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Monday, August 19, 2019

In Ten Years: The Future of AI and ML

https://medium.com/foursquare-direct/in-ten-years-the-future-of-ai-and-ml-fa68a527f378
Posted by Thyagu at 9:33 PM
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Data Sets and Codes

  • Exploratory Data Analysis with Python: Medical Appointments Data
  • Python for healthcare modelling and data science
  • ML using Python Manaranjan Pradhan and U Dinesh Kumar

MCQ with Answers

  • Metrics
  • 30 Questions on Logistic Regression
  • Boosting
  • Ensemble Techniques
  • Boosting and Bagging1
  • Ensemble Vote Classifier
  • 40 Questions to ask a Data Scientist on Ensemble Modeling Techniques (Skilltest Solution)
  • V C dimension
  • 40 Questions to test a Data Scientist on Clustering Techniques
  • 40 Questions to test a data scientist on Deep Learning
  • Solutions for Skilltest Machine Learning : Revealed
  • Support Vector Machines Important Questions
  • Time Series
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  • SVM25
  • KNN Algorithm
  • Tree based Algorithms
  • Deep Learning
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NPTEL Machine Learning

  • NPTEL Lecture Notes
  • Assignment1

BOOKS on ML

  • Deep Learning with Python
  • 6 Best Artificial Intelligence & Machine Learning Books in 2019
  • 17 Best New Artificial Intelligence Books To Read In 2019
  • 10 Essential Books
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ML : Articles, Tutorials and Talks

  • A comprehensive guide to putting a machine learning model in production using Flask, Docker, and Kubernetes
  • FLOYDHUB:DLMLAI
  • Machine Learning for Everyone

VTU Machine Learning

  • TGS VTU ML LECTURE NOTES
  • VTU ML SYLLABUS 2015 /16 Scheme
  • Question Bank Module1
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  • ML VTU QP Dec 2018 / Janu 2019

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Blog Archive

  • ►  2020 (4)
    • ►  February (1)
    • ►  January (3)
  • ▼  2019 (56)
    • ►  September (14)
    • ▼  August (42)
      • Lecture29: Python Exercise on SVM
      • Lecture28:SVM: Solution to the Dual Problem
      • Lecture27: Nonlinear SVM and Kernel Function
      • Lecture26: SVM: Maximum Margin with Noise
      • Lecture25 : SVM: The Dual Formulation
      • Lecture24: Introduction Support Vector Machine
      • Lecture23 :Logistic Regression
      • HEALTHCARE ANALYTICS
      • Lecture22 : Tutorial IV
      • Lecture21 : Python Exercise on Naive Bayes
      • Lecture20: Bayesian Network
      • Lecture19: Naive Bayes
      • LECTURE18: Bayesian Learning
      • Lecture17 : Tutorial III : Curse of Dimensionality...
      • Lecture16: Python Exercise on kNN and PCA
      • Lecture15 : Collaborative Filtering
      • Lecture14 : Feature Extraction
      • Lecture 13: Feature Selection
      • Lecture12 : k-Nearest Neighbour
      • Lecture11 : Tutorial II
      • Lecture10: Python Exercise on Decision Tree and Li...
      • Lecture9 : Overfitting
      • Lecture8 :Learning Decision Tree
      • Lecture7 : Introduction to Decision Trees
      • Lecture 6 : Linear Regression
      • Lecture5 : Tutorial I
      • Lecture4 : Evaluation and Cross-Validation
      • Lecture3 : Hypothesis Space and Inductive Bias
      • Lecture2 : Different Types of Machine Learning
      • Lecture1 : Introduction to Machine Learning
      • Machine Learning Analytics Vidhya
      • 30 Questions to test a data scientist on K-Nearest...
      • The 10 Deep Learning Methods AI Practitioners Need...
      • How to build your first Neural Network to predict ...
      • Ridge and Lasso Regression: A Complete Guide with ...
      • In Ten Years: The Future of AI and ML
      • A Guide to Decision Trees for Machine Learning and...
      • Machine Learning is Fun! Part 3: Deep Learning and...
      • Machine Learning for Humans, Part 2.3: Supervised ...
      • The top Machine Learning courses for 2019
      • Machine Learning for Beginners: An Introduction to...
      • Machine Learning in Agriculture: Applications and ...

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