Best AI,Ml training in Calicut,Kerala

List of topics covered

Fundamentals & Data Acquisition

To start, we will cover some mathematical underpinnings and tools required. Also basic introduction to obtain data from APIs and scraping data

  • Basic scraping and handling unstructured information
  • Statistical learning vs. scientific learning
  • High level overview of general techniques such as gradient descent, curve fitting etc.
  • Fundamental tools
    • Jupyter notebook, Numpy, Pandas, Matplotlib
    • Obtaining data sets and manipulating them – excel, csv, HTML, json, HDF5, SQL, NoSQL
  • Reading from Spark clusters. Pyspark integration

Data Analysis and Preprocessing

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

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

Shallow Learning (Supervised and Unsupervised)

Will cover common training methods and models from sklearn library and use it for regression and classification problems. Will cover clustering techniques using the sklearn library.

  • 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

Neural Networks

Introduction to deep learning techniques using keras library

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