List the steps involved in Machine Learning.

Data collection
Data preparation
Choosing an appropriate model
Training the dataset
Evaluation
Parameter tuning
Predictions

What steps would you take to evaluate the effectiveness of your ML model?

You have to first split the data set into training and test sets. You also have the option of using a cross-validation technique to further segment the data set into a composite of training and test sets within the data.

Then you have to implement a choice selection of the performance metrics like the following:

Confusion matrix
Accuracy
Precision
Recall or sensitivity
Specificity
F1 score
For the most part, you can use measures such as accuracy, confusion matrix, or F1 score. However, it’ll be critical for you to demonstrate that you understand the nuances of how each model can be measured by choosing the right performance measure to match the problem.

What is an iterative deepening depth-first search algorithm?

The repetitive search processes of level 1 and level 2 happen in this search. The search processes continue until the solution is found. Nodes are generated until a single goal node is created. Stack of nodes is saved.

What is a uniform cost search algorithm?

The uniform cost search performs sorting in increasing the cost of the path to a node. It expands the least cost node. It is identical to BFS if each iteration has the same cost. It investigates ways in the expanding order of cost.

What is a cost function?

A cost function is a scalar function that quantifies the error factor of the neural network. Lower the cost function better the neural network. For example, while classifying the image in the MNIST dataset, the input image is digit 2, but the neural network wrongly predicts it to be 3.

Which search method takes less memory?

The “depth first search” method takes less memory.

What are AI neural networks?

in AI mathematically model how the human brain works. This approach enables the machine to think and learn as humans do. This is how smart technology today recognizes speech, objects, and more.

How does face verification work?

Face verification is used by a lot of popular firms these days. Facebook is famous for the usage of DeepFace for its face verification needs.

There are four main things you must consider when understanding how face verification works:

Input: Scanning an image or a group of images
Process:
Detection of facial features
Feature comparison and alignment
Key pattern representation
Final image classification
Output: Face representation, which is a result of a multilayer neural network
Training data: Involves the usage of thousands of millions of images

What is a top-down parser?

A top-down parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual pre-terminal symbols are written.

Which is better for image classification? Supervised or unsupervised classification? Justify.

In supervised classification, the images are manually fed and interpreted by the Machine Learning expert to create feature classes.
In unsupervised classification, the Machine Learning software creates feature classes based on image pixel values.