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Start Machine Learning From Scratch Step By Step Guideline PDF Notes

Download free Machine Learning in PDF. This notes provide excellent case studies of a different techniques in machine learning. In this notes each chapter focus on a specific problem in machine learning, such as classification, prediction, optimisation and recommendation.

Using the R programming language, you’ll learn how to analyse sample datasets and write simple machine learning algorithms. This notes is ideal for programmers, from any background  including business, governments and academic research. 

Notes Machine Learning From Scratch
 Type PDF
Size 24 MB
Language  English 
For  Beginners To Advance 

You learn these topics from this notes:

You learn these topics in this notes:

1.Using R

  • R For Machine Learning
  • Downloading and Installing R
  • IDEs and Text Editors
  • Loading and Installing R packages

2. Data Exploration

  • Exploration versus conformation
  • Inferring Meaning
  • Numeric Summaries
  • Exploratory Data Visualization

3. Classification: Spam Filtering

  • This or That: Binary Classification
  • Moving Gently into Conditional Probabilities
  • Writing Our First Bayesian Spam Classifier

 4. Ranking: Priority Inbox

  • Ordering Email Messages By Priorities
  • Functions for Extracting the Feature Set
  • Creating a Weighting Scheme for Ranking
  • Training and Testing the Ranker 

5. Regression:  Predicting Page Views

  • Introduction Regression
  • Predicting Web Traffic
  • Defining Correlation 

6. Regularization: Text Regression

  • Nonlinear Relationship Between Columns: Beyond Straight Line
  • Methods for Preventing Over fitting
  • Text Regression

7. Optimization: Breaking Codes

  • Introduction to Optimization
  • Ridge Regression
  • Code Breaking at Optimization

8. PCA: Building a Market Index

  • Unsupervised Learning

9. MDS: Visually Exploring US Senator Similarity

  • Clustering Based on Similarities
  • How do US Senator Cluster?

10. KNN: Recommendation System

  • The K-Nearest Neighbor Algorithm
  • R Package Installing Data

11. Analyzing Social Groups

  • Social Network Analysis
  • Thinking Graphically
  • Analyzing Twister Network
  • Local Community Structure

12. Model Comparison

  • SVMs: The Support Vector Machine
  • Comparing Algorithms

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