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 | |
| 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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