Basic Programming
The Objective of this training is to make the delegates understand the concepts and applications of AI domains like NLP, Machine Learning and Deep Learning. The course is covered in three phases: Phase 1 – Machine Learning Concepts(with Python/R based examples), Phase 2 – Introduction to R, Phase 3 – Deep Learning Concepts(with Python/R based examples).
By the end of the training the delegates should be able to understand what machine learning concepts can be applied to a problem at hand and what tools and libraries can be used to achieve the solution.
Introduction to Neural Networks
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
Bias-Variance trade-off
Machine Learning with Python
Choice of libraries
Add-on tools
Machine learning Concepts and Applications
Regression
Linear regression
Generalizations and Nonlinearity
Use cases
Classification
Bayesian refresher
Naive Bayes
Logistic regression
K-Nearest neighbors
Use Cases
Cross-validation and Resampling
Cross-validation approaches
Bootstrap
Use Cases
Unsupervised Learning
K-means clustering
Examples
Challenges of unsupervised learning and beyond K-means
Short Introduction to NLP methods
word and sentence tokenization
text classification
sentiment analysis
spelling correction
information extraction
parsing
meaning extraction
question answering
Getting Started with R
Introduction to R
Basic Commands & Libraries
Data Manipulation
Importing & Exporting data
Graphical and Numerical Summaries
Writing functions
Regression with R
Simple & Multiple Linear Regression
Interaction Terms
Non-linear Transformations
Dummy variable regression
Cross-Validation and the Bootstrap
Subset selection methods
Penalization [Ridge, Lasso, Elastic Net]
Classification with R
Logistic Regression, LDA, QDA, and KNN,
Resampling & Regularization
Support Vector Machine
Resampling & Regularization
Artificial Intelligence & Deep Learning
ANN Structure
Biological neurons and artificial neurons
Non-linear Hypothesis
Model Representation
Examples & Intuitions
Transfer Function/ Activation Functions
Typical classes of network architectures
Feed forward ANN.
Structures of Multi-layer feed forward networks
Back propagation algorithm
Back propagation - training and convergence
Functional approximation with back propagation
Practical and design issues of back propagation learning
Deep Learning
Artificial Intelligence & Deep Learning
Softmax Regression
Self-Taught Learning
Deep Networks
Technical Overview
R v/s Python
Caffe v/s Tensor Flow
Various Machine Learning Libraries