Best Machine Learning Course Online mumbai

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NEAR LEARN

• Machine Learning classroom training Mumbai

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INTRODUCTION
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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

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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.

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Applications

• Association Analysis
• Supervised Learning
• Classification
• Regression/Prediction
• Unsupervised Learning
• Reinforcement Learning

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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.

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Unsupervised Learning

• training data that doesnt 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

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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, ...

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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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