• Introduction to Data Science
  • High level view of Data Science, Artificial Intelligence & Machine Learning
  • Subtle differences between Data Science, Machine Learning & Artificial Intelligence
  • Approaches to Machine Learning
  • Terms & Terminologies of Data Science
  • Understanding an end to end Data Science Pipeline, Implementation cycle
  • Linear Algebra
  • Matrices, Matrix Operations
  • Eigen Values, Eigen Vectors
  • Scalar, Vector and Tensors
  • Prior and Posterior Probability
  • Calculus
  • Differentiation, Gradient and Cost Functions
  • Types of Data (Discrete vs Continuous)
  • Types of Data (Nominal, Ordinal)
  • Measures of Central Tendency (Mean, Median, Mode)
  • Range, Quartiles, Inter Quartile Ranges
  • Random Variables
  • Statistical sampling & Inference
  • t-value and p-value
  • Confidence Intervals
  • Numpy
  • Pandas
  • Matplotlib & Seaborn
  • Jupyter Notebook
  • Data Acquisition
  • Data Preparation
  • Data cleaning
  • Data Visualization
  • Plotting
  • Model Planning & Model Building
  • Selection and Removal of Columns
  • Transform
  • Rescale
  • Standardize
  • Train, Test Splitting
  • Tensorflow & keras installation
  • Hidden Markov Models (HMM)
  • Deep Learning with Convolutional Neural Nets
  • Amazon Web Services Preliminaries - S3, EC2, RDS
  • Big data processing on AWS using Elastic Map Reduce (EMR)
  • Machine Learning using Amazon Sage Maker
  • Deep Learning on AWS Cloud
  • Natural Language processing using AWS Lex
  • Analytics services on AWS Cloud
  • Data Warehousing on AWS Cloud
  • Creating Data Pipelines on AWS Cloud
  • Tasks in Data Science Development
  • Deploying Models in Production
  • Deploying Machine Learning Models as Services
  • Running Machine Learning Services in Containers
  • Scaling ML Services with Kubernetes
Data Science Training in Hyderabad

Preview this course

  • Duration 45 Hours
  • Students 21
  • Days 40 Days
  • Resume Preparation Yes
  • Interview Guidance Yes