Machine Learning Scientist

This track provides hands-on application of the techniques for Data handling, Data Transformations, Data Visualizations, Predictive Model Development, Evaluation & Selection, Supervised, Unsupervised & Ensemble Machine Learning, Deep Learning Concepts and Python Programming for Machine Learning using a variety of use cases.

  • icons final-02 33 Courses
  • icons final-03 19 Projects & Case Studies
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    Difficulty: Advanced

    Prior knowledge or professional experience is highly recommended

  • Asset 1
    Duration: Approximately 12 months

    Suggested learning pace is 5hr/week

Track Overview

  • Learn various Data Mining and Feature Engineering techniques to extract meaningful patterns from a dataset and transform the raw dataset into a suitable format which leads to improved modeling
  • Learn the fundamental concepts associated with dimensionality reduction, its significance, different techniques and develop a working understanding of PCA to deal with the high dimensionality of a dataset.
  • Learn the concepts and various techniques of Supervised Machine Learning, Unsupervised Machine Learning and Ensemble Learning focussing on building an ML model, Hyperparameter optimization, Model Evaluation and Selection.
  • Gain a comprehensive overview of Deep Learning starting with the fundamental concepts of Neural Networks and Perceptron model,  different architectures, optimization, and regularization techniques and understand mathematical details of advanced CNN and RNN models along with their applications on image and sequential data.

What’s included


Shareable Certificate

Earn a sharable certificate upon completion


Lifetime Access

Access this case study for life once completed


Flexible Scheduling

Start learning online immediately, at your own pace


Desktop Only

We recommend completing this learning track on a desktop

Skills You Will Learn

Data Mining

Feature Engineering

Data Preprocessing

Predictive Modeling

Dimensionality Reduction


Model Building

Model Evaluation and Selection

Hyperparameter Tuning

Ensemble Techniques

Bagging and Boosting

Deep Learning

Linear Programming

Mixed-Integer Programming

Linear Fractional Programming

Text Preprocessing


Neural Networks

Logistic Regression

Naive Bayes Algorithm


Linear Regression

Tree-Based Models


  • Data Mining Concepts and Techniques
  • Feature Engineering Techniques
  • Advanced Feature Engineering Techniques
  • Simple Linear Regression using Python
  • Multiple Linear Regression using Python
  • Regularized Linear Models
  • Logistic Regression in Python
  • Naive Bayes in Python
  • Support Vector Machines in Python
  • PCA in Python
  • Tree Models in Python
  • Clustering Algorithms in Python
  • Machine Learning – Linear Regression
  • Model Evaluation Techniques
  • Model Selection Techniques
  • Machine Learning – Logistic Regression
  • Getting Started with Naive Bayes Classifier
  • Support Vector Machines in ML
  • Understanding Decision Trees
  • Understanding Principal Component Analysis
  • Concepts and Application of Objective and Subjective Segmentation
  • Introduction to Neural Networks
  • Artificial Neural Networks in Python
  • Recurrent Neural Networks in Python
  • Convolutional Neural Networks in Python

  • Cleaning & treating – HR attrition case study
  • Data preprocessing on used cars data
  • Analyzing and Predicting the Productivity Performance of Manufacturing Industry Workers
  • Applying Linear Regression to predict used car prices
  • Loan Amount prediction for different applications using Regression Techniques
  • Predicting failure of Power Transformers in a Manufacturing plant using Regression & Performing Root Cause Analysis
  • Predicting Heart Disease with Logistic Regression
  • Using Naïve Bayes Classifier To Predict Water Potability
  • Applying Support Vector Machine (SVM) Classifier To Predict The Drug Type
  • Detection of Breast Cancer in A Clinical Trial – Application Of SVM
  • Build a Regression Tree for Predicting Spend on Credit Card
  • Predicting Traffic, Driving Style & Road Surface Condition By Applying Advanced Classification Techniques
  • Estimating Price for Diamonds – Supervised Learning -Hyperparameter Optimisation
  • Grouping the Driving Styles based on Telematics Data
  • Clustering on Locations Services from Vehicle Telematics Data for Service Center Location Allocation
  • Application of various Clustering techniques to group the Steel type based on its Mechanical properties
  • Predicting Credit Card Spend – Artificial Neural Network
  • Predicting Customer Churn – Artificial Neural Network

How it Works

Learn new skills that will boost your career by enrolling in courses across data analytics, data science, ML and AI. These courses will utilize readings, videos, quizzes, data cases, and even coding exercises to teach you skills and concepts in a way that will solidify your new knowledge for hands-on application.

With our hands-on projects, you will take your newly learned skills along with our 750+ low-code/no-code functions and embedded coding console to complete milestone-based projects. Once completed, you will have effectively applied new skills and concepts to real-world data cases that can be translated directly into your career.

Complete assessments and track your progress in real-time to benchmark your proficiency in relation to key functional areas. As you progress through your courses, our patented platform will utilize ML and AI to record and analyze your inputs and output to provide active feedback and recommendations that will help you learn more effectively than the standard Letter Grade system used today.

Complete courses to earn sharable certificates and badges. These awarded items will be look great in your portfolio as you showcase your skills and project experience to employers and colleagues.

Learner Outcomes

Complete learning tracks to earn shareable certificates and badges. These awarded items will look great in your portfolio as you showcase your skills, projects, and experience to employers and colleagues.

  • The learner will be able to understand and apply different tools and techniques for data management, transformations, pre-processing, visualization, feature engineering and dimensionality reduction.
  • The learner will be able to evaluate and select an appropriate supervised, unsupervised or ensemble machine learning technique(s) for predictive modeling.
  • The learner will be able to understand, build and apply Neural Network models including advanced CNN and RNN models to perform predictive modeling on image and sequential data.
  • The learner will be able to interpret and visualize the outcome of the various applied techniques.

“Rolai provides contextual upskilling opportunities … on one single platform.”

Sundar Ramamoorthy
Managing Director of Solutions.AI, Global Products & Delivery Lead at Accenture

“An excellent tool for anyone who wants to quickly learn the ropes.”

Sanket Kawde
Head Data and Analytics at CitiBank India

“Rolai is the best program available for someone looking to enhance their skills”

Connor McEachron
Planning & Analytics @ Brooks Brothers

“Great way to learn data analytics and data science”

Balaji Reddy
Manager – Applications Development

“The courses were excellent and covered topics that I didn’t expect”

Aadarsha G
Student At Ohio Wesleyan University
All the Most Frequently Asked Questions

What People Are Asking About Data Education

Rolai’s patented process provides a personalized learning process for each user. Rolai goes deeper than simply learning concepts and testing your skills. At Rolai, learners can apply their skills to actual industry use cases and projects.

Our courses include readings, videos, quizzes, and hands-on data cases that are completed using our virtual lab; give learners an applied learning experience.

No additional tools are needed to begin learning with Rolai. Our virtual lab contains the necessary data workspace and an embedded coding console.

  • We have internal SMEs across industries and domains that we work with to develop relevant content and assure quality datasets and problem statements.
  • We also work with enterprises and universities to develop new content directed towards their industry and expertise.