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https://www.eventshigh.com/detail/delhi/329cdd5dd2a0558634d23057320d1a07-machine-learning-bootcamp-2-days

Machine Learning Bootcamp (2 days) with Microsoft Azure ML - With AIGURU

0.0,0.0
Sat, 16 Nov 9:00AM - Sun, 17 Nov 6:00PM
To Be Decided
Rs 7999
575 people viewed this event.

Pricing & Offers

Pricing & Offers
Zero convenience fee, ticket price shown below is the final price.
Sat, 16 Nov
, 9:00AM
- Sun, 17 Nov
, 6:00PM
Per Person
Tea/Coffee & Lunch
₹ 7,999
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Details

Details

Join the race or be left behind!

Whether you want to be a data scientist or not, machine learning tomorrow will not be a differentiator, but a mandatory skillset.

You don't have to be an engineer, coder, or mathematician to learn the basics of data science.

In this 2 day Bootcamp, you'll be introduced to a GUI tool (Free) called MS Azure ML, which requires zero coding skills.

Within hours you will be building your own predictive models with huge data sets.

 

Pre-requisites:

  • Completed at least Class XII
  • Work ex preferred (but not mandatory)
  • Laptop with at least i3 configuration and 2 GB RAM (wifi connection will be provided).
  • No software required as Azure ML works on cloud
  • Enthusiasm to learn new things

Topics covered:

Introduction to Machine Learning

Types of ML Algorithms (Supervised/Unsupervised)

Regression

  • Linear Regression
  • OLS Regression
  • Multicollinearity
  • Bias Variance tradeoff
  • L1 and L2 Regularization

Classification

  • Logistic Regression
  • Decision Trees
  • Support Vector Machines

 Modeling Life Cycle

  • Importing data
  • Cleaning data
  • Splitting into Train/test
  • Training models
  • Performance metrics (Classification/Regression)
  • Confusion matrix
  • Scoring Models
  • Evaluating Models
  • Deploying Models

Ensemble Machine learning

  • Bagging
  • Boosting
  • Replicate
  • Random Forests

Data Processing

  • Normalization
  • Scaling
  • Handling Outliers

Advanced Modeling

  • Missing data imputation (MICE)
  • Handling Data Imbalance (SMOTE)
  • Dimension Reduction (PCA)
  • Hyperparameter Tuning (Grid Search, Random Sweep)
  • Gradient Descent Optimization

Clustering

  • The Distance concept (Euclidean Vs Manhattan)
  • K-Means Clustering

Case Studies & Practice Examples

  • Boston Housing Data set
  • Bank Telemarketing Prediction
  • Titanic Survival prediction
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Map & Directions

Map & Directions
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