AIML-Projects

Projects which were completed as part of assignments of Great Learning's PGP in Artificial Intelligence and Machine Learning

Installations

$ git clone https://github.com/ramanks19/AIML-Projects.git
$ cd AIML-Projects

Projects Done

1. Statistical Learning

• Covers Hypothesis Testing, Data Visualization and Statistical Inference.
  • Project link: Applied Stats
    • This project used Hypothesis Testing and Visualization to leverage customer's health information like smoking habits, bmi, age, and gender for checking statistical evidence to make valuable decisions of insurance business like charges for health insurance.

2. Supervised Machine Learning

• Covers Logistic Regression, Naive Bayes Classifiers and K-NN Classification
  • Project link: Supervised Machine Learning
    • Identified potential loan customers for Thera Bank using classification techniques. Compared models built with Logistic Regression and KNN algorithm in order to select the best performing one.

3. Ensemble Techniques

• Covers EDA, Logistic Regression, KNN Classifiers, SVM Classifiers, Naive Bayes, Ensemble, Decision Trees, Bagging
  • Project link: Ensemble Techniques
    • Involved using classification algorithms and Ensemble techniques to diagnose Parkinson’s Disease (PD) using the patient voice recording data. Various models were used including Naive Bayes, Logistic Regression, SVM, Decision Tree, Bagging etc. and comparison of accuracy across these models was done to finalise the model for prediction.

4. Unsupervised Machine Learning

• Covers Support Vector Machines, Principal Component Analysis, Classification
  • Project link: Unsupervised Learning
    • Classified vehicles into different types based on silhouettes which may be viewed from many angles. Used PCA in order to reduce dimensionality and SVM for classification

5. Featurization, Model Selection & Tuning

• Covers Regression, Decision Trees, Feature Engineering
  • Project link: Feature Engineering Techniques
    • Used feature exploration and selection technique to predict the strength of high-performance concrete. Used regression models like Decision tree regressors to find out the most important features and predict the strength. Cross-validation techniques and Grid search were used to tune the parameters for the best model performance.

6. Recommendation Systems

• Covers Introduction to Recommendation systems, Popularity based model, 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 models were used and evaluated to recommend top-10 product for a user.

7. Neural Networks

• Covers Introduction to Neural Networks, Deep Learning, Keras and Image Recognition
  • 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 - Face Mask Segmentation

• 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, MobileNet
  • Project link: Face Detection
    • Predict and apply masks over the faces within images using CNN and image recognition algorithms. In this hands-on project, the goal is to build a system, which includes building a face detector to locate the position of a face in an image and apply a segmentation mask on the face.

9. Computer Vision - Face Recognition

• Covers Semantic segmentation, Siamese Networks, YOLO, Object & Face Recognition Techniques
  • Project link: Face Recognition
    • 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.

10. Natural Language Processing - Sentiment Analysis

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

11. Natural Language Processing - Sarcasm Detection

• Covers Introduction to Sequential data, Vanishing & Exploding gradients in RNNs, LSTMs, GRUs (Gated recurrent unit), RNNs and its mechanisms, Time series analysis, LSTMs with attention mechanism
  • Project link: Sarcasm Detection
    • The goal of this hands-on project is to analyse the headlines of the articles from news sources and detect whether they are sarcastic or not.

License

Released under MIT License

Copyright (c) 2021 ramanks19