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Artificial Intelligence and Machine Learning | Advanced Engineering

Product ID: VNHA3294

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Only 15 seats are available.

Online - Learn from home

30 Hrs

Starting from 20th July 2020

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Add to Calendar 07/20/2020 09:00 AM 06:00 PM India Standard Time Artificial Intelligence and Machine Learning | Advanced Engineering I'm inviting you to join this event. Please take a look. Event name: *Artificial Intelligence and Machine Learning | Advanced Engineering* Link: https://vaanahaa.com/product/artificial-intelligence-and-machine-learning-2-days/ Online - Learn from home

5990 7490( 20% Off )
18% GST applicable*

Developed by: Vaanahaa Education and Innovation Labs

What you'll learn

Concept Session:

  • Introduction to AI
  • Applications of Artificial Intelligence
  • Introduction to Supervised Learning
  • Introduction to Unsupervised Learning
  • Comparison between Supervised & Unsupervised Learning
  • Comparison between Machine Learning & Statistical Modelling
  • Supervised Learning Classification
  • Unsupervised Learning
Practical Session:

  Supervised Learning
  • K-Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Reliability of Random Forests
  • Advantages & Disadvantages of Decision Trees
  • Regression Algorithms
  • Model Evaluation
  • Model Evaluation: Overfitting & Under fitting
  • Understanding Different Evaluation Models
  Unsupervised Learning
  • K-Means Clustering plus Advantages & Disadvantages
  • Hierarchical Clustering plus Advantages & Disadvantages
  • Measuring the Distances between Clusters - Single Linkage Clustering
  • Measuring the Distances between Clusters - Algorithms for Hierarchy Clustering
  • Density-Based Clustering
  Dimensionality Reduction & Collaborative Filtering
  • Dimensionality Reduction: Feature Extraction & Selection
  • Collaborative Filtering & Its Challenges
  • Part 1 - Data Pre-processing
  • Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
  • Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
  • Part 4 - Clustering: K-Means, Hierarchical Clustering
  • Part 5 - Association Rule Learning: Apriori, Eclat
  • Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
  • Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP
  • Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
...Read more
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