What kind of learning algorithm for "Future stock prices or currency exchange rates"?

What is Machine Learning? (Choose 3 Answers)

What kind of learning algorithm for "Facial identities or facial expressions"?

Which of the following is not type of learning?

This picture shows an application of ...

Field of study that gives computers the ability to learn without being explicitly programmed.

A major benefit of an machine with AI is

What is Machine learning?

__________________ algorithms enable the computers to learn from data, and even improve themselves, without being explicitly programmed.

_______________________ is a category of an algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.

What are the two types of Unsupervised Learning?

What are the two types of Supervised Learning?

In this type of Machine Learning, an AI system is presented with unlabeled, uncategorized data and the system’s algorithms act on the data without prior training. The output is dependent upon the coded algorithms.

What is Classification ?

The type of Classification are : (Choose 3 Answers)

Confusion matrix is a matrix where the predicted vs the actual value are placed to see how well the model performed

What are the evaluation metrics for Classification? (Choose 3 Answers)

To create a model we set the compute target :

What do you conclude after seeing this visualization? (Choose 3 Answers)

Fraud Detection, Image Classification, Diagnostic, and Customer Retention are applications in

Describe the correlation in the graph shown.

In regression analysis, the variable that is being predicted is;

Now imagine that you have some images that contain handwritten numbers. You want to create a program that will recognize which number is in each picture, but you're not sure exactly what characteristics can be used to best tell the numbers apart.

Which is the best approach for creating this program?

In traditional programming, the inputs of hard-coded rules and data are used to arrive at the output of answers, but in machine learning the approach is quite different.

Mark all of the options below that are true statements about machine learning. (Choose 3 Answers)

___________ is a data science technique used to extract patterns from data, allowing computers to identify related data, and forecast future outcomes, behaviors, and trends.

________ A graphical interface enabling no-code development of machine learning solutions.

Data scientists, software engineers, and IT operations professionals can define pipelines to orchestrate model training, deployment, and management tasks.

You can use this service to analyze images and video, and extract descriptions, tags, objects, and text.

A subcategory of machine learning that uses a layered neural-network architecture originally inspired by the human brain.

Without being an expert ornithologist, it’s possible to look at a collection of bird photos and separate them roughly by species, relying on cues like feather color, size or beak shape. That’s how the most common application for unsupervised learning.

In machine learning, often you have to tune parameters for the chosen learning algorithm to improve the performance on relevant metrics, such as prediction accuracy. At what stage of the data science lifecycle do you optimize the parameters?

A property that the items or entities in the table can have.

Scaling Data data means transforming it so that the values fit within some range or scale and it is a good idea to scale your data before feeding it into a machine learning algorithm. Two common approaches to scaling data include standardization and normalization.

Keny is looking to train his first machine learning model. One of his inputs includes the size of the T-Shirts, with possible values of XS, S, M, L, and XL. What is the best approach Keny can employ to preprocess the T-Shirt size input feature?

what is the normalization and formula ?

we can fully encode an image numerically by using a vector with three dimensions. The size of the vector required for any given image would be the _____________ of that image.

In encoding an Image in ML we need to know three things about an image to reproduce it:

In text data (Vectorization) the approach of TF-IDF is to give less importance to words that contain less information and are common in documents, such as "the" and "this"—and to give higher importance to words that contain relevant information and appear less frequently. Thus TF-IDF assigns weights to words that signify their relevance in the documents.

What is the typical pipeline for a classification model using text data?

We are trying to find a mathematical function that, given the values of the independent variables can predict the values of the dependent variables.

Deep Learning start to flourish around

2. In machine learning, we often tune parameters for the chosen learning algorithm to improve the performance on relevant metrics, such as prediction accuracy. At what stage of the data science life cycle do we optimize the parameters?

From a statistical perspective

Output Variable = f(Input Variables)

OR Dependent Variable = f(Independent Variables)

what are best Python libraries for machine learning , select that apply

Is one of the most popular ML libraries for classical ML algorithms. It is built on top of two basic Python libraries, viz., NumPy and SciPy and supports most of the supervised and unsupervised learning algorithms and can also be used for data-mining and data-analysis, which makes it a great tool who is starting out with ML

Data analytics platform, optimized for use with Microsoft cloud services

what is Microsoft's core development environment

There are main libraries used for deep learning. Select all that apply

__________ is an interactive data visualization library. In contrast to a library like matplotlib that generates a static image as its output, Bokeh generates visualizations in HTML and JavaScript. This allows for web-based visualizations that can have interactive features.

_______ is a Python library designed specifically for data visualization. It is based on matplotlib, but provides a more high-level interface and has additional features for making visualizations more attractive and informative.

_________ is a very popular high-level programming language that is great for data science. Its ease of use and wide support within popular machine learning platforms, coupled with a large catalog of ML libraries, has made it a leader in this space.

It is designed for cluster computing and is often used to process large-scale and big data.

________ Organize machine learning workloads and keep track of each task executed through the service.

___________ Expose real-time endpoints for scoring as well as pipelines for advanced automation.

Cloud machine learning services also need to provide support to manage the resources required to run specific machine learning tasks. Select all that apply

Datastores is data sources connected to the service environment (e.g. blob stores, file shares, Data Lake stores, databases).

A drag-and-drop tool that lets you create ML models without a single line of code.

A centralized place to work with all the artifacts you create.

A place to save all the models created in Azure ML or trained outside of Azure ML

A designated resource/environment where you run your training script or host your service deployment

______________ are outputs or specific representations of algorithms that run on data.

A model represents what is learned by a machine learning algorithm on the data.

As the term suggests, linear regression is an algorithm that uses a straight line (or plane) to describe relationships between variables.

To "train a linear regression model" simply means to learn the coefficients and bias that best fit the data. This is the purpose of the linear regression algorithm

when preparing the data for linear regression it's important to perform a correlation check among input variables and remove highly correlated input variables.

Gaussian (normal) distribution: Linear regression assumes that the distance between output variables and real data (called residual) is normally distributed

Linear regression is very sensitive to the distance among data points, so it's always a good idea to normalize or standardize the data.

Machine learning algorithms aim to learn a target function (f) that describes the mapping between data input variables (X) and an output variable (Y).

Which of the following algorithms are parametric?

__________ Learns from examples using a similarity function that measures how similar two objects are.

All deep learning algorithms are machine learning algorithms but not all machine learning algorithms are deep learning algorithms

Mark which general approach to machine learning it best describes. learns from data that contains both the inputs and expected outputs