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Diploma in Neural Networks in Python - Deep Learning for Beginners

This free online course teaches you to build artificial neural networks and deep learning models using Python language.

Publisher: Start-Tech Academy
Does the world of artificial neural networks intrigue you? This free online course is your first step to understanding Artificial Neural Networks and Deep Learning. The videos will guide you through this process by helping you to build neural network models using Python. You will then use those models to make near-accurate predictions as well as learn Python from scratch and use the libraries and data frames in Python to manipulate data.
Diploma in Neural Networks in Python - Deep Learning for Beginners
  • Duration

    6-10 Hours
  • Students

    731
  • Accreditation

    CPD

Description

Modules

Outcome

Certification

View course modules

Description

If you are a deep learning enthusiast, here is your opportunity to become skilled in Artificial Neural Networks (ANN) within a short period. Without needing advanced mathematical or programming knowledge, this python machine learning tutorial will teach you all that you need to know to build predictive deep learning models using Python and its libraries - Keras and Tensorflow. You will have the opportunity to practice as you learn by following simple step-by-step instructions provided through the video lectures. The steps are demonstrated to you and are supported by explaining the theories and rationale behind each. You will begin the course by understanding the applications of neural network models and deep learning and their advantages and then be introduced to different concepts such as perceptrons, network architecture, and gradient descent.

The next part of the course requires you to have some basic Python skills. This will enable you to see how the gradient descent algorithm is used to find the minima of a function and this is used to optimize the network model. You will be given a crash course in Python, whereby you will be familiarised with the Jupyter environment for Python programming and libraries such as Numpy, Pandas, and Seaborn. You may skip this part if you are already skilled in Python. At this point, you will be ready to start creating a simple ANN model and will begin by using the Sequential API to solve a classification problem and then move on to solve a regression problem - this includes learning how to create complex ANN architectures using the functional API. Finally in this Python course, you will learn how to save and restore models. If you want to dive deeper into the process of data analysis and preprocessing for ANN models, follow the next section of this introduction to machine learning with Python, which discusses the basic theory of a decision tree and the techniques of missing value imputation, variable transformation and Test-Train split. The classic machine learning technique of linear regression will follow. You will also learn to quantify a model’s accuracy, interpret the results of predictive analysis and find the solution to a business problem. 

This course is for those who would like to pursue a career in data science or use the science of neural networks to solve complex business problems or for anyone wanting to learn machine learning with Python. Due to advancement in data mining and computing power in recent years, business machines have acquired enhanced deep learning capabilities. All those who can harness those capabilities have immense power in their hands. It is no wonder, therefore, that they are much sought-after in today’s professional world. By enrolling in this course, you will leap in a direction that has numerous career opportunities in many data and analytical business fields.

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