Artificial Intelligence Training In Jaipur


Adhoc Networks Pvt. Ltd. is the best artificial intelligence training institute in jaipur. According to the father of Artificial Intelligence, John McCarthy, it is The science and engineering of making intelligent machines, especially intelligent computer programs”.

Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in a similar manner the intelligent humans think.

artificial intelligence training / Course Outline:

Artificial Intelligence

  • Introduction to Artificial Intelligence (AI)
  • History of AI
  • Importance and other Philosophies about AI
  • General Approaches and Goals of AI
  • Components of AI
  • Working Domains/Companies/Products in the Current Market
  • Programming Languages Used for AI
  • Python Programming

Python programming

  • Basic of python and why python for machine learning
  • Installation of software on different OS.
  • Understanding basic syntax with data types
  • Number, String, List, Tuple, Dictionary
  • Extracting data from a file
  • Committing your code to GIT

More about python programming

  • Conditional statement and loops
  • Function and modules
  • File handling
  • Creating own modules/library
  • Web scraping with urllib2
  • Grabbing system information from Popen and os library
  • Scanning Network IP & MAC address with loop

Libraries Used


  • Introduction to Numpy & Matplotlib
  • Managing array with numpy
  • Multidimensional array with numpy
  • Unit matrix handling & creating
  • Deleting indexes from the matrix
  • Deep dive with Matplotlib
  • Drawing general purpose graphs
  • Graphs with mathematics

Machine learning techniques

  • Types of learning
  • Advice of applying machine learning
  • Machine learning System Design
  • Decision Tree Classifier
  • Training your machine with real-time datasets
  • Deep dive with UCI
  • Lab session for loading data from different APIs
  • Detecting data from numpy and converting for training and
  • testing data
  • Exercise with ML and others framework
  • Introduction to iris datasets
  • Understanding iris datasets
  • Modifying and loading with scikit-learn
  • Separating data with numpy
  • Training classifier
  • Algo data process view
  • Decision Tree understanding

Linear Regression

  • Using House Price Prediction
  • Simple Linear Regression
  • Polynomial Linear Regression
  • Cost Function of Linear Regression
  • Understanding linear regression using matrix

Logistic Regression

  • Using Iris dataset to understand logistic regression
  • The concept of linearly separable data
  • Cost Function & Mathematical Foundation
  • Using Iris dataset to understand logistic regression
  • The concept of linearly separable data
  • Cost Function & Mathematical Foundation

Neural Networks analysis

  • Introduction to Neural Network
  • Understanding neural networks
  • Data learning and machine predictions
  • Neural networks real understanding
  • Neural network implementation with real datasets
  • Natural Language Processing
  • Tokenizing text data
  • Converting words to their base forms using stemming
  • Converting words to their base forms using lemmatization
  • Dividing text data into chunks
  • Extracting the frequency of terms using a Bag of Words model
  • Building a category predictor
  • Constructing a gender identifier
  • Building a sentiment analyzer
  • Topic modelling using Latent Dirichlet Allocation

More About ANN

  • Perception
  • Back Propagation/Training Algo’s
  • Convolutional & Recurrent and Artificial Neural Networks
  • Deep Neural Network

Natural Language Processing (NLP)

  • Introduction to NLP
  • Word Representation Model
  • Sentence Classification
  • Language Modeling

Project:- Building AI based ChatBots


Feature Engineering

  • Categorical Features
  • Text Features
  • Image Features
  • Derived Features
  • Imputation of Missing Data
  • Feature Pipelines – Transformer & Estimator

Naive Bayes Classification

  • Bayesian Classification
  • Gaussian Naive Bayes
  • Multinomial Naive Bayes
  • When to Use Naive Bayes
  • Application: Identify category from text

K-Means Clustering

  • Introducing k-Means
  • The understanding cost function for unsupervised algorithms
  • Elbow rule to decide the number of clusters
  • Application: Image compression
  • Application: Detection of number of characters in Arabic

Decision Trees and Random Forests

  • Understanding Decision trees
  • Printing tree
  • Motivating Random Forests: Decision Trees
  • Ensembles of Estimators: Random Forests
  • Random Forest Regression
  • Application: Random Forest for Classifying Digits
  • Other Boosting techniques – AdaBoost, Gradient Tree Boosting

Genetic Algorithms

  • Fundamental concepts in genetic algorithms
  • Generating a bit pattern with predefined parameters
  • Visualising the evolution
  • Solving the symbol regression problem
  • Building an intelligent robot controller

Project: Handwriting recognition with Neural Networks

Building Recommender Systems

  • Extracting the nearest neighbors
  • Building a K-Nearest Neighbors classifier
  • Computing similarity scores
  • Finding similar users using collaborative filtering
  • Building a movie recommendation system


  • Understanding BeautifulSoup
  • Scraping for text, images
  • Searching for Links, Data
  • web data extraction: extracting data from websites.

Image Processing and ML

  • Introduction to Image Processing and
  • How image search is going to work
  • Taking pictures with python for image processing
  • Loading and registering images
  • Object detection
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