Low code and no code platforms are valuable IT tools. They make it easy for employees outside of the IT team to develop apps that simplify business processes. And in some cases, they can aid in the transition from amateur developer or...

In this video, we will use python to write a simple ensemble learning model for classification. • We will use Jupyter Notebook to execute our code • Use Iris dataset to perform classification • Use hard voting and soft voting for...

From the section: Build a Threat Model and Learn Different Attacks on AI. In this section, you will create a high-level threat model for AI solution to choose the right priorities against various threats.



It’s important to...

From the section: Choosing the Right Defense. In this section, you will deploy the right defense methods to protect AI systems from attacks by comparing their efficiency.



The aim of this video is to provide guidance on...

From the section: Choosing the Right Defense. In this section, you will deploy the right defense methods to protect AI systems from attacks by comparing their efficiency.



The aim of this video is to show a practical...

From the section: NLP Visualization and Model Experimentation. Visualize text data and view data embeddings. View and track hyperparameter tuning and display training configurations to run reproducible experiments.



Let’s...

From the section: Common Coding Concepts.This section will cover common coding concepts such as Scratch setup, bugs, pseudocode, decomposition, commenting and many more.<b<br/>r/>

Common Coding Concepts: Control Flow

From the section: Introduction to Python. In this section we look at Python's fundamental, built-in data structures, and discuss how and when to use them.



This video will introduce you to single-quote and multiple-quot
e...

From the section: Computer Vision Basics: Part 3. In this section, we’ll cover the advanced features of Computer Vision.<b<br/>r/>

Computer Vision Basics: Part 3: Histogram of Oriented Gradients (HOG)

From the section: Tree-Based Data Structures. In this section, we will go over a few robust tree-based data structures, that are indispensable for efficiently solving various query-related problems.



In this video, we...

This introduction includes information about naming variables, built-in constants, reserved keywords, built-in functions, syntax highlighting, data types (integer, float, list, tuple, dictionary), and basic commands to built a first...

A description of a code library in Arduino programming.

From the section: NLP Visualization and Model Experimentation. Visualize text data and view data embeddings. View and track hyperparameter tuning and display training configurations to run reproducible experiments.



Here,...

From the section: Searching in Graphs. In this section, we will see what we are searching for. Also, we will look at how to use and implement Breadth-First Search and Depth-First Search along with a simple puzzle game...

From the section: Networking and Programming with IP and UDP In this section, we will learn about all about networking and using programming with IP and UDP.



In this video, will learn about the basics of Network programming...

From the section: Introduction to Artificial Neural Networks (ANN). This section introduces Artificial Neural Networks. You will learn about Neural Network for Binary Classifications, Neural Network with PCA for Binary...

From the section: Predicting Sales with Supervised Learning. In this section, learners will use their first machine learning techniques, including Support Vector Machines and Artificial Neural Networks. These techniques will be...

The TensorFlow programming model signifies how to structure your predictive models. • Look at the steps for structuring the model

From the section: Lights, Camera, Action – Building Blocks of Reinforcement Learning. In this section, we learn about the main building blocks of reinforcement learning, including the action space, the environment, and the rewards....

From the section: Model-Free Prediction and Control With Monte Carlo (MC). In this section, we will learn one of two improvements on DP – Monte Carlo, a method which does not require a model of the environment. We talk about a new...

From the section: The Multi-Armed Bandit. In this section, we will learn about the basics and look at one of the most foundational concepts in Reinforcement Learning – The Multi-Armed Bandit We construct a model of a MAB...

From the section: Errors in Python. This section introduces you to errors in Python.<b<br/>r/>

This video provides an introduction to errors in Python.

From the section: Unit Testing with Python. In this section, you will learn about unit testing using Python.<b<br/>r/>

This video explains the various testing functions in Python.

From the section: Power Supplies. Here, we check our power supply, mount the power supply and learn how to cool our PC. All PCs make heat. Too much heat will damage components, so the heat must be dissipated from the system. Cooling...