Artificial Intelligence Training by Experts

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Our Training Process

Artificial Intelligence - Syllabus, Fees & Duration

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

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 Kernel

  • 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

Module 18: Tasks of NLP

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

Download Syllabus - Artificial Intelligence
This syllabus is not final and can be customized as per needs/updates
 
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Artificial Intelligence Jobs in Ottawa

Enjoy the demand

Find jobs related to Artificial Intelligence in search engines (Google, Bing, Yahoo) and recruitment websites (monsterindia, placementindia, naukri, jobsNEAR.in, indeed.co.in, shine.com etc.) based in Ottawa, chennai and europe countries. You can find many jobs for freshers related to the job positions in Ottawa.

  • Artificial Intelligence Specialist
  • Senior Data Scientist
  • Data Cloud AI Architect
  • Senior AI Engineer
  • Computer Vision Engineer
  • Project Manager -AI
  • Machine Learning
  • Data Analytics & AI
  • AI Engineer Trainee
  • Data Engineer-AI

Artificial Intelligence Internship/Course Details

Artificial Intelligence internship jobs in Ottawa
Artificial Intelligence Artificial intelligence is an arena of training that looks into how intelligent human behavior can be replicated on a machine. Learn how to create efficient AI, self-sustaining systems, cutting-edge AI models, Deep Q-Learning, A3C, and much more. Now and again, humans make blunders. In some situations, AI-enabled machines are even smarter than humans. Deep Learning is at the heart of AI, which might be a basic software development capability. Even though AI has been studied for more than half a century, we have yet to produce a machine that is as intelligent as a human. We do, however, have several successful applications. These errors can have a significant impact, ranging from wasting money to putting a patient's life in jeopardy. Firms can use this to simulate various scenarios and change the required activities to increase efficiency. Computers, on the other hand, will avoid making these errors if they are correctly programmed.

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