Introduction to machine learning
Machine Learning has seen a tremendous growth in recent years. The ever increasing amount of information collected by enterprises and organisations has emphasized the need to create intelligent software built on top of the collected data.
The use of neural networks and deep learning has been a major contributor to the success in this new field of software development. Historical data is used to train models that learns to identify complex patterns which can be used to analyse future events.
The fast development in this area has created a vast amount of possibilities for enterprises and organisations to create innovative solutions that solves and automates complex tasks. Today, machine learning is applied in numerous applications ranging from translation of text, classification of fraud and self driving cars.
In this course you will get a practical introduction to the field of neural networks. You’ll learn theory, potential use cases and how to implement neural networks in code. During this course you will receive a practical introduction to machine learning and an understanding of a very exciting field that keeps growing faster and faster.
You will learn
- Theoretical introduction to neural networks and their use cases
- Implementing neural networks
- Tensorflow - one of the largest frameworks for machine learning
- Introduction to a wide range of tools used in machine learning
The course primarily uses Python, Tensorflow and Jupyter
The course mixes theory with practical implementations. As the course is finished you will receive a copy of all course materials.
Programmers that is looking for a introduction to machine learning.
You should be comfortable programing. The course uses Python but previous experience with the language is not required.
Course summary Introduction
- Introduction to machine learning
- Introduction to supervised, unsupervised and reinforcement learning
- Regression & classification using machine learning
- Loss functions & gradient descent
- Introduction to Tensorflow
- Introduction to neural networks and their applications
- Layers, neurons, activation functions and regularisation
- Transfer learning
Image analysis using Neural Networks
- Introduction to convolutional neural networks
- Image classification using neural networks
- Sliding-window and object-detection
Neural networks and text analysis
- Introduction to Natural Language Processing(NLP)
- Representing text in neural networks
- Semantic analysis using neural networks
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