Machine Learning Lab

The Machine Learning Lab amalgamates “Theory” and “Practice”  and offers a complete startup solution for a machine learning enthusiastic who need to understand concepts involved along with a strong application practices. Primarily, the foundational material and tools for Data Science are presented via Sk-Learn are covered in the experimentation and continue rapidly into exploratory data analysis and classical machine learning, where the data is organized, characterized, and manipulated. The Lab resource enables the learners to move from engineered models into custom application based approach. The resource provides a complete solution for the learner to get started with machine learning principles and concepts. This lab resource can be used by any department to strengthen their students towards using data analysis and machine learning for their applications.
Commercial details can be requested by clicking 'Request a Quote' option.
  • Edutech ML Resource Kit contains the necessary pre-installed tools, lab examples etc. required to get immediately started with the learning activity.
  • Can be easily implemented in the existing lab with certain prerequisites.
  • Multi user feature enables more number of students to learn, test and apply machine learning concepts.
  • Lab resource is provided with a workshop for an immediate start.
The Machine Learning Lab Resource contains the following experimentation topics.
  • Linear Regression
  • Polynomial Regression
  • Logistic Regression
  • kNN (k Nearest Neighbourhood)
  • K-Means Clustering
  • SVM (Support Vector Machine)
  • Gradient Descent
  • Newton’s Method
  • MLE (Maximum Likelihood Estimation)
  • MAP (Maximum A Posteriori)
  • PCA (Principle Component Analysis)
  • L1 Regularization (Lasso Regression)
  • L2 Regularization (Ridge Regression)
  • Decision Trees
  • Random Forest
  • ANN (Artificial Neural Network)