# Machine Learning Tutorial Series

1. Introduction

Basic Mathematics behind traditional data algorithms, Only topics would be explored for theoretical understanding and no code would be written for the same.

- Naive Bayes Classifier
- Term Frequency - Inverse Term Frequency
- Cosine Similarity
- Linear Regression

2. Applications and breakdown,

- Tabular Data
- Images
- Audio
- Video

This breakdown along with the detailed analysis on each component would help you figure out how to work on Machine Learning Models.

Most of the research papers whether in computer vision, speech and text need a very good understanding of four things. All of these require thorough understanding along with the code. The idea is to first process them in 3 line definition format and then experiment with the TensorFlow.

Components

Recurrent Neural Networks

Convolutional Neural Networks

Long Short Term Memory Cells

Gated Recurrent Units

Topology Used

Encoder/Decoder

Bidirectional

Grid LSTM

Tree LSTM

Additional factors

Attention

Normalization

Regularization

Share/Unshare Something

Activation Function

TanH

Rectified Linear Unit

Parametric Rectified Linear Unit

Exponential Linear Unit

Sigmoid

Softplus

3. Conclusions and Rules of Thumb for practical purposes.