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[Udemy] Neural Networks in Python: Deep Learning for Beginners

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What you’ll learn

  • Get a stable understanding of Synthetic Neural Networks (ANN) and Deep Learning
  • Perceive the enterprise eventualities the place Synthetic Neural Networks (ANN) is relevant
  • Constructing a Synthetic Neural Networks (ANN) in Python
  • Use Synthetic Neural Networks (ANN) to make predictions

Requirements

  • College students might want to set up Python and Anaconda software program however we now have a separate lecture that can assist you set up the sameS

Description

This course teaches you all of the steps of making a Neural community primarily based mannequin i.e. a Deep Learning mannequin, to unravel enterprise issues.

Beneath are the course contents of this course on ANN:

  • Half 1 – Python fundamentalsThis half will get you began with Python.This half will assist you to arrange the python and Jupyter setting in your system and it will train you methods to carry out some primary operations in Python. We’ll perceive the significance of various libraries equivalent to Numpy, Pandas & Seaborn.
  • Half 2 – Theoretical IdeasThis half will provide you with a stable understanding of ideas concerned in Neural Networks.On this part you’ll be taught in regards to the single cells or Perceptrons and the way Perceptrons are stacked to create a community structure. As soon as structure is ready, we perceive the Gradient descent algorithm to seek out the minima of a operate and find out how that is used to optimize our community mannequin.
  • Half 3 – Creating Regression and Classification ANN mannequin in PythonOn this half you’ll discover ways to create ANN fashions in Python.We’ll begin this part by creating an ANN mannequin utilizing Sequential API to unravel a classification downside. We discover ways to outline community structure, configure the mannequin and prepare the mannequin. Then we consider the efficiency of our skilled mannequin and use it to foretell on new knowledge. We additionally remedy a regression downside in which we attempt to predict home costs in a location. We may also cowl methods to create advanced ANN architectures utilizing practical API. Lastly we discover ways to save and restore fashions.We additionally perceive the significance of libraries equivalent to Keras and TensorFlow in this half.
  • Half 4 – Knowledge PreprocessingOn this half you’ll be taught what actions it is advisable take to arrange Knowledge for the evaluation, these steps are crucial for making a significant.On this part, we’ll begin with the essential idea of choice tree then we cowl knowledge pre-processing matters like  lacking worth imputation, variable transformation and Check-Prepare break up.
  • Half 5 – Traditional ML approach – Linear Regression
    This part begins with easy linear regression after which covers a number of linear regression.We’ve coated the essential idea behind every idea with out getting too mathematical about it so that you justperceive the place the idea is coming from and the way it is vital. However even in case you do not perceiveit,  it will likely be okay so long as you discover ways to run and interpret the outcome as taught in the sensible lectures.We additionally take a look at methods to quantify fashions accuracy, what’s the that means of F statistic, how categorical variables in the impartial variables dataset are interpreted in the outcomes and the way will we lastly interpret the outcome to seek out out the reply to a enterprise downside.

Who this course is for:

  • Folks pursuing a profession in knowledge science
  • Working Professionals starting their Neural Community journey
  • Statisticians needing extra sensible expertise

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