Artificial Neural Networks (ANN) with Keras in Python and R
Understand Deep Learning and build Neural Networks using TensorFlow 2.0 and Keras in Python and R
Note: 4.6/5 (795 notes) 146,500 students
Instructor(s): Start-Tech Academy
Last update: 2022-01-15
What you’ll learn
- Get a solid understanding of Artificial Neural Networks (ANN) and Deep Learning
- Learn usage of Keras and Tensorflow libraries
- Understand the business scenarios where Artificial Neural Networks (ANN) is applicable
- Building a Artificial Neural Networks (ANN) in Python and R
- Use Artificial Neural Networks (ANN) to make predictions
- Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same
You’re looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, 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 and R 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
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 and R 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 and R
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.
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!
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
- Anyone curious to master ANN from Beginner level in short span of time
- Course Resources
- Setting up Python and Jupyter Notebook
- Installing Python and Anaconda
- This is a milestone!
- Opening Jupyter Notebook
- Introduction to Jupyter – part 1
- Introduction to Jupyter – part 2
- Arithmetic operators in Python: Python Basics
- Strings in Python: Python Basics
- Lists, Tuples and Directories: Python Basics
- Working with Numpy Library of Python
- Working with Pandas Library of Python
- Working with Seaborn Library of Python
- Setting up R Studio and R Crash Course
- Installing R and R studio
- Basics of R and R studio
- Packages in R
- Inputting data part 1: Inbuilt datasets of R
- Inputting data part 2: Manual data entry
- Inputting data part 3: Importing from CSV or Text files
- Creating Barplots in R
- Creating Histograms in R
- Single Cells – Perceptron and Sigmoid Neuron
- Activation Functions
- Python – Creating Perceptron model
- Neural Networks – Stacking cells to create network
- Basic Terminologies
- Gradient Descent
- Back Propagation
- Important concepts: Common Interview questions
- Some Important Concepts
- Standard Model Parameters
- Tensorflow and Keras
- Keras and Tensorflow
- Installing Tensorflow and Keras in Python
- Installing TensorFlow and Keras in R
- Dataset for classification problem
- Python – Dataset for classification problem
- Python – Normalization and Test-Train split
- R – Dataset, Normalization and Test-Train set
- More about test-train split
- Python – Building and training the Model
- Different ways to create ANN using Keras
- Building the Neural Network using Keras
- Compiling and Training the Neural Network model
- Evaluating performance and Predicting using Keras
- R – Building and training the Model
- Building,Compiling and Training
- Evaluating and Predicting
- Python – Regression problems and Functional API
- Building Neural Network for Regression Problem
- Using Functional API for complex architectures
- R – Regression Problem and Functional API
- Building Regression Model with Functional AP
- Complex Architectures using Functional API
- Python – Saving and Restoring Models
- Saving – Restoring Models and Using Callbacks
- R – Saving and Restoring Models
- Saving – Restoring Models and Using Callbacks
- Python – Hyperparameter Tuning
- Hyperparameter Tuning
- R – Hyperparameter Tuning
- Hyperparameter Tuning
- Add on : Data Preprocessing
- Gathering Business Knowledge
- Data Exploration
- The Data and the Data Dictionary
- Importing Data in Python
- Importing the dataset into R
- Univariate Analysis and EDD
- EDD in Python
- EDD in R
- Outlier Treatment
- Outlier Treatment in Python
- Outlier Treatment in R
- Missing Value imputation
- Missing Value Imputation in Python
- Missing Value imputation in R
- Seasonality in Data
- Bi-variate Analysis and Variable Transformation
- Variable transformation and deletion in Python
- Variable transformation in R
- Non Usable Variables
- Dummy variable creation: Handling qualitative data
- Dummy variable creation in Python
- Dummy variable creation in R
- Test Train Split
- Test-train split
- Bias Variance trade-off
- Test train split in Python
- Test train split in R
- The final milestone!
- Congratulations & about your certificate
- Bonus lecture
Time remaining or 809 enrolls left
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