Machine Learning

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Course Details

Course Features

Instructor led Sessions
The most traditional way to learn with increased visibility, monitoring, and control over learners with ease to learn at any time from internet-connected devices.
Real-life Case Studies
Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.
Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.
Each certification associated with the program is affiliated with the top universities providing edge to gain epitome in the course.
Instructor led Sessions
With no limits to learning and in-depth vision from all-time available support to resolve all your queries related to the course.

Machine Learning

Oranium Tech introducing some amazing content on Python with Machine Learning. Are you seeking to learn the Machine Learning Course? Your wait is over. Enroll at Oranium Tech for Machine Learning Course in Chennai. Flexible Timings! Most Affordable Rate with 100% Placement Support! Join Machine Learning Training in Chennai at Oranium Tech and gain knowledge in Machine Learning techniques
along with Certification. Python includes a modular machine learning library known as PyBrain, which provides easy-to-use algorithms for use in machine learning tasks. The best and most reliable coding solutions require a proper structure and tested environment, which is available in the Python frameworks and libraries

Course Syllabus

• What is ML?
• Visualization
• Data, Problems, and tools
• Matlab
• Linear Classification
• Perceptron update rule
• Perceptron convergence
• Generalization
• Maximum margin classification
• Classification errors
• Regularization
• Logistic regression
• Linear regression, estimator bias, and variance, active learning
• Kernel regression
• Support vector machine (SVM) and kernels
• Kernel optimization
• Model selection
• Model selection criteria
• Description length, feature selection
• Combining classifiers, boosting
• Boosting, margin, and complexity
• Margin and generalization, mixture models
• Mixtures and the expectation-maximization (EM) algorithm

• Clustering
• Spectral clustering, Markov models
• Hidden Markov models (HMMs)
• Bayesian networks
• Learning Bayesian networks
• Probabilistic inference
• Guest lecture on collaborative filtering
• Current problems in machine learning, wrap up

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