AI and Deep Learning

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Artificial Intelligence(AL) & Deep Learning

    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
    • Jupyter Notebook Overview

    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

      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

      Positive & Negative correlation

    Module 8: Error Metrics

    • Classification
    • Confusion Matrix
    • Precision
    • Recall
    • Specificity
    • F1 Score
    • Regression
    • MSE
    • RMSE
    • MAPE

    Module 9: Machine Learning

    • Supervised Learning

      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

      K-Means

      K-Means ++

      Hierarchical Clustering

    • SVM

      Support Vectors

      Hyperplanes

      2-D Case

      Linear Hyperplane

    • SVM Kernal

      Linear

      Radial

      polynomial

    • Other Machine Learning algorithms

      K – Nearest Neighbour

      Naïve Bayes Classifier

      Decision Tree – CART

      Decision Tree – C50

      Random Forest

    Module 10: ARTIFICIAL INTELLIGENCE

    • AI Introduction

      Perceptron

      Multi-Layer perceptron

      Markov Decision Process

      Logical Agent & First Order Logic

      AL Applications

    Module 11: Deep Learning Algorithms

    • CNN – Convolutional Neural Network
    • RNN – Recurrent Neural Network
    • ANN – Artificial Neural Network
    • Introduction to NLP

      Text Pre-processing

      Noise Removal

      Lexicon Normalization

      Lemmatization

      Stemming

      Object Standardization

    • Text to Features (Feature Engineering)

      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

      Text Classification

      Text Matching

      Levenshtein Distance

      Phonetic Matching

      Flexible String Matching

    Module 12: Design Effective Reports

    • Enhance report design
    • Add report objects to enhance design
    • Format data and report objects
    • Add a background image to a report
    • Add row numbers to a report

    Module 13: Customize Reports with Conditional Formatting

    • Create multi-lingual reports
    • Highlight exceptional data
    • Show and hide data
    • Conditionally render objects in reports

    Module 14: Analysis Studio

    • Analysis Studio Fundamentals
    • Nest Data in Crosstabs in Analysis Studio
    • Create Analysis with Multiple filter
    • Reusable analysis
    • Build Advanced Crosstabs in Analysis Studio
    • Focus with Filters in Analysis Studio
    • Creating reports from cubes
    • Drill down and drill up

    Module 15: Event Studio

    • Introduction to Event Studio
    • Create an agent
    • Add tasks to an agent
    • Run an agent through its lifecycle
    • Schedule an agent

    Module 16: Business Insight

    • Introdcution to Dashboards
    • Create Dashboard
    • Types of Filter-Value, Slider and advanced filter
    • Overview of RSS Feed and web Page
    • Content Pane
    • Create Widgets
    • Sort, Filter and Calculate data
    • Hands on

    Module 17: Business Insight Advanced

    • Overview of Business Intelligence Advance level
    • Create Different types of Reports
    • Reporting Styles and filters
    • Create dashboard objects
    • Summarize data and Create Calculations
    • Dispatcher and Services

    Module 18: Dispatcher in detail

    • All Services
    • Properties of Services