Artificial intelligence (AI) has been one of the core research techniques in computer science since the beginning of the field, and in recent years an emergence in research and computational power has made AI development more accessible for developers.

In this certification course, you will learn the foundation of Artificial Intelligence (AI) and the techniques used to make AI solve problems.


You will receive an online access to e-learning (videos), hard copy material is not applicable to this course.


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Why should one take Artificial Intelligence Certification?

This Course is intended for web developers, programmers and graduates wanting to excel in their chosen areas. It is also well suited for those who are already working and would like to take certification for further career progression.

Earning Vskills Artificial Intelligence Certification can help candidate differentiate in today's competitive job market, broaden their employment opportunities by displaying their advanced skills, and result in higher earning potential.


Who will benefit from taking Artificial Intelligence Certification?

Job seekers looking to find employment in IT or software development departments of various companies, students who want to learn Artificial Intelligence.


Companies that hire Vskills Certified Artificial Intelligence Professionals

Artificial Intelligence professionals are in great demand. It is the most popular core research techniques in computer science. Companies like Accenture, KPMG, Amazon, IBM, Ericsson and Wipro etc specializing in Data Science are constantly looking for certified professionals in Artificial Intelligence.


Artificial Intelligence Table of Contents

https://www.vskills.in/certification/artificial-intelligence-table-of-contents

Artificial Intelligence Sample Questions

https://www.vskills.in/certification/artificial-intelligence-sample-questions


 

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TABLE OF CONTENT


Building Your Own Prediction Models

  • Classification Overview and Evaluation Techniques
  • Decision Trees
  • Prediction with Decision Trees and Student Performance Data
  • Random Forests
  • Predicting Bird Species with Random Forests

Applications for Comment Classification

  • The Problem of Text Classification
  • Detecting YouTube Comment Spam with Bag of Words and Random Forests
  • Word2Vec Models
  • Detecting Positive/Negative Sentiment in User Reviews

Deep Learning

  • Neural Networks
  • Identifying the Genre of a Song Using Audio Analysis and Neural Networks
  • Revising the Spam Detector to Use Neural Networks
  • Overview of Deep Learning and Convolutional Neural Networks
  • Identifying Handwritten Mathematical Symbols with Convolutional Neural Networks
  • Revising the Bird Species Identifier to Use Images

Machine Learning for Spam Detection

  • Solving problems with computers
  • Machine Learning: Why should you jump on the bandwagon?
  • Plunging In - Machine Learning Approaches to Spam Detection
  • Spam Detection with Machine Learning Continued
  • Get the Lay of the Land: Types of Machine Learning Problems

Solving Classification Problems

  • Random Variables
  • Bayes Theorem
  • Naive Bayes Classifier
  • Naive Bayes Classifier: An example
  • K-Nearest Neighbours
  • K-Nearest Neighbours: A few wrinkles
  • Support Vector Machines Introduced
  • Support Vector Machines: Maximum Margin Hyperplane and Kernel Trick
  • Artificial Neural Networks: Perceptrons Introduced

Clustering

  • Clustering: Introduction
  • Clustering: K-Means and DBSCAN

Association Detection

  • Association Rules Learning

Dimensionality Reduction

  • Dimensionality Reduction
  • Principal Component Analysis

Regression and supervised learning

  • Regression Introduced: Linear and Logistic Regression
  • Bias Variance Trade-off

Natural Language Processing and Python

  • Applying ML to Natural Language Processing
  • Installing Python - Anaconda and Pip
  • Natural Language Processing with NLTK
  • Natural Language Processing with NLTK - See it in action
  • Web Scraping with BeautifulSoup
  • A Serious NLP Application: Text Auto Summarization using Python
  • Autosummarize News Articles
  • News Article Classification using K-Nearest Neighbors
  • News Article Classification using Naive Bayes Classifier
  • Scraping News Websites
  • Feature Extraction with NLTK
  • Classification with KNN
  • Classification with Naive Bayes
  • Document Distance using TF-IDF
  • News Article Clustering with K-Means and TF-IDF
  • Clustering with K Means

Sentiment Analysis

  • Solve Sentiment Analysis using Machine Learning
  • Sentiment Analysis - What's all the fuss about?
  • ML Solutions for Sentiment Analysis - the devil is in the details
  • Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)
  • Regular Expressions
  • Regular Expressions in Python
  • Put it to work: Twitter Sentiment Analysis
  • Twitter Sentiment Analysis

Decision Trees

  • Using Tree Based Models for Classification
  • Planting the seed - What are Decision Trees?
  • Growing the Tree - Decision Tree Learning
  • Branching out - Information Gain
  • Decision Tree Algorithms
  • Decision Trees predict Survival (Kaggle)

Overfitting

  • Overfitting - the bane of Machine Learning
  • Overfitting Continued
  • Cross Validation
  • Simplicity is a virtue – Regularization
  • The Wisdom of Crowds - Ensemble Learning
  • Ensemble Learning continued - Bagging, Boosting and Stacking

Random Forests

  • Random Forests - Much more than trees
  • Back on the Titanic - Cross Validation and Random Forests

Recommendation Systems

  • Solving Recommendation Problems
  • What do Amazon and Netflix have in common?
  • Recommendation Engines - A look inside
  • What are you made of? - Content-Based Filtering
  • With a little help from friends - Collaborative Filtering
  • A Neighbourhood Model for Collaborative Filtering
  • Top Picks for You! - Recommendations with Neighbourhood Models
  • Discover the Underlying Truth - Latent Factor Collaborative Filtering
  • Latent Factor Collaborative Filtering contd.
  • Gray Sheep and Shillings - Challenges with Collaborative Filtering
  • The Apriori Algorithm for Association Rules

Recommendation Systems in Python

  • Numpy in Python
  • Numpy and Scipy in Python
  • Movielens and Pandas
  • Data Analysis with Pandas
  • Movie Recommendation with Nearest Neighbour CF
  • Top Movie Picks (Nearest Neighbour CF)
  • Movie Recommendations with Matrix Factorization
  • Association Rules with the Apriori Algorithm

Deep Learning and Computer Vision

  • Computer Vision - An Introduction
  • Perceptron Revisited
  • Deep Learning Networks Introduced
  • Handwritten Digit Recognition
“Exam scheduling to be done through user account” / “Exam once scheduled cannot be cancelled”
Date of Examination
05-Jan-2019
06-Jan-2019
19-Jan-2019
20-Jan-2019
02-Feb-2019
03-Feb-2019
16-Feb-2019
17-Feb-2019
02-Mar-2019
03-Mar-2019
16-Mar-2019
17-Mar-2019
06-Apr-2019
07-Apr-2019
20-Apr-2019
21-Apr-2019
Examination Time
01:00 PM - 02:00 PM
02:30 PM - 03:30 PM
04:00 PM - 05:00 PM
05:30 PM - 06:30 PM
10:00 AM - 11:00 AM
11:30 AM - 12:30 PM

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Tags: AI, Artificial Intelligence, Artificial Intelligence online course, Artificial Intelligence certificate, machine learning, MLP