consumer credit scores. Lenders have traditionally rated creditworthiness based primarily on three factors: total debt, payment history and the length of credit history. But with machine learning algorithms, lenders can predict future behaviors based on those trends and not just rely on history.
Several methods are currently used to fuel machine learning models. But the two most popular are supervised learning and unsupervised learning.
In supervised learning algorithms are trained using labeled examples such as an input where the desired output is known. The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. It then modifies the model accordingly.
Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data. Supervised learning is commonly used in applications where historical data predicts likely future events.
Unsupervised learning is used against data that has no historical labels. The system is not told the “right answer.” The algorithm must figure out what is being shown. The goal is to explore the data and find some structure within. Unsupervised learning works well on transactional data.
Unsupervised learning can be useful for models where an organization wants to identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns.
Machine Learning Seminars
Tonex offers a 3-day Machine Learning Training Bootcamp where participants will learn, comprehend and master ideas on machine learning concepts, key principles, techniques including: supervised and unsupervised learning, mathematical and heuristic aspects, modeling to develop algorithms, linear regression, clustering, classification and prediction.
Who Should Attend
- Anyone whose work interfaces with data analysis who wants to learn key concepts, formulations, algorithms, and practical examples of what is possible in Machine Learning and Artificial Intelligence.
- Managers who need the vision and understanding of the many opportunities, costs, and likely performance hurdles in predictive modeling, especially as they pertain to large amounts of textual (or similar) data.
Why Choose Tonex?
–Course agenda can be tailored to fit the needs of your organization.
–Reasonably priced classes taught by the best trainers is the reason all kinds of organizations from Fortune 500 companies to government’s most important agencies return for updates in courses and hands-on workshops
–Ratings tabulated from student feedback post-course evaluations show an amazing 98 percent satisfaction score.
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