Objective

The Most Coveted Certification in the Data Science domain, SK-CDS is also the Industry’s most trusted credential for Data Scientist’s. This comprehensive Data Scientist course covers the 360 (Degree) Machine Learning topics along with real time case studies and supervised practical sessions.

Why SK-CDS

  • Overview of Machine Learning & It’s applications
  • Supervised Vs Unsupervised Vs Reinforcement learning
  • Mathematical Understanding of Algorithms
  • Develop machine learning models and learn parameter tuning
  • Exposure to Scikit-learn library

Pre requisites

Data Analysis with Python or SK – CDA Certification

Who should attend

This course is designed for the aspiring Data Analyst, Data Scientist, ML engineers, Deep Learning Engineers


Module 01: Introduction to Machine Learning

  • Basic Intuition
  • Supervised Vs Unsupervised Vs Reinforcement
  • Regression & Classification

Module 02: Regression

  • Introduction
  • Simple, Linear & Non-linear Regression
  • Model Evaluation
  • Evaluation Metrics for Regression

Module 03: Classification

  • Introduction
  • Logistic Regression
  • Training Logistic Regression
  • Evaluation Metrics for Classification

Module 04: Practical Tips

  • Problems of Overfitting & Underfitting
  • Regularization
  • Cross Validation Methods
  • Problems of Imbalanced Class
  • Resampling Methods

Module 05: More on Classification

  • K-Nearest Neighbour
  • Decision Trees, Random Forest & Other Tree based Algorithms
  • Support Vector Machines
  • Neural Networks

Module 06: Clustering

  • Introduction
  • K-means
  • Introduction to Hierarchical Clustering

Module 07: Recommender Systems

  • Content Based Recommender Systems
  • Collaborative Filtering

Module 08: Time Series

  • Introduction
  • Reading, Plotting Time Series
  • Decomposition of Time Series
  • Smoothening
  • ARIMA Models

Module 09: Project

Capstone Projects



40 hours of instructor led training


Contact Us

+91 93 848408 00

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Module 01: Introduction to Machine Learning

  • Basic Intuition
  • Supervised Vs Unsupervised Vs Reinforcement
  • Regression & Classification

Module 02: Regression

  • Introduction
  • Simple, Linear & Non-linear Regression
  • Model Evaluation
  • Evaluation Metrics for Regression

Module 03: Classification

  • Introduction
  • Logistic Regression
  • Training Logistic Regression
  • Evaluation Metrics for Classification

Module 04: Practical Tips

  • Problems of Overfitting & Underfitting
  • Regularization
  • Cross Validation Methods
  • Problems of Imbalanced Class
  • Resampling Methods

Module 05: More on Classification

  • K-Nearest Neighbour
  • Decision Trees, Random Forest & Other Tree based Algorithms
  • Support Vector Machines
  • Neural Networks

Module 06: Clustering

  • Introduction
  • K-means
  • Introduction to Hierarchical Clustering

Module 07: Recommender Systems

  • Content Based Recommender Systems
  • Collaborative Filtering

Module 08: Time Series

  • Introduction
  • Reading, Plotting Time Series
  • Decomposition of Time Series
  • Smoothening
  • ARIMA Models

Module 09: Project

Capstone Projects

40 hours of instructor led training

Training Schedules

10th November 2018

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Course Curriculum

Introduction to Software Training Details FREE 00:40:00
Object Oriented Design Patterns Details 00:35:00
Software Testing Details 00:30:00
Advanced Database Development Details 00:25:00
Algorithm analysis Details 00:45:00
Multi Threading in Softwares Details 00:40:00
Managing Software Testing Details 00:20:00
The Software Quiz 00:04:00

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Contact Us

+91 93 848408 00


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