Machine Learning Training

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Machine Learning Training


 (4.8) | 350 Ratings


Introduction


Machine Learning Training Details
Track Regular Track Weekend Track Fast Track
Course Duration 30 Hrs 8 Weekends 5 Days
Hours 1hr/day 2 Hours a day 6 Hours a day
Training Mode Online Classroom Online Classroom Online Classroom
Delivery Instructor Led-Live Instructor Led-Live Instructor Led-Live


Course Curriculum

Tools Covered

  • R
  • Python
  • Jupyter
  • Spark
  • H2O
  • AzureML

Pre-requisites

  • Understanding of data science
  • Passion for new technologies

What you will learn

  • Machine Learning Tools In demand by MNCs
  • Machine Learning Methods with real world case studies

Unit 1 : Machine Learning Model Building

  • Machine Learning Foundation
  • Hypothesis Testing & P values
  • Statistical Tests in R
  • Sampling Methods & CI
  • Feature Engineering Methods
  • Dimensionality Reduction
  • Overfitting & Under-fitting
  • Bias & Variance Tradeoff
  • Dataset Sampling & Partitions
  • Computational Limitations
  • Model Building Steps in R
  • Model Building Steps in Python

Unit 2: Predictive Analytics

  • Data Exploration Case Study
  • Linear Regression
  • Linear Model Fits & Evaluation Metrics
  • Non Linear Regression
  • L1 & L2 Regularization
  • Classification Methods Overview
  • Accuracy,Confusion Matrix & ROC curve
  • Logistic Regression
  • Logistic Model Fits & Evaluation Metrics
  • Decision Trees & Rule Learners
  • Trees Model Fits & Evaluation Metrics
  • Naïve Bayes Classifier Model
  • KNN Classifier Model
  • Model Selection Parameters
  • Case Study 1 : Classification
  • Case Study 2 : Trees
  • Case Study 3 : Regression

Unit 3: ML Advanced: Black Box & Ensemble

  • Support Vector Machines
  • Neural Networks
  • ML Ensemble Methods
  • Bagging & Random Forest
  • Gradient Boosting Methods (GBM)
  • Case Study 4 : SVM
  • Case Study 5 : Random Forest
  • Case Study 6 : GBM

Unit 4: Unsuperwised Machine Learning

  • K-means Clustering
  • Hierarchical Clustering
  • Principle Component Analysis(PCA)
  • Feature Hashing

Unit 5: Machine Learning Tools

  • Hadoop Architecture for big data
  • R & h2o
  • Python SciKit Learn
  • Spark Architecture
  • Spark ML and PySpark
  • Spark ML and Sparklyr
  • Microsoft AzureML
  • Working with Azure Cloud
  • Project selection on real life case study
  • Best Practices & Project progress discussion

Unit 6: Machine Learning Using Python – I and II

Machine Learning Using Python - I

  • In this module, train over
  • Introduction to Machine Learning
  • Areas of Implementation of Machine Learning
  • Why Python
  • Major Classes of Learning Algorithms
  • Supervised vs Unsupervised Learning
  • Learning NumPy
  • Learning Scipy
  • Basic plotting using Matplotlib
  • We’ll also build a small Machine Learning application and discuss the different steps involved while building an application.

Machine Learning Using Python - II

  • In this module, learn how to create data Frames, Clustering with k-means in python, Plotting data, Pandas, Creation of Functions.

Unit 7: Job Interview Essentials

  • CV Preparation
  • Effective selling of CV in job sites
  • Soft Skills for Data Scientist
  • Project: Final presentation
  • Executive presence for Interview

 

Exam & Certification

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Course Curriculum

Tools Covered

  • R
  • Python
  • Jupyter
  • Spark
  • H2O
  • AzureML

Pre-requisites

  • Understanding of data science
  • Passion for new technologies

What you will learn

  • Machine Learning Tools In demand by MNCs
  • Machine Learning Methods with real world case studies

Unit 1 : Machine Learning Model Building

  • Machine Learning Foundation
  • Hypothesis Testing & P values
  • Statistical Tests in R
  • Sampling Methods & CI
  • Feature Engineering Methods
  • Dimensionality Reduction
  • Overfitting & Under-fitting
  • Bias & Variance Tradeoff
  • Dataset Sampling & Partitions
  • Computational Limitations
  • Model Building Steps in R
  • Model Building Steps in Python

Unit 2: Predictive Analytics

  • Data Exploration Case Study
  • Linear Regression
  • Linear Model Fits & Evaluation Metrics
  • Non Linear Regression
  • L1 & L2 Regularization
  • Classification Methods Overview
  • Accuracy,Confusion Matrix & ROC curve
  • Logistic Regression
  • Logistic Model Fits & Evaluation Metrics
  • Decision Trees & Rule Learners
  • Trees Model Fits & Evaluation Metrics
  • Naïve Bayes Classifier Model
  • KNN Classifier Model
  • Model Selection Parameters
  • Case Study 1 : Classification
  • Case Study 2 : Trees
  • Case Study 3 : Regression

Unit 3: ML Advanced: Black Box & Ensemble

  • Support Vector Machines
  • Neural Networks
  • ML Ensemble Methods
  • Bagging & Random Forest
  • Gradient Boosting Methods (GBM)
  • Case Study 4 : SVM
  • Case Study 5 : Random Forest
  • Case Study 6 : GBM

Unit 4: Unsuperwised Machine Learning

  • K-means Clustering
  • Hierarchical Clustering
  • Principle Component Analysis(PCA)
  • Feature Hashing

Unit 5: Machine Learning Tools

  • Hadoop Architecture for big data
  • R & h2o
  • Python SciKit Learn
  • Spark Architecture
  • Spark ML and PySpark
  • Spark ML and Sparklyr
  • Microsoft AzureML
  • Working with Azure Cloud
  • Project selection on real life case study
  • Best Practices & Project progress discussion

Unit 6: Machine Learning Using Python – I and II

Machine Learning Using Python - I

  • In this module, train over
  • Introduction to Machine Learning
  • Areas of Implementation of Machine Learning
  • Why Python
  • Major Classes of Learning Algorithms
  • Supervised vs Unsupervised Learning
  • Learning NumPy
  • Learning Scipy
  • Basic plotting using Matplotlib
  • We’ll also build a small Machine Learning application and discuss the different steps involved while building an application.

Machine Learning Using Python - II

  • In this module, learn how to create data Frames, Clustering with k-means in python, Plotting data, Pandas, Creation of Functions.

Unit 7: Job Interview Essentials

  • CV Preparation
  • Effective selling of CV in job sites
  • Soft Skills for Data Scientist
  • Project: Final presentation
  • Executive presence for Interview

 

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