Artificial Neural Networks with NeuroLab and Python

You’re going to learn hands-on artificial neural networks with neurolab, a lesser-known and traditional Python library for artificial intelligence.


  • This is an intermediate level course. You should know Python programming, have basic math knowledge, and basic concepts of machine learning before enrolling.


You’re going to learn hands-on artificial neural networks with neurolab, a lesser-known and traditional Python library for artificial intelligence. This is a hands-on course and you will be working your way through with Python and Jupyter notebooks.

What you will learn:

  • Basic concepts of neural networks (refresher)
  • The perceptron
  • Single-layer neural network
  • Multi-layer neural network
  • Recurrent neural networks (RNN)
  • Optical character recognition (OCR)

Who this course is for:

  • This course is for you if want to learn practical machine learning
  • This course is also for you if you’re a machine learning professional or work in a management position and want to expand your technical knowledge in the field
  • If you’re a student, know some programming and want to learn about artificial neural networks with lesser know libraries, this course is also for you

Course content

Data Science: Intro To Deep Learning With Python In 2021

Understand the intuition behind Artificial Neural Networks

Build artificial neural networks with Tensorflow

Classify images, data using deep learning

Apply Convolutional Neural Networks in practice


  • Some prior coding experience with python is required.


Neural networks are a family of machine learning algorithms that are generating a lot of excitement. They are a technique that is inspired by how the neurons in our brains function. They are based on a simple idea: given certain parameters, it is possible to combine them in order to predict a certain result. For example, if you know the number of pixels in an image, there are ways of knowing which number is written in the image. The data that enters passes through various “ layers” in which a series of adjusted learning rules are applied by a weighted function. After passing through the last layer, the results are compared with the “correct” results, and the parameters are adjusted.

Although the algorithms and the learning process in general are complex, one the network has learned, it can freeze the various weights and function in a memory or execution mode. Google uses these types of algorithms, for example, for image searches.

There is no single definition for the meaning of Deep Learning. In general, when we talk of Deep Learning, we are referring to a group of Machine Learning algorithms based on neural networks that, as we have seen, are characterized by cascade data processing. The entrance signal passes through the various stages, and in each one, they are subjected to a non-linear transformation. This helps to extract and transform the variable according to the determined parameters (weights or boundaries). There isn’t an established limit for the number of stages that a neural network must contain to be considered Deep Learning. However, it is thought that Deep Learning arose in the 80’s, using a model which had 5 or 6 layers. It was (and is) called the neocognitron and was created by the Japanese researcher Kunihiki Fukushima. Neural networks are very effective in identifying patterns.

An example worth highlighting of the application of Deep Learning is the project carried out by Google and the Universities of Stanford and Massachusetts. It aimed to improve the natural language processing techniques of a type of AI called Recurrent Neural Network Language Model (RNNLM). It’s used for automatic translations and creating subtitles, among other thing. Basically, it builds up phrases word by words, basing each word on the previous one and in this way, it can even write poems.

Module 1

1. Introduction to Deep Learning and TensorFlow

2. Basics of Neural Networks

3. Designing a shallow neural network (Scratch and python) (Project)

4. Deeper neural network using TensorFlow. (Project)

Who this course is for:

  • Beginners In Python
  • Beginners In Deep Learning
  • Beginners In Machine Learning

Course content

Learn Python with Google Colab – A Step to Machine Learning

Basics of Python

Data types

Printing output

If-Else conditions

Looping using for, while

Arithmatic operations

Working with functions

Working with List and Arrays

Performing action on List

Tuple, Set and Dictionary

Working with packages

Hands-on with Class


  • Basic programming concepts are sufficient


This course is completely practical based and is per-requisite for our upcoming Machine Learning course. With around 25 lectures, this course is designed in such a way that you can take spark of Google Colab enabling Jupiter notebook , the best platform to practice Machine Learning  and is enriched with all the basic concepts that is required to start with python programming. After completing this course, you should have basic python understanding.

Who this course is for:

  • Beginner and Intermediate

Course content

Introduction to Data Science using Python (Module 1/3)

Understand the basics of Data Science and Analytics

Understand how to use Python and Scikit learn

Get a good understanding of all buzz words like “Data Science”, “Machine learning”, “Data Scientist” etc.


  • This course does not have any pre-requisities. All you need is a Windows or a MAC machine.


Are you completely new to Data science?

Have you been hearing these buzz words like Machine learning, Data Science, Data Scientist, Text analytics, Statistics and don’t know what this is?

Do you want to start or switch career to Data Science and analytics?

If yes, then I have a new course for you. In this course, I cover the absolute basics of Data Science and Machine learning. This course will not cover in-depth algorithms. I have split this course into 3 Modules. This module, takes a 500,000ft. view of what Data science is and how is it used. We will go through commonly used terms and write some code in Python. I spend some time walking you through different career areas in the Business Intelligence Stack, where does Data Science fit in, What is Data Science and what are the tools you will need to get started. I will be using Python and Scikit-Learn Package in this course. I am not assuming any prior knowledge in this area. I have given some reading materials, which will help you solidify the concepts that are discussed in this lectures.

This course will the first data science course in a series of courses. Consider this course as a 101 level course, where I don’t go too much deep into any particular statistical area, but rather just cover enough to raise your curiosity in the field of Data Science and Analytics.

The other modules will cover more complex concepts. 

Who this course is for:

  • Anyone who wants to learn about Data Science from absolute scratch.
  • Anyone who wants to switch or make a career in Data Science and Analytics
  • Anyone who is curious to know what is Data Science and what does a Data Scientist do in his/her day job.

Course content

Beginners Guide to Machine Learning – Python, Keras, SKLearn

Gain a foundational understanding of machine learning

Implement both supervised and unsupervised machine learning models

Measure the performances of different machine learning models using the suitable metrics

Understand which machine learning model to use in which situation

Reduce data of higher dimensions to data of lower dimensions using principal component analysis


  • A windows machine, and a willingness to learn


In this course, we will cover the foundations of machine learning. The course is designed to not beat around the bush, and cover exactly what is needed concisely and engagingly. Based on a university level machine learning syllabus, this course aims to effectively teach, what can sometimes be dry content, through the use of entertaining stories, professionally edited videos, and clever scriptwriting. This allows one effectively absorb the complex material, without experiencing the usual boredom that can be experienced when trying to study machine learning content.   

The course first goes into a very general explanation of machine learning. It does this by telling a story that involves an angry farmer and his missing donuts. This video sets the foundation for what is to come.

After a general understanding is obtained, the course moves into supervised classification. It is here that we are introduced to neural networks through the use of a plumbing system on a flower farm.

Thereafter, we delve into supervised regression, by exploring how we can figure out whether certain properties are value for money or not.

We then cover unsupervised classification and regression by using other farm-based examples.

This course is probably the best foundational machine learning course out there, and you will definitely benefit greatly from it.

Who this course is for:

  • Beginners to machine learning. College students looking to improve their capability. Professionals looking to implement machine learning in their day to day business.

Course content

Practical Machine Learning for Beginners in 2022

Understand how to take a model from notebook to deployment

Ability to use Flask framework for machine learning model deployment

Ability to Use Postman application to test your model API endpoints

Understand How to leverage on Datasist ibrary for faster model building and deployment


  • Introductory knowledge to Python suffices


This course is for every beginner in the data science space. We have been there before and we understood what your learning challenges are. This short course will focus on showing you end to end what it takes to build and deploy a simple machine learning solution.

We will be building a car pricing prediction engine. This will be purely hands-on.

You will be able to deploy this solution using the flask framework as an API and also as a Platform.

We will also introduce you to libraries that make it easy to quickly explore, build, and deploy a machine learning solution.

This course assumes you do not have any prior knowledge of Machine Learning or that you have taken a couple of courses but still missing the full picture.

Machine learning models are not useful in silos and this free course will show you the full picture and cover the knowledge gaps.

You will also be able to put to use your knowledge of HTML as we build a simple web interface to interact with the solution.

This course will also introduce you to Postman Application. It is a popular use for testing API and solutions. Postman will help us to interact with the API deployment of our model while our browser is sufficient for interacting with the platform deployment.

We will be making use of the following major applications:

1. Jupyter Notebook

2. Visual Studio Code

3. Postman

Who this course is for:

  • Data Science Enthusiast
  • Beginner Python Developers curious about Data Science
  • Data Analyst willing to move to the predictive space
  • Business People curious about model building and deployment

Course content

Machine Learning with Python

Supervised learning

Unsupervised learning

Regression learning



  • install numpy matplotlib and pandas


Machine Learning tutorial provides basic and advanced concepts of machine learning. Our machine learning tutorial is designed for students and working professionals.

Machine learning is a growing technology which enables computers to learn automatically from past data. Machine learning uses various algorithms for building mathematical models and making predictions using historical data or information. Currently, it is being used for various tasks such as image recognitionspeech recognitionemail filteringFacebook auto-taggingrecommender system, and many more.

This machine learning tutorial gives you an introduction to machine learning along with the wide range of machine learning techniques such as SupervisedUnsupervised, and Reinforcement learning. You will learn about regression and classification models, clustering methods, hidden Markov models, and various sequential models.

When you tag a face in a Facebook photo, it is AI that is running behind the scenes and identifying faces in a picture. Face tagging is now omnipresent in several applications that display pictures with human faces. Why just human faces? There are several applications that detect objects such as cats, dogs, bottles, cars, etc. We have autonomous cars running on our roads that detect objects in real time to steer the car. When you travel, you use Google Directions to learn the real-time traffic situations and follow the best path suggested by Google at that point of time. This is yet another implementation of object detection technique in real time.

Let us consider the example of Google Translate application that we typically use while visiting foreign countries. Google’s online translator app on your mobile helps you communicate with the local people speaking a language that is foreign to you.

There are several applications of AI that we use practically today. In fact, each one of us use AI in many parts of our lives, even without our knowledge. Today’s AI can perform extremely complex jobs with a great accuracy and speed. Let us discuss an example of complex task to understand what capabilities are expected in an AI application that you would be developing today for your clients.


We all use Google Directions during our trip anywhere in the city for a daily commute or even for inter-city travels. Google Directions application suggests the fastest path to our destination at that time instance. When we follow this path, we have observed that Google is almost 100% right in its suggestions and we save our valuable time on the trip.

You can imagine the complexity involved in developing this kind of application considering that there are multiple paths to your destination and the application has to judge the traffic situation in every possible path to give you a travel time estimate for each such path. Besides, consider the fact that Google Directions covers the entire globe. Undoubtedly, lots of AI and Machine Learning techniques are in-use under the hoods of such applications.

Considering the continuous demand for the development of such applications, you will now appreciate why there is a sudden demand for IT professionals with AI skills.

Who this course is for:

  • Python developers curious about Data Science
  • Machine learners
  • Computer Science Engineers

Course content

The Top 5 Machine Learning Libraries in Python

You’ll receive the completely annotated Jupyter Notebook used in the course.

You’ll be able to define and give examples of the top libraries in Python used to build real world predictive models.

You will be able to create models with the most powerful language for machine learning there is.

You’ll understand the supervised predictive modeling process and learn the core vernacular at a high level.


  • There are no prerequisites however knowledge of Python will be helpful.
  • A familiarity with the concepts of machine learning would be helpful but aren’t necessary.


Recent Review from Similar Course:

“This was one of the most useful classes I have taken in a long time. Very specific, real-world examples. It covered several instances of ‘what is happening’, ‘what it means’ and ‘how you fix it’. I was impressed.”  Steve

Welcome to The Top 5 Machine Learning Libraries in Python.  This is an introductory course on the process of building supervised machine learning models and then using libraries in a computer programming language called Python.

What’s the top career in the world? Doctor? Lawyer? Teacher? Nope. None of those.

The top career in the world is the data scientist. Great. What’s a data scientist?

The area of study which involves extracting knowledge from data is called Data Science and people practicing in this field are called as Data Scientists.

Business generate a huge amount of data.  The data has tremendous value but there so much of it where do you begin to look for value that is actionable? That’s where the data scientist comes in.  The job of the data scientist is to create predictive models that can find hidden patterns in data that will give the business a competitive advantage in their space.

Don’t I need a PhD?  Nope. Some data scientists do have PhDs but it’s not a requirement.  A similar career to that of the data scientist is the machine learning engineer.

machine learning engineer is a person who builds predictive models, scores them and then puts them into production so that others in the company can consume or use their model.  They are usually skilled programmers that have a solid background in data mining or other data related professions and they have learned predictive modeling.

In the course we are going to take a look at what machine learning engineers do. We are going to learn about the process of building supervised predictive models and build several using the most widely used programming language for machine learning. Python. There are literally hundreds of libraries we can import into Python that are machine learning related.

library is simply a group of code that lives outside the core language. We “import it” into our work space when we need to use its functionality. We can mix and match these libraries like Lego blocks.

Thanks for your interest in the The Top 5 Machine Learning Libraries in Python and we will see you in the course. 

Who this course is for:

  • If you’re looking to learn machine learning then this course is for you.

Course content

Getting Started with Machine Learning

Getting Started with Machine Learning


  • Python, Matplotlib, Pandas, Numpy


This course is especially for beginners who want to get started their journey in the field of machine learning.  This course provides the hands-on experience with the python and scikit learn. So if you are new to the machine learning Get started with this course will be a good choice.

Who this course is for:

  • Begginer Python developers who want to get started with Machine Learning

Course content

Practical Machine Learning with Scikit-Learn

How to implement regression, classification and boosting algorithms

Which algorithms work best for a given dataset

Data preprocessing


  • Basic python knowledge
  • Google Colab account


Machine learning is a rapidly growing field. However, a lot of courses on the internet today do not go over some of it’s most powerful algorithms. In this course, we will learn multiple machine learning algorithms, along with data preprocessing, all in under an hour. We will go over regression, classification, component analysis and boosting all in scikit-learn, one of the most popular machine learning libraries for python.

Algorithms we’ll go over (in order):

  • Linear Regression
  • Polynomial Regression
  • Multiple Linear Regression
  • Logistic Regression
  • Support Vector Machines
  • Decision Trees
  • Random Forest
  • Principle Component Analysis
  • Gradient Boosting
  • XGBoost

Who this course is for:

  • People looking to get into AI but don’t know where to start
  • People who want to build accurate models as quickly as possible

Course content