Courses

Guided me through the basics of using Hadoop with MapReduce, Spark, Pig and Hive. I got experience on how one can perform predictive modeling and leverage graph analytics to model problems. This specialization prepared me to ask the right questions about data, communicate effectively with data scientists, and do basic exploration of large, complex datasets.

Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

Learned to create hybrid mobile applications, using the Ionic framework, Cordova and NativeScript. On the server side, I implemented NoSQL databases using MongoDB, worked within a Node.js environment and Express framework, and communicate to the client side through a RESTful API.

Introduction, architecture of clouds. Service, Data and Resource management of cloud. Cloud Security concerns. Hands on Open Source, commercial clouds and their simulators. Research areas in cloud computing and introduction to fog computing.

Introduction, basics of networking and communication protocols. Sensor Networks, Machine2Machine communication. Introduction to Arduino and Raspberry Pi programming. Introduction to SDN for IOT, Data handling and analytics. Fog computing, Smart cities, homes, grids and smart vehicles. IOT in Industries. Implementation on Agriculture, Healthcare and Activity Monitoring.

Learned launching instances in EC2(Elastic Cloud Compute). Adding EBS volumes to EC2 instances. Created and managed snapshots and security groups. Launching, creating and inserting data to DynamoDB. Creating S3(Simple Storage Service) bucket, uploading files to it and its versioning.

Learned basics of developing web and mobile applications. Developed the insight of the authentication, security and the best practices. Connection of backend to frontend and deploying the server for a webapp. Introduction to client-side and server-side javascript. Database concepts, its properties, security, backup and recovery. ACID properties of SQL, their analytics and views. Scaling up the app, introduction to popular network protocols and authentication with HTTP.

Learned classification for detection and ranking. Concepts of cross-entropy and hot labels in Multinomial Logistic Classifications. Creating, training, validating, testing and optimising the logistic classifiers and deep learning network. Implementing Rectified Linear Units(ReLU) in neural networks. Introduction to Stochastic Gradient Descent, Convolutional Neural Networks(CNN), Recurrent Neural Networks(RNN) and Long Short Term Memory(LSTM), Convolutional Networks(CovNets).