AI Solutions ML & DL

Artificial intelligence (AI) is the overarching discipline that covers anything related to making machines smart. Whether it’s a robot, a refrigerator, a car, or a software application, if you are making them smart, then it’s AI. Machine Learning (ML) is commonly used alongside AI but they are not the same thing. ML is a subset of AI. ML refers to systems that can learn by themselves. Systems that get smarter and smarter over time without human intervention. Deep Learning (DL) is ML but applied to large data sets. Most AI work now involves ML because intelligent behavior requires considerable knowledge, and learning is the easiest way to get that knowledge.

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AI - ML Usage

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

  • Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression.
  • Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification.
  • Clustering: K-Means, Hierarchical Clustering
  • Association Rule Learning: Apriori, Eclat
  • Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
  • Natural Language Processing: Sentiment Analysis, ChatBot, Q & A Systems etc...
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AI - DL Usage

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised.

  • Supervised
    • Artificial Neural Networks to solve a Customer Churn problem
    • Convolutional Neural Networks for Computer Vision
    • Recurrent Neural Networks to predict Stock Prices
  • Unsupervised
    • Self-Organizing Maps to investigate Fraud (Feature Detection)
    • Boltzmann Machines to create a Recomender System
    • Stacked AutoEncoders (Used for Recommendation Systems)
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