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: Computer Vision Basics: Part 2. In this section, we’ll explore the some addtiional features of Computer Vision.<b<br/>r/>

Computer Vision Basics: Part 2: Image Transformations – Perspective transform

From the section: Logistic Regression. Learn about Logistic Regression here.<b<br/>r/>

Logistic Regression: Assessing the Accuracy of a Classification Model

From the section: Simple Decision trees. In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. In...

From the section: Algorithms - Search and abstract data structures. This section is about Algorithms. You will learn about Bisection, Binary section etc.



In-depth look at in-order traversal and how it results in a sorted...

From the section: Advanced Python. This section is about Advanced Python.<b<br/>r/>

Advanced Python: Functions

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



Common Coding Concepts: How
Code Runs
• ...

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: Environment Setup and Installation. This section will help you to install the required software and set up the development environment for this course.<b<br/>r/>

Environment Setup and Installation: Introduction

From the section: NumPy Fundamentals in Python. Every programming language is different. You will cover fundamentals in order to get familiar with the Python environment needed for NumPy. You will learn to work with numbers, lists,...

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: Getting Started With Reinforcement Learning Using OpenAI Gym. In this section, we will introduce the basics of the reinforcement learning problem, and explain how OpenAI gym helps with developing and research...

From the section: Deep Learning and Tensorflow: Part 1. In this section, we’ll talk about what Deep Learning is, and how TensorFlow works at a low level.



Deep Learning and Tensorflow: Part 1: Intro to Deep Learning and...

From the section: Simple Decision trees. In this section, we will start with the basic theory of decision tree then we cover data pre-processing topics like missing value imputation, variable transformation and Test-Train split. In...

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: 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: Common Data Types

From the section: You can code!. This section will help you discover some interesting facts about coding.



You can code!: You Can Co
de! Part 2
• Get the synopsis
about the Tuple
• Learn about t
he benefits of...

From the section: You can code!. This section will help you discover some interesting facts about coding.



You can code!: You Can Co
de! Part 1
• Create a simple list with names and anoth
er with numbers
• ...

From the section: Artificial Neural Networks. In this section, we’ll cover Bayes Theorem, Naive Bayes, SVM and SVC to classify data.



Artificial Neural Networks: Introduction: What are Artificial Neural Networks and how do...

From the section: Machine Learning: Part 2. In this section, we’ll cover Bayes Theorem, Naive Bayes, SVM and SVC to classify data.<b<br/>r/>

Machine Learning: Part 2: [Activity] Support Vector Classifiers in Action

From the section: Introduction to Computer Networks (ICND1). This Section introduces Computer Networks. This includes lectures on Physical Components, Topology, Speed etc. Introduction to Computer Networks (ICND1): Physical versus...

From the section: CNN-Industry Live Project: Find Medical Abnormalities and Save Life. This section includes a CNN-Industry live project on working with X-Ray images.



CNN-Industry Live Project: Find Medical Abnormalities and...

From the section: CNN-Industry Live Project: Playing With Real World Natural Images. This section includes a live project of working with flower images.



CNN-Industry Live Project: Playing with Real World Natural Images:...

From the section: Intro to PHP Programming for Web Development. In this section, we’ll learn the basics of PHP programming.<b<br/>r/>

Intro to PHP Programming for Web Development: String Manipulation Functions