Pattern Recognition and Machine Learning with R Training Course and Workshop in Bangalore, Mysore, Chennai, Hyderabad, Pune, Mumbai, Delhi, Noida, Gurgaon, Kolkata

This Pattern Recognition and Machine Learning with R training course will provide skills to derive useful hidden pattern in the data using machine learning methods.

By attending Pattern Recognition and Machine Learning with R workshop, delegates will learn to:

Model pattern recognition problems suitable for machine learning.
Apply supervised regression techniques to predict pattern in the data.
Apply supervised classification techniques to classify pattern in the data.
Apply unsupervised clustering techniques to cluster patterns and detect anomaly in the data.
Apply principal component analysis as alternative method to detect pattern in the data.
Apply deep neural network and CNN models for visual recognition.

The Pattern Recognition and Machine Learning with R class is ideal for:

Data Scientist
Machine Learning Engineer
Statistician
R Developer
Quantitative Researcher
Bioinformatics Scientist
Predictive Modeler
Data Analyst (focusing on advanced analytics)
Big Data Specialist (using R)
Algorithm Developer
AI Researcher (using R)
Financial Quantitative Analyst
Business Intelligence Specialist (with R expertise)
Marketing Analytics Specialist (using R)

Overview of Machine Learning

Introduction to Machine Learning
Pattern Recognition Problems Suitable for Machine Learning
Supervised vs Unsupervised Learnings
Types of Machine Learning
Machine Learning Techniques
R Packages for Machine Learning

Regression

What is Regression
Applications of Regression
Least Square Error Minimization
Data Pre-processing
Bias vs Variance Trade-off
Regression Methods with Regularization
Logistic Regression

Classification

What is Classification
Applications of Classification
Classification Algorithms
Confusion Matrix
Classification Performance Evaluation

Clustering

What is Clustering
Applications of Clustering
Distance Measure
Clustering Algorithms
Clustering Performance Evaluation
Anomaly Detection Problem

Principal Component Analysis

Principal Component Analysis (PCA) and Dimension Reduction
Applications of PCA
PCA Workflow

Deep Learning

What is Neural Network
Activation Functions
Loss Function Minimization
Gradient Descent Algorithms and Learning Rate
Deep Neural Network for Visual Recognition
Improve Visual Recognition with Convolutional Neural Network
The Future of AI
AI Ethics

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Pattern Recognition and Machine Learning with R