Artificial Intelligence Online Training

You may learn Artificial Intelligence and Machine Learning skills like Data Science, CNN, perceptron, TensorFlow, Neural Networks, NLP, etc. with our online Artificial Intelligence course in Chennai utilising TensorFlow in conjunction with CCE, IIT Madras. To become a successful artificial intelligence engineer, enrol in the top online artificial intelligence programme taught by Top professors.

Artificial Intelligence Course Description

Needintech provides a thorough Artificial Intelligence programme that will assist you in working with the most cutting-edge technologies available today (AI). As part of this top AI training, you will learn how to use Python to script Machine Learning programmes, as well as various components of artificial neural networks, supervised and unsupervised learning, logistic regression with a neural network approach, binary classification, and vectorization. 

Mock Interviews

Needintech's mock interviews provide a platform for you to prepare for, practise for, and experience a real-life job interview. You will have an advantage over your colleagues if you familiarise yourself with the interview environment beforehand in a comfortable and stress-free environment.

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Course Objetives
  • Professionals working in the domains of analytics, Data Science, e-commerce, search engine, etc.
  • Software professionals and new graduates seeking a career change.

This course’s main objective is to acquaint you with every facet of AI so you may begin working as an artificial intelligence engineer. Among the various subjects and modules you will learn in the programme are the following:



  • The fundamentals of deep learning methods.
  • The knowledge of artificial neural networks.
  • Utilising the training data to train a neural network.
  • Applications of convolutional neural networks.
  • Processing units for TensorFlow and Tensor.
  • Methods of supervised and unsupervised learning.
  • Python is used in machine learning.
  • Deep learning applications in image recognition, NLP, etc.
  • Actual initiatives in recommender systems, etc.
  • All students who successfully finish the training receive active placement support from Needintech. We have exclusive partnerships with more than 80 leading MNCs worldwide for this. By doing this, you can land jobs at top companies like Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, and Cisco, among other amazing companies. We also assist you with preparing for job interviews and resumes.
  • By gaining all the necessary abilities an AI expert should have, you can become an AI engineer. You may accomplish it with the aid of Needintech’s artificial intelligence and machine learning courses. Participate in the online training course to gain a thorough understanding of the subject. You will be prepared to take the certification exam once you have finished the course and all of the projects.
  • Simply, artificial intelligence is the ability displayed by robots to carry out jobs that would normally be completed by people. Deep learning and machine learning are used to accomplish this.

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Syllabus of Artificial Intelligence Course in Training

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


  • 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

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