Artificial Intelligence

Artificial Intelligence digital concept
Reading Time: 2 minutes

Artificial Intelligence

    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
    • 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 Kernal

    • 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’s

    Module 18: Tasks of NLP

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