Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Learn how to use Azure Machine Learning to create and publish models without writing code.
This Post will cover some quick tips including FAQs on the topics that we covered in the Day 2 live session which will help you to clear Certification [DP-100] & get a better-paid job.
The previous week, in Day 1 session we got an overview of Azure Machine Learning and AutoML. And in this week’s Day 2 Live Session of the AI/ML & Azure Data Scientist Certification [DP-100] training program, we covered the concepts of No-Code Machine Learning with Designer and Run Experiments. We also performed labs.
DP-100 FAQ’s: No Code Machine Learning With Designer
This is how Module 2 looks like on the learning portal
In which we covered the AutoML part in Day 1 session and the Rest is in Day 2 Session.
So, here are some of the DP-100 Questions Answers asked during the Live session from Module 2: No Code Machine Learning with Designer.
Machine Learning
Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. By using machine learning, computers learn without being explicitly programmed.
Q1: Examples of clustering and Reinforcement? A: Clustering is a machine learning technique used to group the unlabeled dataset. It is often referred to as a method of grouping the information points into separate clusters, consisting of comparable information points. The objects with similarities stay in one cluster that has less or no similarities with another cluster.
Example for Clustering: Let’s perceive the cluster technique with the real-world example of Mall: after we visit any mall, we are able to observe that the items with similar usage are sorted along like the t-shirts area unit sorted in one section, and trousers area unit at alternative sections, similarly, at vegetable sections, apples, bananas, Mangoes, etc., area unit sorted in separate sections, so we are able to simply verify the items. The cluster technique additionally works within the same means.
Reinforcement Learning could be a feedback-based Machine learning technique within which associate degree agent learns to behave in associate degree atmosphere by playacting the actions and seeing the results of actions. for every smart action, the agent gets feedback, and for every unhealthy action, the agent gets feedback or penalty.
Example for Reinforcement: Suppose there’s an associate degree AI agent gift among a maze environment, and his goal is to search out the diamond. The agent interacts with the environment by playacting some actions, and supported those actions, the state of the agent gets modified, and it conjointly receives a present or penalty as feedback.
To know more about Reinforcement Learning read our blog.
Q2: Does azure ML handle reinforcement learning? A: Yes, Azure ML can handle reinforcement learning. Read the Microsoft Official Microsoft Document.
Q3: How can we deal with multi-class classification problems? A: Basically, there are three methods to solve a multi-label classification problem, namely:
Problem Transformation
Adapted Algorithm
Ensemble approaches
Q4:Different types of prediction algorithms? A: Here are the Top 10 Machine Learning prediction Algorithms
Linear Regression
Logistic Regression
Linear Discriminant Analysis
Classification and Regression Trees
Naive Bayes
K-Nearest Neighbors (KNN)
Learning Vector Quantization (LVQ)
Support Vector Machines (SVM)
Random Forest
Boosting
No code Machine Learning with Designer
After taking a recap of what we covered in Day 1 we started with the second part of module 2 which is working with the designer
Azure Machine Learning designer provides a haul & drop setting during which you’ll outline a progress, or pipeline of information bodily function, transformation, and model coaching modules to make a machine learning model. then publish this pipeline as an online service that shopper applications can use for inferencing (generating predictions from new data)
An ML pipeline may be a means that of automating the machine learning progress by facultative knowledge to be reworked and correlative into a model which will then be analyzed to attain outputs. this sort of ml pipeline makes the method of inputting knowledge into the ml model absolutely automatic
Q5: What are the pipeline Parameters? A: Pipeline parameters are typed pipeline variables that are declared in the parameters key at the top level of a configuration. Users can pass parameters into their pipelines when triggering a new run of a pipeline through the API.
Q6: Designer Preview Pipeline has been running for 21 minutes – is that normal? A: The pipeline might take 10-12 minutes to run, but if running for a long time please check the steps once again and if still not resolved then create a new training pipeline.
Q7: How long will it run for after submitting Pipeline Run? A: It takes around 10-12 minutes for pipeline execution to finish after submitting the Pipeline run.
Q8: What do we do in the normalization step? A: Normalization could be a technique usually applied as a part of data preparation for machine learning. The goal of Normalization is to alter the values of numeric columns within the dataset to use a typical scale, while not distorting variations within the ranges of values or losing info. Normalization is additionally needed for a few algorithms to model the information properly.
Q9: What happens if there are both numeric and categorical features? Do we have to split the data? A: The categorical data has to be converted to numeric or some common format by encoding (one-hot encoding) and then the data can be split for training and testing.
After setting up our machine learning environment, we moved on to the next module which introduced us to Running Experiments which consists of Introduction to Experiments, Training, and Registering Models
Running Experiments
A run is a single execution of a training script. An experiment will typically contain multiple runs. Azure Machine Learning records all runs and stores the following information in the experiment:
• Metadata about the run (timestamp, duration, and so on)
• Metrics that are logged by your script
• Output files that are auto collected by the experiment or explicitly uploaded by you
• A snapshot of the directory that contains your scripts, prior to the run
Q10: What are trained models? Do you mean updated? A: Trained model is the one that has been trained with some specific data parameters with different algorithms and predicted output. This trained model is further used to test the accuracy of the model by testing on the test data.
Feedback Received…
From our DP-100 day 2 session, we received some good feedback from our trainees who had attended the session, so here is a sneak-peek of it.
With my AI/ML & Azure Data Science training program, we cover 150+ sample exam questions to help you prepare for the certification DP-100.
Check out one of the questions and see if you can crack this…
Ques: You need to ingest data from a CSV file into a pipeline in a Designer. What should you do?
A: Create a Dataset by uploading the file, and drag the dataset to the canvas.
B: Add a Convert to CSV module to the canvas.
C: Add an Enter Data Manually module to the canvas
Comment with your answer & we will tell you if you are correct or not!!
To know more about the course, AI, ML, Data Science for beginners, why you should learn, Job opportunities, and what to study Including Hands-On labs you must perform to clear[DP-100] Microsoft Azure Data Scientist Associate Certificationregister for our FREE CLASS.
