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Tensorflow Machine Learning: with Python

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Tensorflow Machine Learning: with Python

WHAT YOU WILL LEARN

TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library due to numerical computations, which doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, which are communicated between these edges. The name “TensorFlow” is derived from the operations which neural networks perform on multidimensional data arrays or tensors! It’s literally a flow of tensors.

AUDIENCE

Anyone who wants to use Python programming language to do Data Analysis.

PREREQUISITES

Python programming knowledge preferred. To understand tensors well, it’s good to have some working knowledge of linear algebra and calculus.

METHODOLOGY

This program will be conducted with interactive lectures, PowerPoint presentation, discussion and practical exercise.

COURSE OBJECTIVES

You will learn how to:

• Explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.

• Describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.

• Apply TensorFlow to tune the weights and biases while the Neural Networks


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Modules

Module 1: Introduction to Machine Learning

What is Artificial Intelligence

Evolution of Machine Learning

Where do we use machine learning

Types of Machine Learning

Machine Learning vs. Deep learning


Module 2: Machine Learning Features

Concepts to Understand: Classifier

Loss Function, and Optimization

Machine Learning Pipeline

Getting started with Tensorflow

Setting up Tensorflow


Module 3: Framing

Labels and Features

Models

Regression vs. Classification


Module 4: Descending into ML

Linear Regression

Training and Loss

Module 5: Reducing Loss

An Iterative Approach

Gradient Descent

Learning Rate and Optimization

Stochastic Gradient Descent

First Steps with TensorFlow Toolkit


Module 6: Generalization

Peril of Overfitting

The ML fine print

Module 7: Training and Test Sets

Splitting Data Sets

Validation Set

Module 8: Representation

Feature Engineering

Qualities of Good Features

Cleaning Data

Module 9: Feature Crosses

Encoding Nonlienearity

Crossing One-Hot Vectors

Module 10: Logistic Regression and Classification

Calculating a Probability

Loss and Regularization


Module 11: Regularization

L2 Regularization

Lambda

L1 Regularization

Module 12: Neural Nets

• Training Neural Nets and Best Practices

• Multi-Class Neural Nets

o One vs. All

o Softmax


Module 15: Embeddings

Motivation from Collaborating Filtering

Categorical Input Data

Translating to a Lower-Dimensional Space

Obtaining Embeddings


Module 16: Case Study: Machine Predictive Maintenance
Dr.Selvakumar Manickam

Dr.Selvakumar Manickam

He is well known for her highly charged, energetic and power-packed training sessions. Selvakumar also holds many other portfolios in addition to be an affluent trainer. He has authored and co-authored more than 140 articles in journals, conference proceedings and book reviews. He has graduated 9 PhDs. He has successfully completed many research grants and industrial projects. He has given several keynote speeches as well as dozens of invited lectures and workshops at conferences, international universities and for industry. He also lectures in various Computer Science and IT courses which includes development of new courseware in tandem with current technology trend. He also codes in Android, Java, .net, C, PHP and Python in relation to the R&D work that he carried out. Read More

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