Machine Learning using Python


The Machine Learning Course that dives deeper into the basic knowledge of the technology using one of the most popular and well-known language, i.e. Python. During this course, students will be taught about the significance of the Machine Learning and its applicability in the real world. Secondly, the machine learning online course will give you a proper overview about Machine Learning topics such as its algorithms, model evaluation as well as supervised vs unsupervised learning.

During the Machine Learning with Python course, you'll learn about the following -

  • Become expert at Machine Learning on Python
  • Supervised vs Unsupervised Machine Learning
  • Setting up a Python development environment in a correct manner
  • Develop new features for improving algorithm predictions
  • Handling advanced techniques such as Dimensionality Reduction
  • How Machine Learning is associated with Statistical Modelling, and how to compare
  • both of them.
  • The diverse ways through which machine learning is useful to society

Who is the right candidate for the course?
  • Anyone who is keen to learn machine learning algorithm using Python
  • Any person who wants to learn about practical application of machine learning to solve real world problems
  • Individuals with basic knowledge of Machine Learning who want to develop their understanding of the machine learning algorithms
  • Intermediate EXCEL users not able to work with large datasets
  • Anyone looking to start a career as a data scientist
  • Individuals who want to utilize and apply the technology of Machine learning to their domain

  • Training

    1 month Training, 6 months access, Industry-Oriented, Self-Paced.

  • Certification

    Small & basic Objective MCQ type online exams. Microsoft Technology Associate & Foxmula Certification.

  • Internship

    45 days Internship Completion letter post project submission on our GitLab. Projects are Industrial, Small and based on your training.


  • Perform data operations using Data Types and Operators
  • Control Flow with Decision and Loops
  • Perform Input and Output Operations
  • Document and Structure Code
  • Perform Troubleshooting and Error Handling
  • Perform Operations using Modules and Tools
  • Data Preprocessing: Missing Data, Categorical Data & Feature Scaling
  • Regression I: Linear Regression, Multiple Regression, Polynomial Regression
  • Regression II: Logistic Regression, K-Nearest Neighbors
  • Support Vector Machines (SVM) & Kernel SVM
  • Clustering: K-Means and Hierarchical
  • Natural Language Processing (NLP)
  • Intro to Neural Networks: Artificial Neural Networks (ANN)
  • Enterprise Application of Machine Learning