Machine and deep learning are some of those quantitative analysis skills that differentiate the data scientist from the other members of the team. The field of machine learning is the driving force of artificial intelligence. This course will teach you how to leverage deep learning and neural networks from this powerful tool for the purposes of data science. The technology we employ is TensorFlow 2.0, which is the state-of-the-art deep learning framework.
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What is machine learning, deep learning and AI? How is it useful and is it really as important as people tend to believe?
Neural networks are more or less what we mean by 'deep learning' nowadays. In this section we explain the main rationale behind simple feed-forward neural networks.
Here, we will show you how to install the Jupyter Notebook (the environment we will use to code in Python) and how to import the relevant libraries. Because this course is based in Python, we will be working with several popular libraries: NumPy, SciPy, scikit-learn and TensorFlow 2.0.
To understand the inner workings of Neural Networks, we start with a simple example (called 'minimal example'). It is a naïve network, basically equivalent to a linear regression.
Having created the simple net, we 'translate' it to TensorFlow. This is our way of taking a simple, well-understood problem to introduce the syntax and logic of TensorFlow.
To have 'deep learning' we need 'deep' neural networks. In this section, we explain what exactly it means to be deep and focus on other important characteristics like width and activation functions. Finally, we explore the backpropagation algorithm.
Neural networks are extremely good at modeling the data at hand. That's why we can often 'learn the data TOO WELL.' This is called overfitting. Of course, there are numerous ways to prevent this from happening, which we explore in that section.
When the model is learning, it is searching for better and better solutions to the problem at hand. However, it starts from some initial values for its parameters. It matters what our starting point is, and that's what initialization is all about.
There is a trade-off between having a fast model and an accurate model. In this section, we explore different optimization algorithms, based on the gradient descent logic, as well as learning rate schedules and batching.
Preprocessing is a crucial step relevant to any modeling problem. While there are dozens of different preprocessing techniques, there are several that are commonly employed for almost all neural networks.
Once we have learned all the relevant theory, we are ready to jump into deep waters. We explore the 'Hello world' of deep learning - the MNIST dataset, where we classify 60,000 images into 10 classes (the 10 digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10).
Data science without an application is nothing but research. Since we at 365 believe that the skills you acquire should be relevant for your work, we finish the course with a business case, where we implement all the deep learning knowledge you've acquired.
This course is part of Module 3 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. Expanding on your statistical and programming skills from Modules 1 and 2, Module 3 is designed to improve your programming skills and develop your advanced statistical thinking. You will learn how to build complete linear and logistic regression models, how to cluster data, and how to build deep learning models with TensorFlow 2.0.See All Modules
Real-life project and data. Solve them on your own computer as you would in the office.
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The course is in-depth and is delivered at a steady pace with eye catching visuals. The instructors go through all the basics really well. They try not to over-simplify the material, you get a good sense аof how deep Data Science is in the course. Great job!!!
This course is amazing! After watching the video carefully and doing all the exercises, I am even capable of having discussions with Machine learning major Master’s students! High standard course with reasonable pricing.
Very clear and in-depth explanation of data science and how all the inter-related concepts apply in real life business environment. Absolutely great for beginners! Best data science course I have come across so far!
I would highly recommend the course to any beginner who wants to venture into the world of Data Science. The concepts are very well explained and there is an emphasis on practical application which really helps create a better understanding of the concepts.