Advanced Machine Learning
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
Course Curriculum
Curriculum
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 Mahcines
- 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: Job Interview Essentials
- CV Preparation
- Effective selling of CV in job sites
- Soft Skills for Data Scientist
- Project: Final presentation
- Executive presence for Interview