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Projects I completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning

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Artificial Intelligence and Machine Learning Projects

Projects completed as a part of Great Learning's PGP - Artificial Intelligence and Machine Learning.

Installation

$ git clone https://github.com/sharmapratik88/AIML-Projects.git
$ cd AIML-Projects

Projects done

1. Statistical Learning

2. Supervised Machine Learning

  • Covers Multiple Variable Linear regression, Logistic regression, Naive Bayes classifiers, Multiple regression, K-NN classification, Support vector machines

3. Ensemble Techniques

  • Covers Decision Trees, Bagging, Random Forests, Boosting
    • Project link: Ensemble Technique

      • Leveraged customer information of bank marketing campaigns to predict whether a customer will subscribe to term deposit or not. Different classification algorithms like Decision tree, Logistic Regression were used. Ensemble techniques like Random forest were used to further improve the classification results.

4. Unsupervised Machine Learning

5. Feature Engineering Techniques

6. Recommendation Systems

  • Covers Introduction to Recommendation systems, Popularity based model, Hybrid models, Content based recommendation system, Collaborative filtering (User similarity & Item similarity)
    • Project link: Recommendation Systems
      • Project involved building recommendation systems for Amazon products. A popularity-based model and a collaborative filtering filtering models were used and evaluated to recommend top-10 product for a user.

7. Neural Networks

  • Covers Gradient Descent, Batch Normalization, Hyper parameter tuning, Tensor Flow & Keras for Neural Networks & Deep Learning, Introduction to Perceptron & Neural Networks, Activation and Loss functions, Deep Neural Networks
    • Project link: Neural Networks
      • SVHN is a real-world image dataset for developing object recognition algorithms with a requirement on data formatting but comes from a significantly harder, unsolved, real-world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images. The objective of the project is to learn how to implement a simple image classification pipeline based on the k-Nearest Neighbour and a deep neural network.

8. Computer Vision

  • Covers Introduction to Convolutional Neural Networks, Convolution, Pooling, Padding & its mechanisms, Transfer Learning, Forward propagation & Backpropagation for CNNs, CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
    • Project link: Face Detection

      • Recognize, identify and classify faces within images using CNN and image recognition algorithms. In this hands-on project, the goal is to build a face recognition system, which includes building a face detector to locate the position of a face in an image and a face identification model to recognize whose face it is by matching it to the existing database of faces.

      image

      mask

      mask

9. Advanced Computer Vision

  • Covers Semantic segmentation, Siamese Networks, YOLO, Object & face recognition using techniques above
    • Project link: Face Recognition

      • Face recognition deals with Computer Vision a discipline of Artificial Intelligence and uses techniques of image processing and deep learning. The objective of this project is to build a face recognition system, which includes building a face detector to locate the position of a face in an image and a face identification model to recognize whose face it is by matching it to the existing database of faces.
      • Aligned Faces Dataset from Pinterest (10k+ images of 100 celebs) - Face recognition model recognises similar faces with an accuracy of 97% and F1 score of 96%.
      • Faces Identified - Results result

10. Statistical NLP (Natural Language Processing)

  • Covers Bag of Words Model, POS Tagging, Tokenization, Word Vectorizer, TF-IDF, Named Entity Recognition, Stop Words
    • Project link: NLP Sentiment Analysis

      • The objective of this project is to build a text classification model that analyses the customer's sentiments based on their reviews in the IMDB database. The model uses a complex deep learning model to build an embedding layer followed by a classification algorithm to analyze the sentiment of the customers.

      Results

11. Sequential NLP (Natural Language Processing)

License

Released under MIT License

Copyright (c) 2020 Pratik Sharma

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