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AI Scientist
This track provides hands-on application of Supervised, Unsupervised & Ensemble Machine Learning, Text Analytics, NLP, XAI, Deep Learning and the concepts of Network Flow, Optimization & Operation Research along with Python Programming for AI.
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28 Courses
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23 Projects & Case Studies
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Difficulty: Advanced
Prior knowledge or professional experience is highly recommended
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Duration: Approximately 1 Year
Suggested learning pace is 5hr/week
Course Overview
- Learn the concepts and various techniques for Supervised Learning, Unsupervised Learning, Ensemble Learning, and Text Analytics and their Python implementation.
- Learn about the various optimization problems and mathematical techniques to solve them like linear programming and mixed-integer programming, along with a detailed study of specialized optimization problems like Knapsack, Travelling Salesman and Assignment problem.
- Learn the concepts and techniques of Explainable AI like LIME, SHAP and apply them to perform detailed model evaluation and comparison and understand the predictions of different models.
- 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
Lifetime Access
Access this learning track 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
- Machine Learning – Logistic Regression
- Logistic Regression in Python
- Getting Started with Naive Bayes Classifier
- Naive Bayes in Python
- Support Vector Machines in ML
- Support Vector Machines in Python
- Understanding Decision Trees
- Tree Models in Python
- Concepts and Application of Objective and Subjective Segmentation
- Clustering algorithms in Python
- Understanding Principal Component Analysis (PCA)
- PCA in Python
- Introduction to Optimization Techniques
- Optimization Techniques – Network Flow Problem
- Optimization Techniques – Specialized Optimization Problems
- Introduction to Natural Language Processing (NLP)
- Mining Text Data Cleansing, Treatment, Structural Representation & Visualization
- Text Analytics – Classification and Clustering
- Sentiment Analysis – Using Unstructured Text Data
- Introduction to Explainable AI (XAI) using LIME
- Introduction to Explainable AI (XAI) using SHAP
- Introduction to Explainable AI (XAI) for Text using LIME & SHAP
- Introduction to Neural Networks
- Artificial Neural Networks in Python
- Convolutional Neural Networks in Python
- Recurrent Neural Networks in Python
- Applying Linear Regression to predict used car prices
- Predict Holiday Sales for A Retail Client – Application of Linear Regression
- Assumptions in OLS Regression Models (Ordinary Least Squares)
- Predicting Heart Disease using Logistic Regression
- Using Naive 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
- Identify risk class and eligibility of a customer: Application of Machine Learning
- Predicting the Survival of patients with Hepatocellular carcinoma (HCC)
- Grouping the Driving Styles based on Telematics Data
- Application of Non-Hierarchical Clustering in HR Analytics Domain
- Segmentation on Conversion of Insurance Leads
- Exploratory Data Analysis On Spam Text Classification
- Sentiment Analysis on Car Reviews
- Application of Text Classification on Women’s E-Commerce Clothing Reviews
- Predictive Modelling for Fake News Detection Using NLP Techniques
- Sentiment Analysis on Movies Review Data – Application of Text Analytics
- Predicting Car Price using XAI
- Credit Risk Modelling – Probability of Default: Model Comparison & XAI
- Predicting Income Using Adult Census Data – Supervised ML Model Comparison & Explainable AI
- 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, resume, and LinkedIn as you showcase your skills and project experience to employers and colleagues.
- Evaluate and select an appropriate supervised, unsupervised or ensemble machine learning technique(s) for predictive modelling on real world data to generate business insights.
- Get a comprehensive understanding of explainable AI methods, its importance and apply the technique of LIME and SHAP to perform detailed model comparison and understand the predictions of different models.
- Understand and apply a variety of optimization techniques like Linear Programming, and Mixed-Integer Programming to solve specialized problems and generate business insights.
- Interpret and visualize the outcome of the various applied techniques.
- Understand and apply a wide variety of neural network models along with their theoretical details and implement advanced CNN and RNN models to perform predictive modelling on image and sequential data
“Rolai provides contextual upskilling opportunities … on one single platform.”
“An excellent tool for anyone who wants to quickly learn the ropes.”
“Rolai is the best program available for someone looking to enhance their skills”
“Great way to learn data analytics and data science”
“The courses were excellent and covered topics that I didn’t expect”
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.
