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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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 Ballarat, chennai and europe countries. You can find many jobs for freshers related to the job positions in Ballarat.

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Artificial Intelligence Internship/Course Details

Artificial Intelligence internship jobs in Ballarat
Artificial Intelligence Now and again, humans make blunders. Professors need to spend more time one-on-one with pupils, conducting research, and current their own education, but they don't have the time to do so. Deep Learning is at the heart of AI, which might be a basic software development capability. AI uses many algorithms to method knowledge, generate data-driven judgments, and complete tasks in a manner comparable to that of a person. The creation of Ai may necessitate the use of a variety of languages and technologies to create a solution that meets a variety of aims and requirements. 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. When it comes to acquiring and analyzing large analyzing of data to improve potency and personalization, AI is quite effective. Artificial intelligence has the possibility to be a valuable tool for forecasting the outcomes of processes and systems. Computers, on the other hand, will avoid making these errors if they are correctly programmed. Live Project Sessions for hands-on learning.

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