Data Science Deep Learning in Python
Data Science Deep Learning in Python
What Does The Course Offer?
This course will offer you hands-on knowledge of the fundamentals of data science. You will learn to build your first artificial neural network with the help of deep learning techniques.
We will take logistic regression as a building block and build non-linear neural networks straight out of the gate with Numpy and Python. The best part is that all the materials for this course are free of cost.
Our multiple classes are an extension of the previous binary classification model using the softmax function. With the help of the first principles, we derive the essential training method “backpropagation.” Your course will begin with learning how to code backpropagation in Numpy “the slow way”, and then gradually, we will move to “the fast way” with Numpy features.
After that, we implement a neural network with Google’s new TensorFlow library. If you want to become a pro at machine learning and data science, you should definitely consider enrolling in this course.
Our curriculum is not just limited to basic models like logistic regression and linear regression. Instead, we go above and beyond to facilitate you and enlighten you with the deep knowledge of data sciences.
This course promises you tons of practical examples so you can implement what you learn. During the course, we will conduct course projects to teach you real-life predictions. One of the projects will teach you how to predict user actions on a website when you are provided with the user’s data. You will learn to predict:
- If the user had a mobile device
- The number of products the user saw
- How long did they stay on the site
- What time of the day did the user visit
- Are they a returning visitor or not
The course will include another project as you reach the end. It will show you how to use your learning for facial expression recognition. If you think about it, after you have finished, you will be able to predict a person’s emotion based on just a picture! Isn’t it fascinating?
Once you are done and dusted with the basics, we will provide you with a brief overview of some new developments in neural networks. This will include somewhat modified structures and their applications.
Here, we would like to make a note that if you are already familiar with softmax and backpropagation and want to skip the theory to speed up your learning process, you can check out the follow-up courses where you can learn advanced techniques with GPU optimisation.
Additional Courses for Advanced Levels
We have some other courses to cover some advanced level topics, such as:
- Convolutional Neural Networks
- Restricted Boltzmann Machines
If you have developed a strong understanding of the previous course, you can move forward with advanced topics!
The Main Focus of this Course
The central focus of this course is not just about how you can use a program, but in fact, how to build and understand what’s going on. With this course, we promise that anyone can build an Application Programming Interface in a matter of minutes after reading some basics.
It’s not just about remembering facts but about carrying it out yourself through experimentation. You will learn how to visualise everything that’s happening internally in the model. This course is the right choice for you if your quest for knowledge is beyond a superficial look at machine learning models.
Prerequisites for the Course
Before you begin, you must have an understanding of:
- Linear algebra
- Numpy coding: loading a CSV file, matrix/vector operations
- Python coding: loops, if/else, sets, lists
How to Get Through the Course?
These tips will help you throughout the course:
- Take handwritten notes during the lecture to retain the information efficiently
- Ask plenty of questions on the discussion board
- Don’t get impatient if it takes you days or even weeks to finish the exercises
Useful Course Ordering
- Linear Regression in Python
- Logistic Regression in Python
- Supervised Machine Learning in Python
- Deep Learning in Python
- Practical Deep Learning in Theano and TensorFlow
- Convolutional Neural Networks in Python
- Easy NLP
- Cluster Analysis and Unsupervised Machine Learning
- Unsupervised Deep Learning
- Hidden Markov Models
- Recurrent Neural Networks in Python
- Natural Language Processing with Deep Learning in Python
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StartPrediction Section Introduction and Outline (5:38)
StartFrom Logistic Regression to Neural Networks (5:12)
StartSigmoid vs. Softmax (1:30)
StartWhere to get the code for this course (1:30)
StartFeedforward in Slow-Mo (part 1) (19:42)
StartFeedforward in Slow-Mo (part 2) (10:55)
StartSoftmax in Code (3:39)
StartBuilding an entire feedforward neural network in Python (6:23)
StartE-Commerce Course Project Pre-Processing the Data (5:24)
StartE-Commerce Course Project Making Predictions (3:55)
StartPrediction Quizzes (3:25)
StartPrediction Section Summary (1:45)
This course went above and beyond my expectations. This is a great business plan course. It was very easy to follow and very thorough. Alex is very real and makes you feel as if you are in the same room with him when he is speaking. Highly recommended all his courses & books.
- Amanda Smith
Tons and tons of advice. Make sure to take notes. Very comprehensive course with lots of great advice, a lot of things I never thought of trying. Definitely a don't miss for authors, musicians, or those with any other small business.
- Kristie Snively