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- Get a strong understanding of Artificial Neural Networks (ANN) and Deep Learning
- Understand the enterprise eventualities the place Artificial Neural Networks (ANN) is relevant
- Building a Artificial Neural Networks (ANN) in Python
- Use Artificial Neural Networks (ANN) to make predictions
- Learn utilization of Keras and Tensorflow libraries
- Use Pandas DataFrames to govern information and make statistical computations.
What is roofed in this course?
This course teaches you all of the steps of making a Neural community based mostly mannequin i.e. a Deep Learning mannequin, to unravel enterprise issues.
Below are the course contents of this course on ANN:
Part 1 – Python fundamentals
This half will get you began with Python.
This half will aid you arrange the python and Jupyter surroundings in your system and it’s going to train you easy methods to carry out some fundamental operations in Python. We will perceive the significance of various libraries similar to Numpy, Pandas & Seaborn.
Part 2 – Theoretical Concepts
This half gives you a strong understanding of ideas concerned in Neural Networks.
In this part you’ll be taught concerning the single cells or Perceptrons and how Perceptrons are stacked to create a community structure. Once structure is ready, we perceive the Gradient descent algorithm to seek out the minima of a operate and learn the way that is used to optimize our community mannequin.
Part 3 – Creating Regression and Classification ANN mannequin in Python
In this half you’ll learn to create ANN fashions in Python.
We will begin this part by creating an ANN mannequin using Sequential API to unravel a classification drawback. We learn to outline community structure, configure the mannequin and practice the mannequin. Then we consider the efficiency of our skilled mannequin and use it to foretell on new information. We additionally resolve a regression drawback in which we attempt to predict home costs in a location. We may also cowl easy methods to create advanced ANN architectures using purposeful API. Lastly we learn to save and restore fashions.
We additionally perceive the significance of libraries similar to Keras and TensorFlow in this half.
Part 4 – Data Preprocessing
In this half you’ll be taught what actions it’s good to take to organize Data for the evaluation, these steps are crucial for making a significant.
In this part, we’ll begin with the fundamental concept of choice tree then we cowl information pre-processing subjects like lacking worth imputation, variable transformation and Test-Train cut up.
Part 5 – Classic ML method – Linear Regression
This part begins with easy linear regression and then covers a number of linear regression.
We have lined the fundamental concept behind every idea with out getting too mathematical about it so that you simply
perceive the place the idea is coming from and how it will be significant. But even in the event you do not perceive
it, will probably be okay so long as you learn to run and interpret the outcome as taught in the sensible lectures.
We additionally take a look at easy methods to quantify fashions accuracy, what’s the which means of F statistic, how categorical variables in the impartial variables dataset are interpreted in the outcomes and how can we lastly interpret the outcome to seek out out the reply to a enterprise drawback.
By the top of this course, your confidence in making a Neural Network mannequin in Python will soar. You’ll have a radical understanding of easy methods to use ANN to create predictive fashions and resolve enterprise issues.
Go forward and click on the enroll button, and I’ll see you in lesson 1!
Below are some fashionable FAQs of scholars who need to begin their Deep studying journey-
Why use Python for Deep Learning?
Understanding Python is likely one of the invaluable abilities wanted for a profession in Deep Learning.
Though it hasn’t all the time been, Python is the programming language of alternative for information science. Here’s a quick historical past:
In 2016, it overtook R on Kaggle, the premier platform for information science competitions.
In 2017, it overtook R on KDNuggets’s annual ballot of knowledge scientists’ most used instruments.
In 2018, 66% of knowledge scientists reported using Python every day, making it the primary device for analytics professionals.
Deep Learning specialists count on this development to proceed with growing improvement in the Python ecosystem. And whereas your journey to be taught Python programming could also be simply starting, it’s good to know that employment alternatives are considerable (and rising) as nicely.