Ementorhub

What you'll learn

Understand the basics of training a DNN using the Gradient Descent algorithm.

Apply knowledge to implement a complete DNN using NumPy.

Analyze and create a complete structure for DNN from scratch using Python.

Evaluate and work on a project using deep learning for the IRIS dataset.

Skills you'll gain

Deep Learning

Network Architecture

Performance Tuning

Data Processing

NumPy

Supervised Learning

Machine Learning Algorithms

Data Cleansing

Python Programming

Feature Engineering

Regression Analysis

Artificial Neural Networks

There are 5 modules in this course

This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Are you ready to become a deep learning expert? This step-by-step course guides you from basic to advanced levels in deep learning using Python, the hottest language for machine learning. Each tutorial builds on previous knowledge and assigns tasks solved in the next video. You will: - Learn to train machines to predict like humans by mastering data preprocessing, general machine learning concepts, and deep neural networks (DNNs). - Cover the architecture of neural networks, the Gradient Descent algorithm, and implementing DNNs using NumPy and Python. - Understand DNN methodologies with real-world datasets, such as the IRIS dataset. Designed for those interested in data science or advancing their skills in DNNs, this course requires a background in deep learning and a basic understanding of Python and mathematics will be helpful. It’s clear and beginner-friendly, teaching theoretical concepts followed by practical implementation.

Basics of Deep Learning

Deep Learning

Optimizations

Final Project