machine learning online class mumbai

1. NEAR LEARN Machine Learning classroom training Mumbai

2. INTRODUCTION

3. What is Machine Learning? • Investigation of calculations that enhance their execution at some assignment with experience • Enhance an execution rule utilizing precedent information or past experience. • Job of Statistics: Inference from an example • Job of Computer science: Efficient calculations to • Take care of the improvement issue • Speaking to and assessing the model for deduction

4. Growth of Machine Learning Machine learning is favoured way to deal with • Discourse acknowledgment, Natural dialect handling • PC vision • Therapeutic results investigation • Robot control Computational science This incline is quickening • Enhanced machine learning calculations • Enhanced information catch, organizing, quicker PCs • Programming excessively mind boggling, making it impossible to compose by hand • New sensors/IO gadgets • Interest for self-customization to client, condition • It ends up being hard to extricate information from human experts failure of master frameworks in the 1980's.

5. Applications • Association Analysis • Supervised Learning • Classification • Regression/Prediction • Unsupervised Learning • Reinforcement Learning

6. Supervised Learning • Prediction of future cases: Use the rule to predict the output for future inputs • Knowledge extraction: The rule is easy to understand • Compression: The rule is simpler than the data it explains • Outlier detection: Exceptions that are not covered by the rule, e.g., fraud • Supervised learning involve training data in which there is a desired output.

7. Unsupervised Learning • training data that doesn’t have clear outputs. • Clustering: Grouping similar instances • Other applications: Summarization, Association Analysis • Example applications • Customer segmentation in CRM • Image compression: Color quantization • Bioinformatics: Learning motifs

8. Reinforcement Learning • Reinforcement learning can help machines achieve feats like figuring out how to play video games through trial-and-error, based on working out what increases its score. • No supervised output but delayed reward • Credit assignment problem (what was responsible for the outcome) • Applications: • Game playing • Robot in a maze • Multiple agents, partial observability, ...

9. Examples of Machine Learning Problems • Pattern Recognition • Facial identities or facial expressions • Handwritten or spoken words (e.g., Siri) • Medical images • Sensor Data/IoT • Optimization • Many parameters have “hidden” relationships that can be the basis of optimization • Pattern Generation • Generating images or motion sequences • Anomaly Detection • Unusual patterns in the telemetry from physical and/or virtual plants (e.g., data centers) • Unusual sequences of credit card transactions • Unusual patterns of sensor data from a nuclear power plant • or unusual sound in your car engine or … • Prediction • Future stock prices or currency exchange rates

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