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
- Covers Descriptive Statistics, Probability & Conditional Probability, Hypothesis Testing, Inferential Statistics, Probability Distributions, Types of distribution and Binomial, Poisson & Normal distribution.
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
4. Unsupervised Machine Learning
- Covers K-means clustering, High-dimensional clustering, Hierarchical clustering, Dimension Reduction-PCA4
5. Feature Engineering Techniques
- Covers Exploratory Data Analysis, Feature Exploration and Selection Techniques, Hyperparameter Tuning
6. Recommendation Systems
- Covers Introduction to Recommendation systems, Popularity based model, Hybrid models, Content based recommendation system, Collaborative filtering (User similarity & Item similarity)
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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
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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
9. Advanced Computer Vision
- Covers Semantic segmentation, Siamese Networks, YOLO, Object & face recognition using techniques above
10. Statistical NLP (Natural Language Processing)
- Covers Bag of Words Model, POS Tagging, Tokenization, Word Vectorizer, TF-IDF, Named Entity Recognition, Stop Words
11. Sequential NLP (Natural Language Processing)
- Covers Introduction to Sequential data, Vanishing & Exploding gradients in RNNs, LSTMs, GRUs (Gated recurrent unit), Case study: Sentiment analysis, RNNs and its mechanisms, Time series analysis, LSTMs with attention mechanism, Case study: Machine Translation
License
Released under MIT License
Copyright (c) 2020 Pratik Sharma