Saswat Mohanty

Years of Experience in Embedded & VLSI Classes

5

Years of Experience in Embedded C Training

5

Years of Experience in Embedded Systems Training

5

Teaching Experience in detail in Embedded Systems Training

Basics of embedded systems, peripherals of an embedded system, SPI, I2C, UART, RTOS Basics, Basics of Porting, Hands on project.

Years of Experience in C Language Classes

5

Years of Experience in Python Training classes

7

Years of Experience in C++ Language Classes

5

Years of Experience in Data Science Classes

5

Teaching Experience in detail in Data Science Classes

Advanced Python and Data Science: The course includes thorough coverage of Learning Algorithms, Terminology, Data Types, Data Visualization. The course takes a practical approach to creating and organizing Linear Regression ,Binary Classification ,Multiclass Classification. Attendees will learn how to use Python to create scripts that do Linear Regression , Binary Classification, Multiclass Classification, Data Visualization - Linear, Log, Quadratic and More. Comprehensive hands on exercises are integrated throughout to reinforce learning and develop real competency. Students Will Learn Linear Regression, Linear Model, Squared Error Loss Function, Stochastic Gradient Descent Linear Regression for complex shapes, AWS - Linear Regression Models How to evaluate regression model accuracy ,Evaluate predictive quality of the trained model, Review Default Recipe Settings Used To Train model ,Train Model With Custom Recipe and Review Performance Adding Features To Improve Model Normalization, Impact of Features With Different Magnitude, Normalization to smoothen magnitude differences, Train Model With Feature Normalizaton Adding Complex Features, Logistic Regression, Binary Classification - Logistic Regression, Loss Function, Optimization, Binary Classification Approach, True Positive, True Negative, False Positive and False Negative Data Transformation using Recipes, Text Transformation, Numeric Transformation - Quantile Binning, Numeric Transformation – Normalization, Cartesian Product Transformation - Categorical and Text Hyper Parameters, Model Optimization and Lifecycle, Data Rearrangement, Maximum Model Size, Passes, Shuffle Type, Regularization, Learning Rate, Regularization Effect

Years of Experience in Amazon Web Services Training

5

Teaching Experience in detail in Amazon Web Services Training

AWS Solutions architect Hands on Training Provided.