Launching into Machine Learning by Google via Coursera: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

The Launching into Machine Learning programme is a part of the Machine Learning with TensorFlow specialisation, offered by Google Cloud. This Beginner-level online professional course takes about 14 hours to complete. The course of instruction in English and subtitles are also provided for ease of grasping.

The Launching into Machine Learning online course is offered by industry-leading professionals who have had multiple years of work experience in Machine learning and Tensorflow. Thus, you can be assured of being trained by the best in class. Through this course, you will understand the essence of deep learning and how to optimise it to mitigate common problems in machine learning.

The Launching into Machine Learning certification syllabus provides you with repeatable and scalable test datasets perfect for training and evaluation. Moreover, you will learn to evaluate the models through loss functions and performance metrics. What’s more, the flexible schedule allows you to learn at your own pace and convenience.

Course and certificate fees

Launching into Machine Learning fee structure

Head	Amount in INR
1 month	Rs. 4,117
3 month	Rs. 8,234
6 month	Rs. 12,352

certificate availability

Yes

certificate providing authority

Coursera

Eligibility criteria

Certificate Qualifying Details

Besides, you need to complete the Launching into Machine Learning course and participate in all the in-course exercises to receive the certification.

What you will learn

Machine learning

Post-completion of lessons in the Launching into Machine Learning certification course, you will be able to gain conceptual clarity in the following areas:

Determining the importance of deep learning
Mitigation of regularly seen problems in machine learning
Foundational knowledge of Machine learning
Bootstrapping Machine learning codes
Gaining practical knowledge from Machine learning practitioners
Solving data quality issues
Primary types of machine learning
Optimisation of machine learning models

The syllabus

Module 1: Introduction

Video

Course introduction

Module 2: Get to know your data: Improve data through Exploratory Data Analysis

Videos

Introduction
Improve Data Quality
Getting Started with Google Cloud and Qwiklabs
Lab intro: Improve the quality of your data
Lab Demo: Improve the quality of your data
What is exploratory data analysis
How is EDA used in machine learning
Data analysis and visualization
Lab intro: Explore the data using Python and BigQuery

Reading

Resources: Get to know your data: Improve data through Exploratory Data Analysis

Assignment

Get to know your data: Improve data through Exploratory Data Analysis

App items

Lab: Improving data quality
Lab: Exploratory Data Analysis using Python and BigQuery

Module 3: Machine Learning in Practice

Videos

Introduction
Supervised learning
Linear regression
Lab intro: Introduction to linear regression
Lab Demo: Intro to Linear Regression
Logistic regression

Reading

Resources: Machine Learning in Practice

Assignment

Machine Learning in Practice

App item

Lab: Introduction to linear regression

Module 4: Training AutoML Models Using Vertex AI

Videos

Introduction
Machine learning vs. deep learning
What is automated machine learning?
AutoML regression model
Evaluate AutoML models

Reading

Resources: Training AutoML Models Using Vertex AI

Assignment

Training AutoML Models Using Vertex AI

Module 5: BigQuery Machine Learning: Develop ML Models Where Your Data Lives

Videos

Introduction
Training an ML model using BigQuery ML
BigQuery Machine Learning supported models
Lab intro: Using BigQuery ML to predict penguin weight (BigQuery ML & Explainable AI)
Lab Demo: Using BigQuery ML to predict penguin weight (BigQuery ML & Explainable AI)
BigQuery ML hyperparameter tuning
How to build and deploy a recommendation system with BigQuery ML

Reading

Resources: BigQuery Machine Learning: Develop ML Models Where Your Data Lives

Assignment

BigQuery Machine Learning: Develop ML Models Where Your Data Lives

App item

Lab: Using BigQuery ML to predict penguin weight

Module 6: Optimization

Videos

Introduction
Defining ML models
Introducing the course dataset
Introduction to loss functions
Gradient descent
Troubleshooting loss curves
ML model pitfalls
Lecture lab: Introducing the TensorFlow Playground
Lecture lab: TensorFlow Playground - Advanced
Lecture lab: Practicing with neural networks
Performance metrics
Confusion matrix

Reading

Resources: Optimization

Assignment

Optimization

Module 7: Generalization and Sampling

Videos

Introduction
Generalization and ML models
When to stop model training
Creating repeatable samples in BigQuery
Demo: Splitting datasets in BigQuery

Reading

Resources: Generalization and Sampling

Assignment

Generalization and Sampling

Module 8: Summary

Readings

Summary
Resource: All quiz questions
Resource: All readings
Resource: All slides

Admission details

Step 1: Visit the course webpage to register for the Launching into Machine Learning certification course.

Step 2: Now, click on the ‘Enroll’ button present on the webpage and then tap on the ‘Select Free Trial’ option. If you are a first-time user, you also need to enter your username, email address and set a password to authenticate your account.

Step 3: Enter your card details and complete the payment to sign up for Coursera plus. Besides the 7-day access to the course, you can enjoy unlimited access to multiple other courses.

Step 4: Post-completion of the registration process, you can use your login credentials to sign in to the platform and finish the course.

Filling the form

You don’t have to fill a dedicated application form to register for the Launching into Machine Learning training course. You can enrol for free by signing up with Google, Apple ID, or Facebook and access the course material.

Scholarship Details

Candidates who cannot afford to pay for the Launching into Machine Learning online course can place their application to avail financial aid. This can be done by clicking on the ‘Financial aid available’ option present under the ‘Enroll’ option on the course webpage. The application takes 15 days to be processed, and Coursera notifies you if your application has been accepted.

How it helps

The Launching into Machine Learning certification course equips candidates with a 360-degree knowledge of deep learning and neural networks. More than 37% of the students who have registered for the online course have reported having received lucrative career opportunities after completing the course. Moreover, the flexibility of schedules, deadlines makes learning all the more convenient!

FAQs

How to preview the Launching into Machine Learning online course?

Yes, candidates can preview the course by visiting the course page. Upon reaching the webpage, you can watch the first video of each module in the syllabus contents.

When will I receive the course certificate?

Upon completing the course, the e-certificate will be available on the Accomplishments page present on the portal.

Can I audit the course?

No, the Launching into Machine Learning online course cannot be audited. It can only be availed after subscribing to Coursera plus or when you receive financial support.

Will I earn credit for completing the course?

Although the course does not carry any university credits, some universities might accept the course certificate as credit. You can check with your educational institution to find out more.

What will I receive post-enrollment?

Post-enrolment, you will get access to all the course contents, including videos, quizzes, study materials and other in-course exercises. Once you complete the course and finish the graded assignments, you will be eligible for certification.