/data_science_for_everyone

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Data Science for Everyone

Welcome everyone to the Data Science for Everyone video request page. Below you'll out TODO list of videos. Some have been requested by viewers others are created out of need or interest.

Video TODO List

Financial Data Science

  • FXCM Trading Platform
  • Trading Strategies
  • Automated Trading
  • Valuation Framework
  • Simulate Finacial Models
  • Derivative Valuation
  • Portfolio Valuation
  • Market-Based Valuation

TensorFlow Certificate Checklist

  • TensorFlow Developer Skills

    • Basic Python Skills (Resolve Issues, Run/Compile programs in PyCharm)
    • Review TensorFlow API Reference
    • Know how to debug/solve error messages from TF API
    • Create ML Models using TF where the model size is reasonable
    • Save ML Models and Check the model file size
    • Understand compatibility discrepancies between different versions of TF

  • Building and Training Neural Network Models with TF 2.0

    • User TensorFlow 2.0
    • Build, Compile and train ML models with TF
    • Preprocessing data to get it ready for use in a model
    • Use models to predict results
    • Build Sequential models with multiple layers
    • Build and train models for binary classification.
    • Build and train models for multi-class categorization.
    • Plot loss and accuracy of a trained model.
    • Identify strategies to prevent overfitting, including augmentation and dropout.
    • Use pretrained models (transfer learning).
    • Extract features from pre-trained models.
    • Ensure that inputs to a model are in the correct shape
    • Ensure that you can match test data to the input shape of a neural network.
    • Ensure you can match output data of a neural network to specified input shape for test data.
    • Understand batch loading of data.
    • Use callbacks to trigger the end of training cycles.
    • Use datasets from different sources.
    • Use datasets in different formats, including json and csv.
    • Use datasets from tf.data.datasets.

  • Image Classification

    • Define Convolutional neural networks with Conv2D and pooling layers.
    • Build and train models to process real-world image datasets.
    • Understand how to use convolutions to improve your neural network.
    • Use real-world images in different shapes and sizes..
    • Use image augmentation to prevent overfitting.
    • Use ImageDataGenerator.
    • Understand how ImageDataGenerator labels images based on the directory structure.

  • Natural Language Processing (NLP)

    • Build natural language processing systems using TensorFlow.
    • Prepare text to use in TensorFlow models.
    • Build models that identify the category of a piece of text using binary categorization
    • Build models that identify the category of a piece of text using multi-class categorization
    • Use word embeddings in your TensorFlow model.
    • Use LSTMs in your model to classify text for either binary or multi-class categorization.
    • Add RNN and GRU layers to your model.
    • Use RNNS, LSTMs, GRUs and CNNs in models that work with text.

  • Time series, sequences and predictions

    • Train, tune and use time series, sequence and prediction models.
    • Train models to predict values for both univariate and multivariate time series.
    • Prepare data for time series learning.
    • Understand Mean Absolute Error (MAE) and how it can be used to evaluate accuracy of sequence models.
    • Use RNNs and CNNs for time series, sequence and forecasting models.
    • Identify when to use trailing versus centred windows.
    • Use TensorFlow for forecasting.
    • Prepare features and labels.
    • Identify and compensate for sequence bias.
    • Adjust the learning rate dynamically in time series, sequence and prediction models.
    • Train LSTMs on existing text to generate text (such as songs and poetry)