Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow| Python
What you'll learn
·
Get a solid understanding of Artificial Neural Networks (ANN)
and Deep Learning
·
Understand the business scenarios where Artificial Neural
Networks (ANN) is applicable
·
Building a Artificial Neural Networks (ANN) in Python
·
Use Artificial Neural Networks (ANN) to make predictions
·
Learn usage of Keras and Tensorflow libraries
·
Use Pandas DataFrames to manipulate data and make statistical
computations.
Requirements
·
Students will need to install Python and Anaconda software but
we have a separate lecture to help you install the sameS
Description
You're looking for a complete Artificial
Neural Network (ANN) course that teaches you everything you need to
create a Neural Network model in Python, right?
You've found the right Neural Networks course!
After completing this course you will
be able to:
- Identify the business problem
which can be solved using Neural network Models.
- Have a clear understanding of
Advanced Neural network concepts such as Gradient Descent, forward and
Backward Propagation etc.
- Create Neural network models in
Python using Keras and Tensorflow libraries and analyze their results.
- Confidently practice, discuss
and understand Deep Learning concepts
How this course will help you?
A Verifiable Certificate of Completion is
presented to all students who undertake this Neural networks course.
If you are a business Analyst or an executive,
or a student who wants to learn and apply Deep learning in Real world problems
of business, this course will give you a solid base for that by teaching you
some of the most advanced concepts of Neural networks and their implementation
in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one
should take to create a predictive model using Neural Networks.
Most courses only focus on teaching how to run
the analysis but we believe that having a strong theoretical understanding of
the concepts enables us to create a good model . And after running the
analysis, one should be able to judge how good the model is and interpret the
results to actually be able to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj.
As managers in Global Analytics Consulting firm, we have helped businesses
solve their business problem using Deep learning techniques and we have used
our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most
popular online courses - with over 250,000 enrollments and thousands of 5-star
reviews like these ones:
This is very good, i love the fact the all
explanation given can be understood by a layman - Joshua
Thank you Author for this wonderful course.
You are the best and this course is worth any price. - Daisy
Our Promise
Teaching our students is our job and we are
committed to it. If you have any questions about the course content, practice
sheet or anything related to any topic, you can always post a question in the
course or send us a direct message.
Download Practice files, take Practice test,
and complete Assignments
With each lecture, there are class notes
attached for you to follow along. You can also take practice test to check your
understanding of concepts. There is a final practical assignment for you to
practically implement your learning.
What is covered in this course?
This course teaches you all the steps of
creating a Neural network based model i.e. a Deep Learning model, to solve
business problems.
Below are the course contents of this course
on ANN:
- Part 1 - Python basics
This part gets you
started with Python.
This part will help
you set up the python and Jupyter environment on your system and it'll teach
you how to perform some basic operations in Python. We will understand the
importance of different libraries such as Numpy, Pandas & Seaborn.
- Part 2 - Theoretical Concepts
This part will give
you a solid understanding of concepts involved in Neural Networks.
In this section you
will learn about the single cells or Perceptrons and how Perceptrons are
stacked to create a network architecture. Once architecture is set, we
understand the Gradient descent algorithm to find the minima of a function and
learn how this is used to optimize our network model.
- Part 3 - Creating Regression
and Classification ANN model in Python
In this part you will
learn how to create ANN models in Python.
We will start this
section by creating an ANN model using Sequential API to solve a classification
problem. We learn how to define network architecture, configure the model and
train the model. Then we evaluate the performance of our trained model and use
it to predict on new data. We also solve a regression problem in which we try
to predict house prices in a location. We will also cover how to create complex
ANN architectures using functional API. Lastly we learn how to save and restore
models.
We also understand the
importance of libraries such as Keras and TensorFlow in this part.
- Part 4 - Data Preprocessing
In this part you will
learn what actions you need to take to prepare Data for the analysis, these
steps are very important for creating a meaningful.
In this section, we
will start with the basic theory of decision tree then we cover data
pre-processing topics like missing value imputation, variable
transformation and Test-Train split.
- Part 5 - Classic
ML technique - Linear Regression
This section starts with simple linear regression and then covers multiple linear regression.
We have covered the
basic theory behind each concept without getting too mathematical about it so
that you
understand where the
concept is coming from and how it is important. But even if you don't
understand
it, it will be
okay as long as you learn how to run and interpret the result as taught in the
practical lectures.
We also look at how to
quantify models accuracy, what is the meaning of F statistic, how categorical
variables in the independent variables dataset are interpreted in the results
and how do we finally interpret the result to find out the answer to a business
problem.
By the end of this course, your confidence in
creating a Neural Network model in Python will soar. You'll have a thorough
understanding of how to use ANN to create predictive models and solve business
problems.
Go ahead and click the enroll button, and I'll
see you in lesson 1!
Cheers
Start-Tech Academy
------------
Below are some popular FAQs of students who
want to start their Deep learning journey-
Why use Python for Deep Learning?
Understanding Python is one of the valuable
skills needed for a career in Deep Learning.
Though it hasn’t always been, Python is the
programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on
Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on
KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists
reported using Python daily, making it the number one tool for analytics
professionals.
Deep Learning experts expect this trend to
continue with increasing development in the Python ecosystem. And while your
journey to learn Python programming may be just beginning, it’s nice to know
that employment opportunities are abundant (and growing) as well.
What is the difference between Data Mining,
Machine Learning, and Deep Learning?
Put simply, machine learning and data mining
use the same algorithms and techniques as data mining, except the kinds of
predictions vary. While data mining discovers previously unknown patterns and
knowledge, machine learning reproduces known patterns and knowledge—and further
automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses
advanced computing power and special types of neural networks and applies them
to large amounts of data to learn, understand, and identify complicated
patterns. Automatic language translation and medical diagnoses are examples of
deep learning.
Who this course is
for:
·
People pursuing a career in data science
·
Working Professionals beginning their Neural Network journey
·
Statisticians needing more practical experience
·
Anyone curious to master ANN from Beginner level in short span
of time
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