KNIME with Python and R for Machine Learning Training Course


Getting Started with Knime

  • What is KNIME?
  • KNIME Analytics
  • KNIME Server

Machine Learning

  • Computational learning theory
  • Computer algorithms for computational experience

Preparing the Development Environment

  • Installing and configuring KNIME


  • Adding nodes
  • Accessing and reading data
  • Merging, splitting, and filtering data
  • Grouping and pivoting data
  • Cleaning data


  • Creating workflows
  • Importing data
  • Preparing data
  • Visualizing data
  • Creating a decision tree model
  • Working with regression models
  • Predicting data
  • Comparing and matching data

Learning Techniques

  • Working with random forest techniques
  • Using polynomial regression
  • Assigning classes
  • Evaluating models

Summary and Conclusion

Understanding Machine Learning

What are models in Machine Learning?

How to build models for Machine Learning?

How does Machine Learning build a Linear Regression model?


  • Some knowledge of programming in any language is essential.


Machine Learning is becoming ubiquitous across all industries. Already many applications have been identified which use Machine Learning now. Few examples include Spam Detection, Face Recognition, Emotion Analysis, Object Detection, Credit Card Fraud Detection, Weather Prediction, and the list is almost endless. More new applications are being identified by different industries almost everyday.

It is not just about applying superior technology for traditional problems when we apply Machine Learning. It is also about business sense since applying Machine Learning, we can make experiments and applications much more economical.

This course is a result of a discussion among my Project Team from our cohort in IIT, Kanpur learning Cyber Security. We have embarked to create a product for Malware Detection using Machine Learning. While all of us are getting grips on Malware Analysis, the team needed some inputs of Machine Learning. To fill the gap, I conducted some sessions with our Project Team members on Machine Learning. This course is a collection of the recording of these sessions.

This course discusses what are Machine Learning Algorithms. We discuss Random Forest Algorithm and Linear Regression as examples to understand what are models in Machine Learning. We see how to implement such models using Python. During the discussion on the development of the Machine Learning models, we discuss the various steps like Data Preprocessing, Normalisation, Scaling, etc. We touch upon the basics of Neural Network and take a slight deep dive into Regression. The course includes discussion on concepts like what is overfitting, what is hyper-parameter tuning, etc.

This course tries to give an idea for what it takes to create a product which uses Machine Learning. I believe that the discussions can get one started to apply Machine Learning to many problems.

Who this course is for:

  • Students
  • Professionals
  • Engineers
  • Researchers

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

Machine Learning Fundamentals [Python]

What is basically Machine Learning? How Machine Learns?

Learn the most Basic Mathematics behind Simple Linear Regression and its Best fit line.

What is Gradient Descent, how it works Internally with full Mathematical explanation.


  • For Machine Learning Concept no prerequisite. Anyone can do this course.
  • Understanding of Data Preprocessing is required for Coding.
  • After completing this course, you can connect to me on my blog for any question.
  • Python is required to do the coding part


This course is designed to understand basic Concept of Machine Learning.  Anyone can opt for this course. No prior understanding of Machine Learning is required.  Simple Linear Regression Concepts are covered in detail. Coding part is not covered, however wherever possible I have attached the code in the resources.

Now question is why this course?

This Course will not only teach you the basics of Machine learning and Simple Linear Regression. It will also cover in depth mathematical explanation of Cost function and use of Gradient Descent for Simple Linear Regression. Understanding these is must for a solid foundation before entering into Machine Learning World. This foundation will help you to understand all other algorithms and mathematics behind it.

Who this course is for:

  • Anyone who is looking or dont know from where to start Machine Learning can opt for this course.
  • This will provide a good foundation in understanding concept of Machine Learning.

Course content

Introduction to Machine Learning

Theoretical concept of Machine learning

Concepts of Regression

Concepts of data preprocessing

Overall Machine Learning theory and its algorithms


  • None. You will learn everything from its basic


The course introduces the concepts of Machine Learning.  It covers the regression, both linear and logistic. Decision trees, Data preprocessing etc., to explore the machine learning in brief with plenty of examples. it also discusses the concept of state space, bias, etc. in Machine learning. Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. This course covers the following aspects of machine learning:

1.Basic Definitions 2.Types of Learning

3.Hypothesis space and Inductive Bias



6.Linear Regression

7.Decision Trees 8.Overfitting

The course is designed after studying the syllabus of various technological universities. Machine learning is basically of three types: The main goal in supervised learning is to learn a model from labeled training data that allows us to make predictions about unseen or future data. Supervised refers to a set of samples where the desired output signals (labels) are already known.

Unsupervised learning deals with unlabeled data or data of unknown structure. Using unsupervised learning techniques explores the structure of our data to extract meaningful information without the guidance of a known outcome variable or reward function.

In reinforcement learning, the goal is to develop a system (agent) that improves its performance based on interactions (reward signals) with the environment.

Who this course is for:

  • The course is intended for the students of BCA, BE, BTech and anybody interested in Machine Learning

Course content