Artificial Intelligence AI Training

Module 1: Introduction to Data Science (Duration-1hr)

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 (Duration-1hr)

What is Python?

Why Python?

Installing Python

Python IDEs

Jupyter Notebook Overview

Module 3: Python Basics (Duration-5hrs)

Python Basic Data types

Lists

Slicing

IF statements

Loops

Dictionaries

Tuples

Functions

Array

Selection by position & Labels

Module 4: Python Packages (Duration-2hrs)

Pandas

Numpy

Sci-kit Learn

Mat-plot library

Module 5: Importing Data (Duration-1hr)

Reading CSV files

Saving in Python data

Loading Python data objects

Writing data to csv file

Module 6: Manipulating Data (Duration-1hr)

Selecting rows/observations

Rounding Number

Selecting columns/fields

Merging data

Data aggregation

Data munging techniques

Module 7: Statistics Basics (Duration-11hrs)

Central Tendency

Mean

Median

Mode

Skewness

Normal Distribution

Probability Basics

What does mean by probability?

Types of Probability

ODDS Ratio?

Standard Deviation

Data deviation & distribution

Variance

Bias variance Trade off

Underfitting

Overfitting

Distance metrics

Euclidean Distance

Manhattan Distance

Outlier analysis

What is an Outlier?

Inter Quartile Range

Box & whisker plot

Upper Whisker

Lower Whisker

Scatter plot

Cook’s Distance

Missing Value treatment

What is a NA?

Central Imputation

KNN imputation

Dummification

Correlation

Pearson correlation

ositive & Negative correlation

Module 8: Error Metrics (Duration-3hrs)

Classification

Confusion Matrix

Precision

Recall

Specificity

F1 Score

Regression

MSE

RMSE

MAPE

Data Exchange – Course modules

Hyper Text Transfer Protocol (HTTP)

Detailed course contents:

Introduction to HTTP HTTP Parameters

HTTP Messages HTTP Request HTTP Response HTTP Methods

HTTP Status Code HTTP Header Fields Registration

HTTP Authentication

HTTP Caching

HTTP URL Encoding HTTP Securit

Module 9: Machine Learning

Supervised Learning (Duration-6hrs)

Linear Regression

Linear Equation

Slope

Intercept

R square value

Logistic regression

ODDS ratio

Probability of success

Probability of failure Bias Variance Tradeoff

ROC curve

Bias Variance Tradeoff

Unsupervised Learning (Duration-4hrs)

K-Means

K-Means ++

Hierarchical Clustering

SVM (Duration-2hrs)

Support Vectors

Hyperplanes

2-D Case

Linear Hyperplane

SVM Kernal (Duration-2hrs)

Linear

Radial

polynomial

Other Machine Learning algorithms (Duration-10hrs)

K – Nearest Neighbour

Naïve Bayes Classifier

Decision Tree – CART

Decision Tree – C50

Random Forest

Module 10: ARTIFICIAL INTELLIGENCE

AI Introduction (Duration-9hrs)

Perceptron

Multi-Layer perceptron

Markov Decision Process

Logical Agent & First Order Logic

AL Applications

Module 11: Deep Learning Algorithms (Duration-10hrs)

CNN – Convolutional Neural Network

RNN – Recurrent Neural Network

ANN – Artificial Neural Network

Introduction to NLP (Duration-5hrs)

Syntactical Parsing

Dependency Grammar

Part of Speech Tagging

Entity Parsing

Named Entity Recognition

Topic Modelling

N-Grams

TF – IDF

Frequency / Density Features

Word Embedding’s

Tasks of NLP (Duration-2hrs)

Text Classification

Text Matching

Levenshtein Distance

Phonetic Matching

Flexible String Matching