AI & ML

24 Weeks March 2020

List of topics covered

Fundamentals & Data acquisition

Will cover some mathematical underpinnings and tools used during the course. Will cover various data sources and how to read out data from them and put it back. Also some basic introduction to obtaining data from APIs and scraping.

  • Statistical learning vs. scientific learning
  • High level overview of general techniques (gradient descent, curve fitting etc.)
  • Tool fundamentals
    • Jupyter notebook
    • Numpy, Pandas, Matplotlib
    • Obtaining data sets and manipulating them.
  • Data formats and sources – excel, csv, HTML, json, HDF5.
  • Reading from databases (SQL, NoSQL)
  • Pros and cons of various data formats
  • Reading from Spark clusters. Pyspark integration
  • Basic scraping and handling unstructured information.

Data analysis and preprocessing

Will cover how to obtain and inspect data to understand its quality. Will also cover how data can be cleaned up and augmented.

  • Studying data to discern insights
  • Transforming data to reveal patterns
  • Using visualisations and graphs to understand data trends
  • Data transforms to improve training quality and creating training features.

Shallow learning (supervised and unsupervised)

Will cover common training methods and models from the sklearn library used to regression and classification problems. Will cover clustering techniques using the sklearn library.
Also, some general sklearn techniques

  • Linear models and their pros and cons
  • Decision trees
  • Ensemble models (Random forests, Gradient boosted trees)
  • SVMs
  • Bayesian classifiers
  • Model evaluation, cross validation, and tuning.
  • Hyperparameter optimisation.
  • KNN
  • Agglomerative Clustering
  • DBScan
  • Evaluating clustering estimators
  • Saving and loading estimators

Neural Networks

Will provide an introduction to deep learning techniques using the keras library

  • Basic idea of Neural Networks
    • Layers, Activation functions, Interconnects
  • Simple layers, RNNs, CNNs
  • Evaluating Neural Networks

Our students work with

Chief Mentor

Noufal Ibrahim
B.Tech, NIT Calicut
  • 18+ years of industry experience and a well recognised technologist in India
  • Worked for archive.org, openlibrary.org, Synopsys, Cisco systems
  • Consulted for many startups ( Idea Device (acquired by Nutanix), TANDBERG (acquired by Cisco)
  • Founder of PyCon India and member of the Python Software Foundation
  • Mentor and trainer to freshers and working professionals (Cisco, VMware, Juniper Networks, Intel)