Machine-Learning Training



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Target Audience

10th & 12th class students Undergraduates, Graduates Post-Graduates & Job aspirants

Pre-requisites

C & OOPS Concepts would be an advantage

Duration

45 days Duration, Classes taken 5 Days a week


Batches

5 day in a week, Weekend batch, Only Sunday batch

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Fee Structure

Starting from 5000/-


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  1. Introduction: What is Machine Learning? What is Artificial Intelligence?
  2. Landscape of problems
    1. Supervised versus unsupervised learning
    2. Classification versus forecasting
    3. Classifying data sets: Tall, wide, and dense data
    4. Predictive modeling/ policy intervention
  3. Python basics
    1. Anaconda
    2. Spyder
    3. Numpy, Scipy
    4. Matplotlib
    5. Scikit Learn
  4. Model complexity
    1. Overfitting
    2. Training/validation/testing
  5. Simplest models
    1. Getting started with scikit-Learn
    2. K nearest neighbors
    3. Application: Iris data
    4. Linear regression
  6. Controlling model complexity
    1. Regularization
    2. Ridge regression
    3. Lasso regression
  7. Classification
    1. Logistic regression
    2. Support vectors
    3. Naive Bayes
    4. Linear discriminant
    5. Multi(k>2) class problems
  8. Decision trees
    1. Controlling complexity again
    2. Feature importance and reading trees
    3. Bagging predictors
    4. Multiple trees (The Random Forest)
    5. Boosting
  9. Recommendation systems
    1. Content based recommendation
    2. Collaborative filtering
  10. Clustering
    1. K-means and Hierarchical clustering
    2. Principal component analysis
  11. Model evaluation
    1. Cross validation
    2. Evaluation metrics
    3. Tuning Models