🎓 Setup & develop locally in a Jupyter Notebook Server

🎓 Download and Extract train & test Datasets

🎓 Clean, Transform and Manipulate raw Datasets

🎓 Build a neural net and train a model for Object Detection

🎓 Prepare Text Dataset and train an LSTM Model

• Experience with Python 3
• Basic Theoretical ML understanding is beneficial

• We use the twitter sentiment analysis dataset and explore the data with different ways.
• We prepare the text data of tweets by removing the unnecessary things.
• We trained model based on tensorflow with all settings.

1 hour content

• We evaluate thye model with different evaluation measures.
• If you are intereste to work on any text based project, you can simply apply the same methodolgy but might be you will need to change little settings like name of coloumns etc.
• We work on the classification problem and sepcifically we call it binary classification which is two class classification.

1 hours content

• We use the fruits360 dataset and explore the data with different ways.
• We prepare the the images data and extract the features.

1 hours content

• We train model based on tensorflow with all settings.
• We evaluate the model with accuracy and look at the performance of the model with plots.
• If you are interested to work on any image based project, prepare the data as we prepared in this project and there could be some changes in the code like number of classes or loss function.
• We work on the classification problem and sepcifically we call it multi class classification because we are using in total 81 classes of fruits.

1 hours content