Tag: Regression

Course Overview

Selenium WebDriver is a powerful web framework that allows you to execute cross-browser tests. By learning this tool, you can create robust, browser-based regression automation suites and tests, all while scaling and distributing scripts across many different environments. This is a recipe course, meaning you can directly go to the part that interests you. For example, if you are testing a multiple select list and don’t know how, you can look it up in the course roadmap and proceed with that lesson. With over 170 recipes for web application testing, you’ll have the opportunity to learn and practice both beginning and advanced techniques with Selenium WebDriver. By the time you complete this course, you will have a great new skill, and you’ll save tons of time when it comes to automating real user interactions in Firefox, Safari, Edge, Chrome, Internet Explorer and more!

Course Contents

1. Getting Started

• What is this Course About?
• Introduction to Selenium WebDriver
• Mocha: A JavaScript-Based Test Framework
• Selenium with JavaScript Binding
• Running Your First WebDriver Recipe
• Quiz Yourself on Selenium Concepts

2. Working with Locators

• Locators
• Locating Web Elements
• Advanced Techniques
• Quiz Yourself on Locators

3. Working with Hyperlinks

• Hyperlinks
• Clicking a Link By Text, ID, and XPath
• Clicking the Nth Link and Link Verifications
• Getting Link Attributes and Tabs Switching
• Quiz Yourself on Hyperlinks

4. Working with Buttons

• Buttons
• Clicking a Button By ID, Name, Image, and Javascript
• Form Submissions
• Button Assertions
• Quiz Yourself on Buttons

5. Working with Text Fields and Areas

• Text Fields and Text Areas
• Entering Text Into Text Fields and Text Areas
• Clearing a Text Field and Focusing On a Control
• Handling Read-Only, Disabled or Hidden Fields
• Quiz Yourself on Text Field and Text Areas

6. Working with Radio Buttons and Radio Groups

• Radio Buttons
• Working with Radio Buttons
• Working with Radio Groups
• Working with Customized Radio Buttons
• Quiz yourself on Radio buttons

7. Working with Checkboxes

• Checkboxes
• Checking a Box by Name and ID
• Unchecking and Asserting a Checkbox
• Handling a Customized Checkbox
• Quiz Yourself on Checkboxes

8. Working with Select Lists

• Select Lists
• Selecting a Single Option From a Select List
• Selecting Multiple Options From a Select List
• Clearing an Option From a Select List
• Select List Assertions
• Quiz Yourself on Select Lists

9. Page Navigations and Browser Management

• Page Navigation with Selenium
• Browser Management with Selenium
• Advanced Techniques
• Quiz Yourself on Page Navigation and Browser Management

10. Assertions

• Page Assertions
• Text Assertions
• Table Assertions
• Miscellaneous Assertions
• Quiz Yourself on Assertions

11. Working with Frames

• Framei
• Frame
• Quiz Yourself on Frame

12. Working with AJAX

• AJAX
• Wait Within a Time Frame
• Wait Until Timeout
• Wait Until AJAX Call Completes
• Quiz Yourself on AJAX

13. Working with File Uploads and Pop-Up Dialogs

• Uploading a File
• Handling JavaScript Pop-Up Dialogs
• Handling Modal Style Dialogs
• Quiz Yourself on Dialogs

14. Debugging Test Scripts

• Debugging by Printing Texts
• Debugging with Mocha
• Miscellaneous Techniques
• Quiz Yourself on Debugging Test Scripts

15. Testing Data

• Testing Numeric Data
• Testing Alphabetic Data
• Testing Alphanumeric Data
• Testing Miscellaneous Data
• Quiz Yourself on Testing Data

16. Working with Browser Profiles and Capabilities

• Browser Profiles and Capabilities
• Running Browsers Headlessly
• Verifying File Downloads
• Bypassing Authentications
• Quiz Yourself on Browser Profiles and Capabilities

17. Working with Advanced User Interactions

• User Interactions
• Keyboard Interactions
• Mouse Interactions
• Quiz Yourself on Advanced User Interactions.

18. Working with HTML5 and Dynamic Websites

• Working with HTML5 Email and Time Field
• Working with JavaScript Events
• Working with Select2
• Working with Frameworks
• Working with HTML5 Geolocation
• Working with HTML Canvas
• Quiz Yourself on HTML5 & Dynamic Web Concepts

19. “What You See Is What You Get” HTML Editors

Working with ‘WYSIWYG’ Editors

20. Leverage Programming

• Why Programming?
• Ignorable Test Errors and External File Reading
• Data-Driven Tests
• Working with Dynamically Generated IDs, Special Keys, and Unicode
• Dynamic Data Extraction

21. Optimizations

• Optimization Techniques: Part 1
• Optimization Techniques: Part 2

22. Gotchas

• Common Test Execution Failures
• Tag Error and Element Not Clickable

23. Appendix A: Miscellaneous

• Working with Material Design Web App
• Working with Selenium Server
• Working with Selenium Grid

24. Appendix B: Installations

• NodeJS
• Selenium and Mocha
• BrowsersIDEs

25. Wrapping Things Up

Afterword

How You’ll Learn

Hands-on Coding Environments

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

2x Faster Than Videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

No Set-up Required

Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.

Progress You Can Show

Built in assessments let you test your skills. Completion certificates let you show them off.

About this Course

After completing this course, you will have an understanding of the fundamental principles and processes of software testing. You will have actively created test cases and run them using an automated testing tool. You will being writing and recognizing good test cases, including input data and expected outcomes.

After completing this course, you will be able to… – Describe the difference between verification and validation. – Explain the goal of testing. – Use appropriate test terminology in communication; specifically: test fixture, logical test case, concrete test case, test script, test oracle, and fault. – Describe the motivations for white and black box testing. – Compare and contrast test-first and test-last development techniques. – Measure test adequacy using statement and branch coverage. – Reason about the causes and acceptability of and poor coverage – Assess the fault-finding effectiveness of a functional test suite using mutation testing. – Critique black-box and white-box testing, describing the benefits and use of each within the greater development effort. – Distinguish among the expected-value (true), heuristic, consistency (as used in A/B regression), and probability test oracles and select the one best-suited to the testing objective. – Craft unit and integration test cases to detect defects within code and automate these tests using JUnit. To achieve this, students will employ test doubles to support their tests, including stubs (for state verification) and mocks (for behavioral verification) (https://martinfowler.com/articles/mocksArentStubs.html). This course is primarily aimed at those learners interested in any of the following roles: Software Engineer, Software Engineer in Test, Test Automation Engineer, DevOps Engineer, Software Developer, Programmer, Computer Enthusiast. We expect that you should have an understanding of the Java programming language (or any similar object-oriented language and the ability to pick up Java syntax quickly) and some knowledge of the Software Development Lifecycle.

WHAT YOU WILL LEARN

• You will gain an understanding of the theory of testing.
• You will practice writing tests for a variety of quality intent, including code coverage, defect finding, and statistical testing.
• You will develop test plans to guide the testing stage of the software development lifecycle.
• You will create defect reports to provide transparency and understanding to supervisors, colleagues, and users.

SKILLS YOU WILL GAIN

• Writing Test Plans
• Writing Defect Reports
• Understanding of Testing Theory
• Writing Tests
• Testing Vocabulary

Syllabus – What you will learn from this course

WEEK1

7 hours to complete

Introduction

In this module, you will be introduced to the basics of testing, especially the variety of terminology to be used through the rest of the course.

6 videos (Total 66 min), 1 reading, 6 quizzesSee All

WEEK2

5 hours to complete

Testing Foundations

In this module, you will investigate a variety of testing principles, models of testing, and types of systematic testing strategies.

8 videos (Total 57 min)See All

WEEK3

8 hours to complete

Testing in the Software Development Lifecycle

In this module, you will learn about the social aspects of testing. We will learn about test plans, testing status reports, and defect reporting.

10 videos (Total 49 min)See All

WEEK4

10 hours to complete

Writing Good Unit Tests

In this module, you will learn about writing unit tests and gain practice in writing these tests through three coding assignments, each with additional testing sophistication.

• Test Doubles: Introduction11m
• Test Doubles: Input18m
• Test Doubles: Output14m
• Assessing Adequacy and Code Coverage Analysis with JaCoCo9m
• Flakey Tests and How to Avoid Them22m

3 practice exercises

Test Doubles: IntroductionTest Doubles: InputTest Doubles: Output

About this Course

This course introduces state-of-the-art techniques for automated analysis. Automated analysis encompasses both approaches to automatically generate a very large number of tests to check whether programs meet requirements, and also means by which it is possible to *prove* that software meets requirements and that it is free from certain commonly-occurring defects, such as divide-by-zero, overflow/underflow, deadlock, race-condition freedom, buffer/array overflow, uncaught exceptions, and several other commonly-occurring bugs that can lead to program failures or security problems. The learner will become familiar with the fundamental theory and applications of such approaches, and apply a variety of automated analysis techniques on example programs.

After completing this course, a learner will be able to: – Understand the foundations of automated verification: randomization and symbolic representations – Distinguish the strengths and weaknesses of random testing, symbolic analysis, static analysis, and model checking – Use a variety of state-of-the-art static analysis and automated testing tools for automated verification – Create executable requirements as an oracle suitable for automated testing and symbolic analysis – Understand how the choice of oracle affects fault-finding for automated analysis strategies. – Use automated testing to achieve full mutation coverage – Create a test plan that utilizes both manually-written tests and automated tests towards maximizing rigor, minimizing effort and time, and minimizing test costs. This course is intended for learners interested in understanding the principles of automation and the application of tools for analysis and testing of software This knowledge would benefit several typical roles: Software Engineer, Software Engineer in Test, Test Automation Engineer, DevOps Engineer, Software Developer, Programmer, Computer Enthusiast. We expect that you have some familiarity with the Software development Life-Cycle, an understanding of the fundamentals of software testing, similar to what is covered in the Introduction to Software Testing and Black-box and White-Box Testing Courses. Familiarity with an object-oriented language such as Java or ability to pick-up Java syntax quickly to write and modify code, and willingness to use tools and IDEs are assumed.

SKILLS YOU WILL GAIN

• Software Testing
• Formal Verification
• Test Automation

Syllabus – What you will learn from this course

WEEK1

6 hours to complete

Introduction to Automated Analysis

In this module we will learn about a range of techniques for analysis of programs and methods to automate testing. Along the way we will learn how to specify properties of interest to check about a program and capture assumptions about the environment for effective testing. To reinforce some of the important concepts learned we will practice automated testing using effective tools on a concrete example.

8 videos

• Introduction to Automated Analysis13m
• Automated Analysis Techniques9m
• Symbolic Representations12m
• Property Specification8m
• Environmental Specification and Assumptions6m
• Parameterized Unit Testing using junit-quickcheck12m
• Environmental Specifications in junit-quickcheck2m
• (Optional) Installation of Eclipse and Gradle2m

1 reading

Overview and Syllabus10m

5 practice exercises

• Introduction to Automated Analysis30m
• Automated Analysis Techniques30m
• Symbolic Representations30m
• Property Specification30m
• Environmental Specifications and Assumptions

WEEK2

6 hours to complete

Automated Test Generation

The focus of this module is to understand how various techniques can help us automate the generation of useful and numerous tests. We will examine ways to specify and use properties of the system and the environment to guide the generation of test data.

• Overview of Automated Test Generation6m
• Automated Test Generation using Random Testing14m
• Automated Test Generation using Symbolic Execution15m
• Automated Test Generation using Metaheuristic Search16m
• Property-Based Testing for Real-Time Systems6m
• Biasing Input Values in junit-quickcheck6m
• Using Generators to Create Complex Inputs in junit-quickcheck9m
• Explanation of Test Harness and Assignment for Microwave Example8m

4 practice exercises

• Overview of Automated Test Generation7m
• Automated Test Generation using Random Testing30m
• Automated Test Generation Using Metaheuristic Search12m
• Property-Based Testing for Real-Time Systems30m

WEEK3

Static Analysis

5 hours to complete

The goal of this module is to introduce the learner to the principles of statically analyzing programs, understand how analysis techniques work by looking at some example analyses, and some good practices to follow when designing programs to enable the tools to help us detect and avoid defects. The learner will gain an understanding of using static analysis tools by looking at one concrete tool.

5 videos

• What is Static Analysis14m
• Dataflow Analysis16m
• Program Wellformedness Properties12m
• Designing programs for analyzability9m
• Static Analysis with Infer11m

1 reading

Analysis Exercise with Infer2h

3 practice exercises

• What is static analysis?30m
• Designing programs for analyzability30m
• Summative Review30m

WEEK4

3 hours to complete

Effective Automated Verification

This module will examine how to use effective automation techniques for a variety of purposes such as performing effective regression testing, discovering security vulnerabilities and monitoring software at run-time for critical properties.

6 videos

• Automating Regression Testing10m
• Automating Security Testing Using Fuzz Testing14m
• Runtime Monitoring7m
• Where Automation Fails9m
• Using Multiple Methods Effectively9m
• The Evolution of Software Testing10m

2 readings

• Fuzz Testing with AFL10m
• Runtime Monitoring Tools10m

4 practice exercises

• Automating Regression Testing30m
• Automating Security Testing Using Fuzz Testing30m
• Runtime Monitoring30m
• Summative Review of Week 4 material

Introduction to Machine Learning and Deep Learning

PyTorch Basics: Tensors & Gradients

Linear Regression with PyTorch

Working with Image Data in PyTorch

Image Classification using Convolutional Neural Networks

Residual Networks, Data Augmentation and Regularization Techniques

Generative Adverserial Networks

Requirements

• Basic Linear Algebra (matrix multiplication)
• Basic Python Programming
• Basic Calculus (Derivatives)

Description

“Deep Learning with PyTorch for Beginners is a series of courses covering various topics like the basics of Deep Learning, building neural networks with PyTorch, CNNs, RNNs, NLP, GANs, etc. This course is Part 1 of 5.

Topics Covered:

1. Introduction to Machine Learning & Deep Learning
2. Introduction on how to use Jovian platform
3. Introduction to PyTorch: Tensors & Gradients
4. Interoperability with Numpy
5. Linear Regression with PyTorch
    – System setup
    – Training data
    – Linear Regression from scratch
    – Loss function
    – Compute gradients
    – Adjust weights and biases using gradient descent
    – Train for multiple epochs
    – Linear Regression using PyTorch built-ins
    – Dataset and DataLoader
    – Using nn.Linear 
    – Loss Function
    – Optimizer
    – Train the model
    – Commit and update the notebook
7. Sharing Jupyter notebooks online with Jovian

Who this course is for:

• Beginner Python developers curious about Deep Learning and PyTorch

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Course content

You Will Learn how to implement some of the most common machine learning algorithms in PHP

You will Learn about the some of the common algorithms like classification, regression, clustering

You will learn about Supervised and Unsupervised learning

You will NOT learn the details and mathmatics of each algorithm. Our focus is mainly on implementing them in PHP

You will Learn about the steps to build a machine learning model

You will Learn how to divide your data to training set and test set

You will Learn how to train your machine learning model

You will Learn how to make prdictions

You will learn about the persistency of your model

Requirements

• Basic knowledge of Machine learning is a plus because we are not going though the details of each algorithm and our main focus is on the implementation in PHP

Description

WHY Machine Learning

Machine learning is a rapidly growing field that is changing the way technology and solving complex problems.

Machine learning is widely used in industries such as healthcare, finance, marketing, and self-driving cars to automate processes, improve decision making, and provide personalized experiences to customers.

The amount of data generated by society is continually growing, further increasing the demand for skilled machine learning practitioners.

Learning machine learning provides valuable skills for a career in technology and data science.

The demand for machine learning talent is growing at a rapid pace, with the number of job postings for machine learning roles increasing by over 75% in the past 5 years.

WHY PHP

PHP is used in more than 70 percent of the websites across the Internet. That’s HUGE!

PHP is more alive than ever! It’s simple yet very powerful. It’s secure. It’s scalable.It’s very easy to learn.

Just to get an idea of how powerful PHP is, Websites like Facebook, Wikipedia, Slack, MailChimp, Flickr, SourceForge, Tumblr, Etsy and Yahoo have PHP as their core.

oh and not to forget, the biggest blogging system on the web (WordPress), is powered by PHP.

enough teasing let’s get started with Machine Learning in PHP.

In this course:

• You Will Learn how to implement some of the most common machine learning algorithms in PHP
• You will Learn about the some of the common algorithms like classification, regression, clustering
• You will learn about Supervised and Unsupervised learning
• You will NOT learn the details and mathmatics of each algorithm. Our focus is mainly on implementing them in PHP
• You will Learn about the steps to build a machine learning model
• You will Learn how to divide your data to training set and test set
• You will Learn how to train your machine learning model
• You will Learn how to make prdictions
• You will learn about the persistency of your model
• and a lot more

Prior Knowledge

Basic knowledge of Machine learning is a plus because we are not going though the details of each algorithm and our main focus is on the implementation in PHP

Basic Knowledge about PHP is a plus.

This Course is for:

• PHP Developers who want to start their journey in Machine Learning
• Developers who are familiar with Machine Learning and want to learn how to implement them in PHP
• Curious to learn about Machine Learning

If this is you, then what are you waiting for?! Let’s get Started

Who this course is for:

• PHP Developers who want to start their journey in Machine Learning
• Developers who are familiar with Machine Learning and want to learn how to implement them in PHP
• Curious to learn about Machine Learning

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Course content

Apply Machine Learning to Web sites, Mobile Apps

Requirements

• Beginners level knowledge for working with Data .
• Programming knowledge not required.

Description

The main purpose of this course is to give students the ability to analyze and present data by using Azure Machine Learning, and to provide an introduction to the use of machine learning and big data.

By- 
Uditha Bandara is specializes in Microsoft Development technologies.  He is the South East Asia`s First XNA/DirectX MVP (Most Valuable Professional).  He had delivered sessions at various events and conferences in Hong Kong, Malaysia, Singapore, Sri Lanka and India. 

Who this course is for:

• Anyone Interested to learn Machine Learning

Course content

Supervised learning

Unsupervised learning

Regression learning

SVM

Requirements

• install numpy matplotlib and pandas

Description

Machine Learning tutorial provides basic and advanced concepts of machine learning. Our machine learning tutorial is designed for students and working professionals.

Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognition, speech recognition, email filtering, Facebook auto-tagging, recommender system, and many more.

This machine learning tutorial gives you an introduction to machine learning along with the wide range of machine learning techniques such as Supervised, Unsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, hidden Markov models, and various sequential models.

When you tag a face in a Facebook photo, it is AI that is running behind the scenes and identifying faces in a picture. Face tagging is now omnipresent in several applications that display pictures with human faces. Why just human faces? There are several applications that detect objects such as cats, dogs, bottles, cars, etc. We have autonomous cars running on our roads that detect objects in real time to steer the car. When you travel, you use Google Directions to learn the real-time traffic situations and follow the best path suggested by Google at that point of time. This is yet another implementation of object detection technique in real time.

Let us consider the example of Google Translate application that we typically use while visiting foreign countries. Google’s online translator app on your mobile helps you communicate with the local people speaking a language that is foreign to you.

There are several applications of AI that we use practically today. In fact, each one of us use AI in many parts of our lives, even without our knowledge. Today’s AI can perform extremely complex jobs with a great accuracy and speed. Let us discuss an example of complex task to understand what capabilities are expected in an AI application that you would be developing today for your clients.

Example

We all use Google Directions during our trip anywhere in the city for a daily commute or even for inter-city travels. Google Directions application suggests the fastest path to our destination at that time instance. When we follow this path, we have observed that Google is almost 100% right in its suggestions and we save our valuable time on the trip.

You can imagine the complexity involved in developing this kind of application considering that there are multiple paths to your destination and the application has to judge the traffic situation in every possible path to give you a travel time estimate for each such path. Besides, consider the fact that Google Directions covers the entire globe. Undoubtedly, lots of AI and Machine Learning techniques are in-use under the hoods of such applications.

Considering the continuous demand for the development of such applications, you will now appreciate why there is a sudden demand for IT professionals with AI skills.

Who this course is for:

• Python developers curious about Data Science
• Machine learners
• Computer Science Engineers

Course content

How to implement regression, classification and boosting algorithms

Which algorithms work best for a given dataset

Data preprocessing

Requirements

• Basic python knowledge
• Google Colab account

Description

Machine learning is a rapidly growing field. However, a lot of courses on the internet today do not go over some of it’s most powerful algorithms. In this course, we will learn multiple machine learning algorithms, along with data preprocessing, all in under an hour. We will go over regression, classification, component analysis and boosting all in scikit-learn, one of the most popular machine learning libraries for python.

Algorithms we’ll go over (in order):

• Linear Regression
• Polynomial Regression
• Multiple Linear Regression
• Logistic Regression
• Support Vector Machines
• Decision Trees
• Random Forest
• Principle Component Analysis
• Gradient Boosting
• XGBoost

Who this course is for:

• People looking to get into AI but don’t know where to start
• People who want to build accurate models as quickly as possible

Course content

How to implement an Artificial Neural Network in Python

How to do Regression

How to use Google Colab

Requirements

• Deep Learning Basics

Description

Are you ready to flex your Deep Learning skills by learning how to build and implement an Artificial Neural Network using Python from scratch?

Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.

In this free course, AI expert Hadelin de Ponteves guides you through a case study that shows you how to build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant.

The objective is to create a data model that predicts the net hourly electrical energy output (EP) of the plant using available hourly average ambient variables.

Go hands-on with Hadelin in solving this complex, real-world Deep Learning challenge that covers everything from data preprocessing to building and training an ANN, while utilizing the Machine Learning library, Tensorflow 2.0, and Google Colab, the free, browser-based notebook environment that runs completely in the cloud. It’s a game-changing interface that will supercharge your Machine Learning toolkit.

Check out what’s in store for you when you enroll:

Part 1: Data Preprocessing

• Importing the dataset
• Splitting the dataset into the training set and test set

Part 2: Building an ANN

• Initializing the ANN
• Adding the input layer and the first hidden layer
• Adding the output layer
• Compiling the ANN

Part 3: Training the ANN

• Training the ANN model on the training set
• Predicting the results of the test set

More about Combined-Cycle Power Plants

A combined-cycle power plant is an electrical power plant in which a Gas Turbine (GT) and a Steam Turbine (ST) are used in combination to produce more electrical energy from the same fuel than that would be possible from a single cycle power plant.

The gas turbine compresses air and mixes it with a fuel heated to a very high temperature. The hot air-fuel mixture moves through the blades, making them spin. The fast-spinning gas turbine drives a generator to generate electricity. The exhaust (waste) heat escaped through the exhaust stack of the gas turbine is utilized by a Heat Recovery Steam Generator (HSRG) system to produce steam that spins a steam turbine. This steam turbine drives a generator to produce additional electricity. CCCP is assumed to produce 50% more energy than a single power plant.

Who this course is for:

• Anyone interested in Machine Learning and Deep Learning

Course content

Simple implementation of convolutional neural networks, deep neural networks, recurrent neural networks, and linear regression

Understanding of keras syntax

Understanding of different deep learning algorithms

Requirements

• Basic python knowledge
• Familiarity with data science and numpy

Description

When I started learning deep learning, I had a hard time figuring out how everything worked. What library was the best for me? Which algorithms worked best for which data set? How could I know my model was accurate? I spent a lot of time on tutorials, courses and reading to try and answer these questions. In the end, I felt like the process I took to learn deep learning was too inefficient. That is why I created this course.

Learn Keras: Build 4 Deep Learning Applications is a course that I designed to solve the problems my past self had. This course is designed to get you up and running with deep learning as quickly as possible. We use keras in this course because it is one of the easiest libraries to learn for deep learning. Each video, we go over a different machine learning algorithm and its use cases. The four algorithms we focus on the most are:

1. Linear Regression

2. Dense Neural Networks

3. Convolutional Neural Networks

4. Recurrent Neural Networks

In conclusion, if you are looking at a quick intro into deep learning, this course is for you.

So what are you waiting for? Let’s get started!

Who this course is for:

• Someone who wants to get into machine learning but feels overwhelmed by other tutorials
• Someone who is interested in machine learning but doesn’t know where to start

Course content