Machine Learning & Tensorflow - Google Cloud Approach

Description

Interested in the field of Machine Learning? Then this course is for you!

This course has been designed by experts so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative field of ML.

This course is fun and exciting, but at the same time we dive deep into Machine Learning.

we will be covering the following topics in a well crafted way:

Tensors and TensorFlow on the Cloud - what neural networks, Machine learning and deep learning really are, how neurons work and how neural networks are trained.

-  Datalab, Linear regressions, placeholders, variables, image processing, MNIST, K- Nearest Neighbors, gradient descent, softmax and more

Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.

Course Overview

Module 1- Introduction

Gcloud Introduction Labs

Module 2 - Hands on GCP

Labs

Module 2-Datalab

Module 3-Machine Learning & Tensorflow

Introduction to Machine Learning, Typical usage of Mechine Learning, Types,

The Mechine Learning block diagram, Deep learning & Neural Networks, Labels, Understanding  Tenser Flow, Computational Graphs, Tensors, Linear regression , Placeholders & variables,

Image processing in Tensor Flow, Image as tensors, M-NIST – Introduction, K-nearest neighbors Algorithm, L1 distance, Steps in K- nearest neighbour implementation, Neural Networks in Real Time, Learning regression and learning XOR

Module 4 –Regression in Detail

Linear Regression, Gradient descent, Logistic Regression, Logit, Activation function, Softmax, Cost function -Cross entropy, Labs

Module 12-More on Gcloud

 Labs

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