MACHINE LEARNING – TENSORFLOW

Introduction to TensorFlow

Installing TensorFlow using Docker

Installing Matplotlib

Hello World application with TensorFlow

Basic Statistics

Basic Statistics and Exploratory Analysis

Descriptive summary statistics with Numpy

Summarize continuous and categorical data

Outlier analysis

Machine Learning Introduction

Machine learning essentials

Data representation and features

Distance metrics

Supervised learning

Unsupervised learning

Reinforcement learning

Theano, Caffe, Torch, CGT, and TensorFlow

TensorFlow Essentials

Representing tensors

Creating operators and executing with sessions

Introduction Jupyter notebook for TensorFlow coding

TensorFlow variables

Visualizing data using TensorBoard

ML Algorithm - Linear Regression in TensorFlow

Regression problems

Linear regression applications

Regularization

Available datasets

Coding Linear Regression with TensorFlow - Case study

ML Algorithm - Classification in TensorFlow

Classification problems

Using linear regression for classification

Using logistic regression (including multi-dimensional input)

Multiclass classifiers (such as softmax regression)

Hands on Classification with TensorFlow

ML Algorithm - Clustering in TensorFlow

Traversing files in TensorFlow

K-means clustering

Clustering using a self-organizing map

Simple Neural Networks in TensorFlow

Introduction to Neural Networks

Batch training

Variational, denoising and stacked autoencoders

Reinforcement learning

Concept of Reinforcement Learning

Simple model applying Reinforcement Learning in TensorFlow

Convolutional and Recurrent Neural Networks

Advantages and disadvantages of neural networks

Convolutional neural networks

The idea of contextual information

Recurrent neural networks

Real world predictive model - example

Case study - Stock Market Analysis with TensorFlow

Case study - Stock Market Analysis

Hands on Coding in TensorFlow