Data Science


Neural Networks in Python: Deep Learning for Beginners

Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow| Python

4.16 (874 reviews)



9 hours


Nov 2020

Last Update
Regular Price

What you will 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.


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!


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.


Neural Networks in Python: Deep Learning for Beginners
Neural Networks in Python: Deep Learning for Beginners
Neural Networks in Python: Deep Learning for Beginners
Neural Networks in Python: Deep Learning for Beginners



Welcome to the course

Introduction to Neural Networks and Course flow

Course resources

Setting up Python and Jupyter Notebook

Installing Python and Anaconda

Opening Jupyter Notebook

Introduction to Jupyter

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

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


Practice Test

Test your conceptual understanding

Tensorflow and Keras

Keras and Tensorflow

Installing Tensorflow and Keras

Python - Dataset for classification problem

Dataset for classification

Normalization and 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

Python - Solving a Regression problem using ANN

Building Neural Network for Regression Problem

Complex ANN Architectures using Functional API

Using Functional API for complex architectures

Saving and Restoring Models

Saving - Restoring Models and Using Callbacks

Hyperparameter Tuning

Hyperparameter Tuning

Add-on 1: Data Preprocessing

Gathering Business Knowledge

Data Exploration

The Dataset and the Data Dictionary

Importing Data in Python

Univariate analysis and EDD

EDD in Python

Outlier Treatment

Outlier Treatment in Python

Missing Value Imputation

Missing Value Imputation in Python

Seasonality in Data

Bi-variate analysis and Variable transformation

Variable transformation and deletion in Python

Non-usable variables

Dummy variable creation: Handling qualitative data

Dummy variable creation in Python

Correlation Analysis

Correlation Analysis in Python

Add-on 2: Classic ML models - Linear Regression

The Problem Statement

Basic Equations and Ordinary Least Squares (OLS) method

Assessing accuracy of predicted coefficients

Assessing Model Accuracy: RSE and R squared

Simple Linear Regression in Python

Multiple Linear Regression

The F - statistic

Interpreting results of Categorical variables

Multiple Linear Regression in Python

Test-train split

Bias Variance trade-off

Test train split in Python

Practice Assignment


LakshmiKishore14 November 2020

Superb!! Clarity in Explanation. Have Gone Through many on web but this one is pretty clear. If Mathematical part(two videos done on needed calculus) also covered in depth it will help academically too.

Erick24 August 2020

The content is really good as a firts approach to DL, something that can improve is the attention to the questions in the forum.

Sebastian22 July 2020

The course is well presented with clear (step by step) explanations. One can practice with real examples. It's a good course

Sergii8 July 2020

can add in the beginning more information on the purpose of the soft , like why do we need this jypter notebook at all

ANIL16 June 2020

I really found this course is interesting. Trainers are explaining the contents in excellent and easiest way. Thanks you so much.

Shivam11 June 2020

I learn ANN with smoothness as this course provide depth knowledge of mathematics too behing deep leaning parameters

P.Srideviponmalar16 May 2020

My last Practice Assignment is still not showing a tick mark though my course is 100% completed. Please take some action and reply as soon as possible.

Babalola,15 May 2020

I love the course delivery particularly the focus on Machine learning and I must say it was well delivered. Kudos to our amiable instructors!

Lewis15 May 2020

I have experience with ANN and Python, however I was hoping to understand the topic in more detail without focussing too heavily on the mathematics. So far so good.

José12 May 2020

I have seen a few lessons yet, but by now it seems a good course to me. It begins from te essentials. As an adittional comment, I'm not english native speaker and the english used by the tutors are very easy to undestand to me.

Thomas7 May 2020

the pronunciation of the speakers makes it difficult for me to understand. Also, the back noise from cars is kinda distracting. I find the way of presenting the topic efficient enough

Umang22 April 2020

Really very well explained with practical solution that will helpful for beginners and intermediate learner of this course.

Twarit19 April 2020

It was a good beginning of my learning of ANN and hopefully I will be able to build on this learning.

Oana6 April 2020

I am satisified with the content of this course. The first lessons are very useful to familiarize yourself with Python. There are presented simple applications. I really enjoyed these lectures.

Abdelrahman27 March 2020

more explanation of parameters & please improve the [cc] in the video has a lot of mistakes and I don't really understand the accent


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