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Tag: AI and ML online training

An introduction to neural networks: AI/ML for beginners

Posted on by Imarticus Learning

The field of AI and machine learning is overgrowing, with new advancements in algorithms happening nearly every day. One area with a lot of growth recently is neural networks, which are artificially intelligent systems built on an architecture inspired by the human brain. In this post, we will explore what precisely neural networks are and how they work so you can get started today!

What is a neural network?

Neural networks are machine learning algorithms that you can use to recognize objects in pictures or understand human speech. 

For example, imagine you wish to teach a convolutional neural network how to recognize pictures of cats. You might show the computer thousands of examples of what cats look like and let it learn from that data. Then, when somebody shows the computer a picture that isn’t a cat, it could determine whether or not this is an image of something else using its knowledge of cats.

A step-by-step tutorial on how to train the convolutional neural network and make predictions:

 

  • Choose your dataset:

 

The first step is choosing a dataset to train your neural network. It could be a data set of images, text, or anything else you want to predict.

 

  • Preprocess the data:

 

Before starting training your neural network, you need to preprocess the data. It includes cleaning and formatting the data to be ready to be used by the deep neural network.

 

  • Choose your model:

 

The next step is to choose a model for your neural network. There are many different models, so you need to choose one that will work best for your dataset.

 

  • Train the model:

 

Now it’s time to train the network. It is where you will feed in your data and let the neural network learn from it.

The future of AI/ML:

AI/ML is becoming more widely used today. AI/ML has many benefits for the world around us. Machine learning help diagnose diseases, drive cars and even write music!

  • Websites like Amazon use AI/ML to recommend products you may like based on what you have bought in the past.
  • Facebook uses AI/ML to determine which posts or status to show first in your newsfeed.
  • Google uses AI/ML to generate search results.

The possibilities are endless, and the future of AI/ML is inspiring!

Discover Artificial intelligence and machine learning course with Imarticus Learning

This Artificial intelligence and machine learning course is by industry specialists to assist students in learning real-world applications from the ground up and building sophisticated models to offer helpful business insights and forecasts. This AIML course is for recent graduates and early-career professionals (0-5 years) who want to further their careers in Data Science and Analytics, the most in-demand job skill.

Course Benefit For Learner: 

  • Students get a solid understanding of the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies. 
  • Learn data science skills by participating in 25 in-class real-world projects and case studies from business partners. 
  • Impress employers & showcase skills with artificial intelligence courses recognized by India’s prestigious academic collaborations.

Regression and classification metrics with python in AI/ML

Posted on by Imarticus Learning

Python is one of the most popular languages used in data science. It has a massive library that makes it easy for anyone to conduct machine learning and deep learning experiments. In this blog, we will be discussing regression and classification metrics with python Programming in AI/ML.  

We will show how to use some of these metrics to measure the performance of your models, which can help you make decisions about what algorithm or architecture might work best for your application or dataset!

What is a regression metric?

A regression metric measures how accurately a machine learning model predicts future values. To calculate a regression metric, you first need to collect predicted and actual values data. Then, you can use various measures to evaluate how well the model performs. 

How to use classification metrics with python Programming in AI/ML?

A classification metric or accuracy score measures how accurately a machine learning model predicts the correct class label for each data point in your training dataset. Once you have a classification metric, you can evaluate your machine learning model’s performance. 

You can use many different classification metrics to measure performance for a classifier machine learning model. Common ones include accuracy score, precision, recall, actual positive rate, and recall at different false-positive rates. You can also calculate the Matthews correlation coefficient (MCC) to measure how well your model performs.

Accuracy Score:

Accuracy score measures how often the predicted value equals the actual value. It’s also known as error rate, accuracy, or simply classification accuracy. You can calculate the accuracy score by dividing the total number of correct predictions from all predictions made.

Precision:

Precision is the number of correct predictions divided by the number of predictions made. 

Recall:

Recall, or valid positive rate is the number of correct predictions divided by the number of positives. You can calculate how well your model performs for different classes by plotting a ROC curve and calculating the AUC.

False Positive:

False-positive is also known as Type I Error or alpha error in statistical hypothesis testing. It’s when your model predicts that an instance belongs to one class, but it belongs to another.

False Negative:

False-negative is also known as Type II Error or beta error in statistical hypothesis testing. It’s when your model predicts that an instance belongs to one class but belongs to another, and the actual value isn’t present in training data. 

Matthews Correlation Coefficient (MCC):

The Matthews correlation coefficient measures how well your model predicts the labels of unseen instances from training data. 

Area Under Curve (AUC):

The AUC score measures how well your model predicts future values by plotting a ROC curve and calculating the area under it.

Discover AIML course with Imarticus Learning

This artificial intelligence course is by industry specialists to help students understand real-world applications from the ground up and construct strong models to deliver relevant business insights and forecasts. 

Course Benefit For Learner: 

  • Students get a solid understanding of the fundamentals of data analytics and machine learning and the most in-demand data science tools and methodologies.
  • Learn data science skills by participating in 25 in-class real-world projects and case studies from business partners.
  • Impress employers & showcase skills with artificial intelligence courses recognized by India’s prestigious academic collaborations.

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