Surendra Sharma

Surendra Sharma

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Showing posts with label AI. Show all posts
Showing posts with label AI. Show all posts

Thursday, June 13, 2019

Week of AI

In May 2019, Microsoft India scheduled "Week of AI" virtual workshop series for 5 days. I have attended all the sessions and participated in all the quiz series. Happy to share that today I got my Microsoft "Week of AI" Azure Ninja Cat badge.


Sunday, March 3, 2019

Course : Ethics and Law in Data and Analytics


Finished the course "Ethics and Law in Data and Analytics" successfully on EDX offered by Microsoft by 92%.


Ethics and Law in Data and Analytics
Ethics and Law in Data and Analytics

I study below modules in this course
  • Data, Ethics, and Law
  • Data, Individuals, and Society
  • Data Ethics and Law in Business
  • Artificial Intelligence and Future Opportunities

Sunday, November 25, 2018

Course : Reinforcement Learning Explained

Finished the course "Reinforcement Learning Explained" successfully on EDX offered by Microsoft by 78%.

Reinforcement Learning Explained Score
Reinforcement Learning Explained Score


I study below modules in this course

  • Introduction to Reinforcement Learning
  • Bandits
  • The Reinforcement Learning Problem
  • Dynamic Programming
  • Temporal Difference Learning
  • Function Approximation
  • Policy Gradient and Actor Critic

Saturday, October 27, 2018

Course : Data Science Essentials

Finished the course "Data Science Essentials" successfully on EDX offered by Microsoft by 98%.

Data Science Essentials
Data Science Essentials
I study below modules in this course
  • Module 1: Introduction to Data Science
  • Module 2: Probability and Statistics for Data Science
  • Module 3: Simulation and Hypothesis Testing
  • Module 4: Exploring and Visualizing Data
  • Module 5: Data Cleansing and Manipulation
  • Module 6: Introduction to Machine Learning

Monday, October 15, 2018

Course : Deep Learning Explained

Finished the course "Deep Learning Explained" successfully on EDX offered by Microsoft by 80%.


Deep Learning Explained score
Deep Learning Explained score

I study below modules in this course
  • 1 | Introduction and Overview
  • 2 | Multi-class Classification using Logistic Regression
  • 3 | Multi-Layer Perceptron
  • 4 | Convolution Neural Network
  • 5 | Recurrent Neural Network and Long Short Term Memory
  • 6 | Text Classification with RNN and LSTM

Monday, September 17, 2018

Course : Essential Mathematics for Artificial Intelligence

Finished the course "Essential Mathematics for Artificial Intelligence" successfully on EDX offered by Microsoft by 92%.

Essential Mathematics for Artificial Intelligence
Essential Mathematics for Artificial Intelligence


I study below modules in this course

  • Equations, Graphs, and Functions
  • Derivatives and Optimization
  • Vectors and Matrices
  • Statistics and Probability

Sunday, September 2, 2018

Course : Introduction to Python for Data Science

Finished the course "Introduction to Python for Data Science" successfully on EDX offered by Microsoft by 98%.

Introduction to Python for Data Science
Introduction to Python for Data Science

I study below modules in this course
1. Python Basics
2. List - A Data Structure
3. Functions and Packages
4. Numpy
5. Plotting with Matplotlib
6. Control Flow and Pandas
7. Final Exam and Course Wrap-up

Thursday, August 23, 2018

Course : Introduction to Artificial Intelligence (AI)

Finished the course "Introduction to Artificial Intelligence (AI)" successfully on EDX offered by Microsoft by 87%.

 Introduction to Artificial Intelligence (AI)
 Introduction to Artificial Intelligence (AI)

I study below modules in this course

  • Introduction
  • Machine Learning
  • Language and Communication
  • Computer Vision
  • Conversation as a Platform

Sunday, August 19, 2018

Course : Principles of Machine Learning


Finished the course "Principles of Machine Learning" successfully on EDX offered by Microsoft by 94%.


Principles of Machine Learning
Principles of Machine Learning

I study below modules in this course

  • Module 1: Classification
  • Module 2: Regression
  • Module 3: Improving Machine Learning Models
  • Module 4: Tree and Ensemble Methods
  • Module 5: Optimization-Based Methods
  • Module 6: Clustering and Recommenders