Machine Learning

Table of Contents----

• ML Projects
• Tensorflow

ML_Projects:

• Alexnet Architecture: Implement the popular Alexnet Architecture (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/alexnet_architecture.ipynb)
• Article Recommendation System: Recommend article based on the content of the article user already read. (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/article_recommendation_system.ipynb)
• Classify using Neural Network: Build a Neural Network to classify popular MNIST Fashion Dataset. (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/classification_with_neural_network.ipynb)
• Cryptocurrency Price Prediction: Predict the next 30 days price using AutoTS. (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/cryptocurrency_price_prediction.ipynb)
• Flipkart Reviews Sentiment Analysis: Analyze the flipkart reviews sentiment using SentimentIntensityAnalyzer from NLTK. (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/flipkart_reviews_sentiment_analysis.ipynb)
• Future Sales Prediction: using Linear Regression. (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/future_sales_prediction.ipynb)
• Netflix Stock Price Prediction: using LSTM. (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/netflix_stock_price_prediction.ipynb)
• Online Payment Fraud Detection: using Decission Tree Classifier. (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/online_payment_fraud_detection.ipynb)
• Stock Price Prediction: using Linear Regression. (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/stock_price_prediction.ipynb)
• Stock Price Prediction with LSTM: Apple's stock price prediction analyzing correlation with LSTM. (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/stock_price_prediction_with_lstm.ipynb)
• Stress Detection: using BernoulliNB and MultinomialNB. (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/stress_detection.ipynb)
• Time Series Analysis: Analyze apple's monthly sales data by ploting using- Candlestick, Barplot, Lineplot, Candlestick Chart. (https://github.com/abs-sayem/machine_learning/blob/main/ml_projects/time_series_analysis.ipynb)

Tensorflow:

• Installation: tensorflow installation, gpu support, verification (https://github.com/abs-sayem/machine_learning/blob/main/tensorflow/01.installation.md)
• Tensor Basics: create tensor, cast tensor, operations on tensor, string tensor, variable tensor (https://github.com/abs-sayem/machine_learning/blob/main/tensorflow/02.tensor_basics.ipynb)
• 1st Neural Network: nn basics, design a one layer neural network, train on mnist dataset (https://github.com/abs-sayem/machine_learning/blob/main/tensorflow/03.1st_nn.ipynb)
• Linear Regression: create a dataset and use simple and multiple linear regression for prediction (https://github.com/abs-sayem/machine_learning/blob/main/tensorflow/04.linear_regression.ipynb)
• Convolutional Neural Network: cnn basic, design a basic cnn for image classification, trained on cifar10 dataset (https://github.com/abs-sayem/machine_learning/blob/main/tensorflow/05.cnn.ipynb)
• Save and Load Model: save a pretrained nn model in several ways and load the saved model in different ways (https://github.com/abs-sayem/machine_learning/blob/main/tensorflow/06.save_load_model.ipynb)