Machine Learning in R

Day 0 – Why Machine Learning

• Join the revolution
• R vs Python
• Machine Learning Demo
• Traditional Programming vs ML
• Machine Learning Path
• Course Requirements
• Beginner’s FAQ

Day 1 – Just enough R

• Environment Setup
  • R Setup
  • Hello world in R
  • R studio setup
  • Source Code
• R Basics
  • Number guessing game
  • Average grade Program
  • Prime number Program
  • Reverse string program
  • Date and time
• Where are Python libraries
• Number guessing game – Random
• Challenges

Day 2 – Data Structures

• Python Lists
• Python Dictionary
• Python Sets
• Python Tuples
• Challenges

Day 3 – NumPy

• Just enough NumPy
  • n-dimensional Array
  • Array Operations
  • Array Indexing and Slicing
  • Challenges
• Additional Reading
  • meshgrid

Day 4 – Pandas

• Just enough Pandas
  • What is Pandas
  • Dataframe
    • Create Dataframe
    • Display Dataframe
    • Select data from Dataframe
    • Add/delete Rows
    • Add/delete Columns
    • Grouping data
    • Merging Dataframes

Day 5 – Visualization

• Just enough Matplotlib
  • What is Matplotlib
  • Plot Object
  • Lifecycle of a Plot
  • More Plots

Day 6 – Linear Algebra

• Just enough Linear Algebra
  • Linear Algebra Basics
  • Dot Product
  • Miscellaneous Topics

Day 7 – Probability & Statistics

• Just enough Probability & Statistics
  • Key Terms
  • What is a Distribution
  • Central Limit Theorem
  • Correlation

Day 8 – Introduction to Regression

• Linear Regression
• Multi-linear Regression
• Correlation
• Hypothesis Tests ( p-value )
• Key Parameters
  • r-squared
  • r-squared adjusted
  • RMSE
• Feature Selection
• Polynomial Regression
• Over fitting
• Assumptions

Day 9 – Introduction to Classification

• What is Classification
• K – Nearest Neighbors
• KNN in Python
  • Confusion Matrix
• KNN Regression
• Feature Scaling
• Effect of Outliers
• Parameter Tuning
• Key features of KNN
• Assumptions

Day 10 – Machine Learning Lifecycle

• What is ML Lifecycle
• Data Ingestion
  • Data Import
  • Data Preprocessing
  • Imputation of Missing Values
• Data Modeling
  • Model Accuracy
  • Validation

Day 11 – Logistic Regression

• What is Logistic Regression
• Math behind Logistic Regression
• Implementation
• Optimization
• Evaluation
  • ROC Curve
  • Area under ROC Curve

Day 12 –Naive Bayes

• What is Naive Bayes
• Bayes Theorem & Conditional Probability
• Naive Bayes Theorem
• Naive Bayes by hand
  • Classify fruits
• Classify Messages as Spam or Ham
• Naive Bayes on Continuous Variables

Day 13 – Support Vector Machine – SVM

• What is SVM
• Support Vectors
• Kernels
• Hyperplane
• Performance Tuning
  • cost
  • kernel
  • gamma
• SVM for Regression

Day 14 – Decision Trees

• What are Decision Trees
• Implementation
• Visualization
• Gini Index
• Decision Trees for Regression
• Overfitting
• Pruning
  • Tree Depth
  • Impurity

Day 15 – Ensemble Methods

• What is an Ensemble
• Types of Ensemble Methods
• Bagging Implementation
  • Bagging
  • Random Forest
• Boosting Implementation
  • AdaBoost
  • Gradient Boost

Day 16 – Clustering

• What is Clustering
• K-means Clustering
  • Optimum k value

Day 17 – Association Rule Learning

• What is Associative Rule Learning
• Key Terms
  • Support
  • Confidence
  • Lift
• Apriori Algorithm

Day 18 – Dimensionality Reduction

• What is Dimensionality Reduction
  • Hughes Phenomonon
  • Curse of Dimensionality
  • The Solution
• Principal Component Analysis
  • Mean & Variance
  • Eigen Vectors & Eigen Values

Day 19 – Just enough SQL

• SQL
  • What is a database
  • What is SQL
• SQL Connectors
• Load data
• Selecting data
  • SELECT Statement
  • Aggregate Functions
  • Table JOINs