PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive "Backronym"..

+91-8826900551

Code: NES_SK_2256

Duration: 40 Hrs / 4 Weeks / Customized

Mode: Online / Offline / Onsite

Modules Covered in the Course

Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage. All of these things mean it's 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.

• Conceptual understanding of learning applications, formulation of learning tasks as computational problems, and methods that are designed to solve these problems

• Understanding the commonalities and differences between different learning tasks and different approaches to learning

• Understanding the trade-offs in developing solutions

• Thorough understanding of rigorously evaluating the performance of learning algorithms

• Ability to manipulate, extend, and apply machine learning methods and algorithms in the context of real-world problems

Python Programming Fundamental

• Starting Python Using the interpreter Running a Python script
• Python scripts on UNIX/Windows Editors and IDEs
• Using variables Built-in functions Strings
• About flow control White space Conditional expressions While loops Alternate loop exits
• File overview Opening a text file Reading a text file Writing to a text file Reading and writing raw (binary) data

Introduction to Applied Machine Learning

• Why is Machine learning important?
• Statistical learning vs. Machine learning
• Common Machine Learning Applications
• How Do You Create a Machine Learning Algorithm?
• Iteration and evaluation

Machine Learning with Python

• Why use python for machine learning?
• The building of environment on python for machine learning
• Choice of libraries
• Scientific Python Packages Overview
• Introduction to notebooks in python (Ipython, jupyter)

Introduction to Applied Machine Learning

• Fundamentals of Machine Learning
• Supervised Machine Learning
• Unsupervised Machine Learning
• Semi-supervised Machine Learning
• Evaluation and revalidation
• Applications and beyond

Supervised Machine Learning Models

• How supervised learning algorithms work
• Why and where to use supervised learning
• Concepts of learning models
• Bias-variance tradeoff
• How supervised learning algorithms work

Algorithms to be used for supervised learning

• Support Vector Machines
• Linear regression
• Logistic regression
• Naive Bayes
• Decision trees
• K-nearest neighbor algorithm
• Challenges of learning
• Exercises

Un-Supervised Machine Learning Models

• How un-supervised learning algorithms work
• Why and where to use unsupervised learning
• Why and where to use unsupervised learning
• Concepts of learning models
• How un-supervised learning algorithms work

Algorithms to be used for unsupervised learning

• Clustering
• k-Means clustering
• Gaussian mixture models
• Association
• Challenges of learning
• Exercises

Resampling Machine Learning Models

• Cross-validation and the Bootstrap
• Training Error versus Test error
• Training- versus Test-Set Performance
• More on prediction-error estimates
• Validation-set approach
• K-fold Cross-validation
• Exercises

Introduction to Deep Learning

• What is Deep Learning?
• Case studies for deep learning

Practicals

• Implementation of Algorithms on a different dataset
• Working on different phases of the project