Artificial neural network (ANN) is a powerful machine learning tool that can be used to solve a wide range of problems, from image recognition to natural language processing. In this project-based course, you’ll learn the fundamentals of deep learning and ANNs by building and training your own ANN model.
The course is structured to provide a step-by-step guide on building your very own ANN, starting with a comprehensive introduction that lays a solid foundation. As you navigate through each section, you’ll delve deeper into the intricacies of ANNs, from installation and data preprocessing to the nuts and bolts of data encoding. But the adventure doesn’t end there! You’ll also tackle the critical steps in building an ANN, ensuring you have a strong grasp of the processes and methodologies essential for success in your projects.
But that’s not all! The final sections of the course will take you on a deep dive into the realms of predictions and imbalance-learn, equipping you with the tools and knowledge to confidently handle imbalanced datasets and extract accurate insights. By the end of your journey, you’ll emerge with a certificate and a profound understanding of ANNs, ready to take on the world of deep learning projects with newfound confidence and expertise. So, are you ready to embark on this exciting learning adventure? The world of artificial neural networks awaits!
Understand the fundamentals of artificial neural networks.
Install and configure necessary tools for ANN development.
Master the art of data preprocessing and encoding for diverse datasets.
Learn the step-by-step process of building an artificial neural network.
Develop skills to manage and process imbalanced datasets.
Gain proficiency in making accurate predictions using ANN.
Acquire valuable insights into imbalance-learn and its applications.
Master the vital skills required for ANN projects.
Gain a competitive edge in the field of deep learning.
Enhance your ability to process and analyze complex datasets.
Acquire the knowledge to make accurate predictions using ANNs.
Open up a plethora of opportunities in the artificial intelligence domain.
Deep Learning Specialist
Machine Learning Engineer
AI Research Scientist
Neural Network Analyst
Basic knowledge of deep learning concepts.
Familiarity with programming languages used in ANN development.
An eagerness to delve into the intricacies of artificial neural networks.
Section 01: Introduction
Introduction of Project
Section 02: ANN Installation
Setup Environment for ANN
Section 03: Data Preprocessing
Import Libraries and Data Preprocessing
Data Preprocessing Continue
Section 04: Data Encoding
Preparation of Dataset for Training
Section 05: Steps to Build ANN
Steps to Build ANN Part 1
Steps to Build ANN Part 2
Steps to Build ANN Part 3
Steps to Build ANN Part 4
Section 06: Predictions and Imbalance-Learn
Resampling Data with Imbalance-Learn
Resampling Data with Imbalance-Learn Continue
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