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.
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)