Artificial Intelligence Training in Salem

Module 1: Introduction to Data Science

• What is Data Science?
• What is Machine Learning?
• What is Deep Learning?
• What is AI?
• Data Analytics & it’s types

Module 2: Introduction to Python

• What is Python?
• Why Python?
• Installing Python
• Python IDEs

Module 3: Python Basics

• Python Basic Data types
• Lists
• Slicing
• IF statements
• Loops
• Dictionaries
• Tuples
• Functions
• Array
• Selection by position & Labels

Module 4: Python Packages

• Pandas
• Numpy
• Sci-kit Learn
• Mat-plot library

Module 5: Importing Data

• Reading CSV files
• Saving in Python data
• Loading Python data objects
• Writing data to csv file

Module 6: Manipulating Data

• Selecting rows/observations
• Rounding Number
• Selecting columns/fields
• Merging data
• Data aggregation
• Data munging techniques

Module 7: Statistics Basics

• Central Tendency
• Probability Basics
• Standard Deviation
• Bias variance Trade off
• Distance metrics
• Outlier analysis
• Missing Value treatment
• Correlation

Module 8: Error Metrics

• Classification
• Regression

Module 9: Machine Learning

• Supervised Learning
• Linear Regression
• Logistic regression

Module 10: Unsupervised Learning

• K-Means
• K-Means ++
• Hierarchical Clustering

Module 11: SVM

• Support Vectors
• Hyperplanes
• 2-D Case
• Linear Hyperplane

Module 12: SVM Kernel

• Linear
• Radial
• polynomial

Module 13: Other Machine Learning algorithms

• K Nearest Neighbour
• Naïve Bayes Classifier
• Decision Tree CART
• Decision Tree C50
• Random Forest

Module 14: ARTIFICIAL INTELLIGENCE

• Perceptron
• Multi-Layer perceptron
• Markov Decision Process
• Logical Agent & First Order Logic
• AL Applications

Module 15: Deep Learning Algorithms

• CNN Convolutional Neural Network
• RNN Recurrent Neural Network
• ANN Artificial Neural Network

Module 16: Introduction to NLP

• Text Pre-processing
• Noise Removal
• Lexicon Normalization
• Lemmatization
• Stemming
• Object Standardization

Module 17: Text to Features

• Syntactical Parsing
• Dependency Grammar
• Part of Speech Tagging
• Entity Parsing
• Named Entity Recognition
• Topic Modelling
• N Grams
• TF IDF
• Frequency / Density Features
• Word Embedding

Module 18: Tasks of NLP

• Text Classification
• Text Matching
• Levenshtein Distance
• Phonetic Matching
• Flexible String Matching