Start Introductory Intermediate Projects Intern Activities

BigData and Database

BigData and Database are inherently connected to each other. There are lots of BigData tools, for example, Hadoop Ecosystem( HDFS, YARN, MapReduce, Spark, PIG, HIVE, etc.), Elasticsearch(Kibana, Logstash), Cassandra, etc. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

Lectures: Database Management Essentials | Managing Big Data with MySQL | Big Data Specialization ( 5 courses) | Data Science and Engineering with Spark (Edx-Xseries) | NodeJS | MangoDB

Tutorial : Apache Hadoop | Elasticsearch (Indexing and Search)

Advanced Algorithm and AI

Advanced search algorithms are part of AI for examples Depth First Search(DFS), Breadth First Search(BFS), Alpha-Beta Pruning, MiniMax search, A* Search, Simulated Annealing, and other advanced graph and tree algorithms.

Lectures: Artificial Intelligence (AI) | Data Structures and Algorithms Specialization (6 courses) | Graph Search, Shortest Paths, and Data Structures | Algorithm-I | Algorithm-II

Bayesian Learning

Bayesian Learning is extended versions of algorithms from Bayes theory in statistics. There are more advanced algorithms, for example, a Markov Model, Hidden Markov model, Markov Random Field, Conditional Random Field, etc. There are a variety of Bayesian learning-based algorithms in 'Robotics', 'Self Driving Car' and 'Flying Car' designing. Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

Lectures : Bayesian Methods for Machine Learning | Probabilistic Graphical Models Specialization - Representation, Inference and Learning ( 3 courses)

Deep Learning

Deep Learning is the most celebrated algorithm in Machine Learning. It has a variety of neural network architectures, for example, CNN, RNN, LSTM, etc. Using transfer learning, one can utilize the prebuild neural network for a new problem.

Lectures: Deep Learning Specialization ( 5 courses) | Deep Learning Explained | Deep Learning with Tensorflow | Deep Learning Fundamentals with Keras | Using GPUs to Scale and Speed-up Deep Learning | Applied AI with DeepLearning An Introduction to Practical Deep Learning

Tutorials: Neural Networks and Deep Learning

Cheat Sheets: Neural Networks

Reinforcement Learning

In reinforcement learning, an agent learns from environment based on reward and punishment policy. The environment is typically formulated as a Markov Decision Process (MDP) or Dynamic Programming. There is more advanced technique called Deep Reinforcement Leraning. Lectures : Reinforcement Learning Explained | Practical Reinforcement Learning | Deep Reinforcement Learning

Network and Complex System

Network and Complex system is very applicable in social interaction and economics. There are very interesting machine learning algorithm based on 'Tree' and 'Graph' that could be applied to harness the complex networks.

Lectures: Applied Social Network Analysis in Python | Social and Economic Networks: Models and Analysis | Network Dynamics of Social Behavior | Graph Algorithms | Emergent Phenomena in Science and Everyday Life


A blockchain, is currently evolving technology. Block chain, in frist principle, is a growing list of records, called blocks, which are linked using cryptography.Blockchains which are readable by the public are widely used by cryptocurrencies.

Lectures: Blockchain Specialization (4 courses) | Bitcoin and Cryptocurrency Technologies | IBM Blockchain Foundation for Developers | Blockchain Technology | Blockchain: Understanding Its Uses and Implications