If you're interested in machine learning and data science, you've probably heard of deep learning. Perhaps you're even familiar with neural networks, a subset of deep learning. But what exactly do these terms mean, and how can you utilize them in Python?
Course Overview
is an in-depth course that will teach you everything you need to know about building artificial neural networks using deep learning techniques. This course assumes you already have some background knowledge in the field of machine learning, but even if you don't, you can still benefit from this course if you're willing to put in the effort.Data Science: Deep Learning and Neural Networks in Python is an in-depth course that will teach you everything you need to know about building artificial neural networks using deep learning techniques. This course assumes you already have some background knowledge in the field of machine learning, but even if you don't, you can still benefit from this course if you're willing to put in the effort.
The course has a 4.61891 aggregate rating and 8971 reviews as of writing, making it one of the top courses in the field. The course is designed to be practical and hands-on, with numerous examples showing how deep learning can be applied to real-life problems. If you're looking to gain new skills or advance your existing knowledge, this course is for you.
What You'll Learn
This course is divided into several sections, each building on the one before it. The course overview discusses the building of your first artificial neural network through the use of deep learning techniques. Following the introductory course, you will learn:
1. Multiple Classes using the Softmax Function
The previously discussed binary classification model will be extended to handle multiple classes using the softmax function.
2. Backpropagation using First Principles
The course then goes into detail on the backpropagation training method using first principles. In the course, you will learn to code backpropagation in Numpy, initially using a slower method, then using a faster process with Numpy features.
3. Implementing a Neural Network with Tensorflow
As the course progresses, you'll learn to implement a neural network using Google's newest TensorFlow library, which is becoming increasingly popular. Tensorflow is a powerful framework that includes many pre-built tools for machine learning. By the end of this section of the course, you'll have a working neural network.
4. Deep Learning Concepts in Theano and TensorFlow
If you already know about softmax and backpropagation, you may want to skip over the theory and speed things up using more advanced techniques with GPU-optimization. In the follow-up course, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow, you will gain a deeper understanding of these libraries.
What You Need to Know Before You Begin
The course recommends you have a background in calculus, matrix arithmetic, probability, Python coding (including if/else statements, loops, lists, dictionaries, and sets), as well as Numpy coding. It's also recommended that you be familiar with basic linear models such as linear regression and logistic regression. This should be enough to follow and benefit from the course material.
Why This Course is So Important
It's one thing to learn about a subject, but it's another thing entirely to be able to apply that knowledge in the real world. This course takes a hands-on approach to teaching machine learning. You'll get to experiment with the material, and understand how to build and understand neural networks, not just how to use them. You'll learn to visualize what is happening inside the model itself and truly understand the learning process.
With deep learning becoming increasingly popular in the job market, this is a must-have course for anyone who wants to become a machine learning or data science expert. By the end of the course, you'll have a practical understanding of how to apply artificial neural networks to various real-world problems.
Projects Covered in the Course
Throughout the course, you'll work on several practical projects:
1. Prediction of User Actions on a Website
In this project, you'll be predicting user actions on a website using both deep learning techniques and logistic regression. You'll use user data such as whether or not the user is on a mobile device, how many products they viewed, how long they spent on the site, whether or not they are a returning visitor, and the time of day they visited. By the end of the project, you will have created a model capable of predicting a user's actions on a website accurately.
2. Facial Expression Recognition
This project is unique in that you will use deep learning to predict a person's emotions given just a photograph. This project shows the endless possibilities of the technology and demonstrates how deep learning can be used in a variety of applications.
Advanced Topics and Other Courses
Once you've completed this course, there are additional courses to help you further expand your knowledge. Some advanced topics covered in other courses include:
- Convolutional Neural Networks
- Restricted Boltzmann Machines
- Autoencoders
It's important to note that you should feel comfortable with the material in this course before moving on to more advanced subjects. This course focuses on "how to build and understand", not just on "how to use" deep learning models.
While there are many courses available that will teach you how to use machine learning libraries, they often fall short on the fundamentals. This course is unique because it is one of the few that will teach you how to implement machine learning algorithms from scratch. Richard Feynman, a renowned physicist, once said, "What I cannot create, I do not understand." This course takes Feynman's quote to heart, encouraging you to explore the theory and "see for yourself" through experimentation.
Wrapping Up
Data Science: Deep Learning and Neural Networks in Python is an essential course for anyone wanting to expand their machine learning skills. The course provides a practical understanding of neural networks and how they work through hands-on experience, enabling you to develop solutions to real-world problems. If you're looking to master the fundamentals of deep learning and neural networks, this course is for you.
