Machine Learning Tensorflow Google Cloud Approach

Machine Learning & Tensorflow – Google Cloud Approach, available at $19.99, has an average rating of 2.75, with 30 lectures, based on 126 reviews, and has 654 subscribers.

You will learn about Learn about basics of Machine Learning and How it could be implemented on Google Cloud This course is ideal for individuals who are Anyone interested in Machine Learning or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. or Anyone willing to learn machine learning on Google cloud platform. or Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning. It is particularly useful for Anyone interested in Machine Learning or Students who have at least high school knowledge in math and who want to start learning Machine Learning. or Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning. or Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets. or Anyone willing to learn machine learning on Google cloud platform. or Any students in college who want to start a career in Data Science. or Any data analysts who want to level up in Machine Learning.

Enroll now: Machine Learning & Tensorflow – Google Cloud Approach

Summary

Title: Machine Learning & Tensorflow – Google Cloud Approach

Price: $19.99

Average Rating: 2.75

Number of Lectures: 30

Number of Published Lectures: 30

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: $34.99

Quality Status: approved

Status: Live

What You Will Learn

Learn about basics of Machine Learning and How it could be implemented on Google Cloud

Who Should Attend

Anyone interested in Machine Learning

Students who have at least high school knowledge in math and who want to start learning Machine Learning.

Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

Anyone willing to learn machine learning on Google cloud platform.

Any students in college who want to start a career in Data Science.

Any data analysts who want to level up in Machine Learning.

Target Audiences

Anyone interested in Machine Learning

Students who have at least high school knowledge in math and who want to start learning Machine Learning.

Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.

Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.

Anyone willing to learn machine learning on Google cloud platform.

Any students in college who want to start a career in Data Science.

Any data analysts who want to level up in Machine Learning.

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Lab 1 : Setting up a GCP Account

Lecture 3: Lab 2 : How to use Cloud Shell

Chapter 2: Datalab

Lecture 1: Lets meet Datalab- Jupyter

Chapter 3: Machine Learning

Lecture 1: Machine learning – The buzz word

Lecture 2: The ML block diagram, Deep learning & neural networks

Lecture 3: Lab : Simple Math in TensorFlow

Lecture 4: Understanding Tensorflow

Lecture 5: Tensors

Lecture 6: Lab : Tensors

Lecture 7: Linear regression – Introduction

Lecture 8: Lab : Regression – Simple Example

Lecture 9: Placeholders and Variables

Lecture 10: Lab : Simple Math with Placeholders

Lecture 11: Lab : Dealing With Variables

Lecture 12: Image processing in TensorFlow

Lecture 13: Lab : Dealing with Images- 1

Lecture 14: Image as Tensors

Lecture 15: Lab : Dealing with Images- 2

Lecture 16: MNIST – Introduction, K- nearest neighbors algorithm

Lecture 17: L1 Distance

Lecture 18: Neural Network in Real Time,Learning regression and XOR

Chapter 4: Regression in Detail

Lecture 1: Linear regression, Gradient Descent

Lecture 2: Logistic Regression,Logit

Lecture 3: Activation function- Softmax, Cost function -Cross entropy,Estimators

Lecture 4: Lab : Taxidemand – 1

Lecture 5: Lab : Taxidemand – 2

Chapter 5: More on Gcloud

Lecture 1: Lab : Creating VM Instance

Lecture 2: Lab : Editing VM instance

Lecture 3: Lab : VM Instance Via Command Line

Instructors

Notez (Rent a Mind)
Notez

Rating Distribution

1 stars: 7 votes

2 stars: 6 votes

3 stars: 13 votes

4 stars: 9 votes

5 stars: 91 votes

Frequently Asked Questions

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