A Course DigiBook
Preface
Acknowledgments
About the Intel-Unnati Programme
1
Introduction to Machine Learning
1.1
Machine Learning (ML)
1.1.1
Why is machine learning important?
1.1.2
Machine learning examples in industry
1.2
Different types of machine learning
1.2.1
Supervised Learning
1.2.2
Unsupervised Learning
1.2.3
Reinforcement Learning
1.3
Choose and build the right machine learning model
1.4
Advantages and disadvantages of machine learning
1.5
Future of machine learning
1.6
FAQs
2
Python for Machine Learning
2.1
History of Python Programming:
2.2
Python as the Best Language for Machine Learning
2.3
Concept of
Libraries
in Python Programming
2.3.1
Key Aspects of Libraries in Python:
2.4
Importance of Libraries in Machine Learning:
2.5
Introduction to Essential Python Libraries for Machine Learning
2.5.1
1. Data Representation:
NumPy
2.5.2
2. Fundamental Analysis:
Pandas
2.5.3
3. Numerical Computation:
SciPy
2.5.4
4. Visualization:
Matplotlib
and
Seaborn
2.5.5
5. Machine Learning:
scikit-learn
2.5.6
Practical Perspective:
2.6
Essential
NumPy
Functions
2.6.1
1.
Creating NumPy Arrays:
2.6.2
2.
Array Shape and Dimensions:
2.6.3
3.
Indexing and Slicing:
2.6.4
4.
Array Reshaping:
2.6.5
5.
Mathematical Operations:
2.6.6
6.
Statistical Operations:
2.6.7
7.
Higher-Dimensional Array Operations:
2.6.8
8.
Advanced Indexing:
2.7
Essential
Pandas
Functions
2.7.1
1.
Loading Data:
2.7.2
2.
Exploratory Data Analysis (EDA):
2.7.3
3.
Data Preprocessing:
2.7.4
4.
Slicing and Indexing:
2.7.5
5.
Merging DataFrames:
2.7.6
6.
Joining DataFrames:
2.7.7
7.
Cross-Tabulation:
2.7.8
8.
Value Counts:
2.7.9
9.
Visualization:
2.7.10
Practical Perspective
2.8
Essential
SciPy
Functions
2.8.1
1.
Linear Algebra:
2.8.2
2.
Calculus:
2.8.3
3.
Optimization:
2.8.4
4.
Descriptive Statistics:
2.8.5
5.
Inferential Statistics and Hypothesis Testing:
2.8.6
Practical Perspective:
2.9
Essential
Matplotlib
Functions
2.9.1
1.
Basic Plots:
2.9.2
2.
Histograms and Density Plots:
2.9.3
3.
Box Plots and Violin Plots:
2.10
Essential
Seaborn
Functions
2.10.1
1.
Statistical Plots:
2.10.2
2.
Distribution Plots:
2.10.3
3.
Categorical Plots:
2.10.4
Practical Perspective:
2.11
Essential scikit-learn Functions
2.11.1
1.
Data Preprocessing:
2.11.2
2.
Model Selection:
2.11.3
3.
Model Training:
2.11.4
4.
Model Evaluation:
2.11.5
Practical Perspective:
3
Supervised Learning
3.1
Understanding Supervised Learning
3.2
Key Elements of Supervised Learning Illustrated with the Iris Dataset
3.2.1
1. Input Data
3.2.2
2. Output Labels
3.2.3
3. Labeled Dataset
3.2.4
4. Model
3.2.5
5. Training
3.3
Real-World Use Cases of Supervised Learning
3.4
Understanding Machine Learning Algorithms
3.4.1
What are Machine Learning Algorithms?
3.4.2
The Role of Models in Machine Learning
3.5
Popular Supervised Learning Algorithms
3.6
1. Regression Models
3.6.1
Mathematical Viewpoint
3.6.2
Statistical Viewpoint
3.6.3
Example: House Price Prediction
3.6.4
Modern Machine Learning Approach with scikit-learn
3.6.5
Comparison and Considerations
3.6.6
Tasks
3.7
2. Classification Algorithm in Machine Learning
3.7.1
Key Characteristics:
3.7.2
Common Classification Algorithms:
3.8
Logistic Regression and Regularization in Classification
3.8.1
a. Logistic Regression
3.8.2
Hypothesis Function
3.8.3
Decision Boundary
3.8.4
Cost Function (Binary Classification)
3.8.5
Gradient Descent (Binary Classification)
3.8.6
Regularization in Logistic Regression
3.8.7
Lasso Regression (L1 Regularization)
3.8.8
b. Decision Trees
3.8.9
Use Cases
3.8.10
Advantages
3.8.11
Limitations
3.8.12
3. Support Vector Machines
3.8.13
Optimization Process
3.8.14
Non-Linear Extension with Kernels
3.8.15
Ensemble methods in Machine Learning
3.8.16
Comparison between Regression and Classification Models
4
Un-supervised Learning
4.1
Clustering
4.1.1
Different clustering approaches
4.1.2
1. Centroid-Based Clustering
4.1.3
2.Hierarchical Clustering
4.2
Steps to Perform Hierarchical Clustering
4.2.1
Mathematical Formulation:
4.2.2
Dendrogram:
4.2.3
Python
code to implement Heirarchical Clustering.
4.2.4
Limitations:
4.2.5
Pseudocode:
4.3
Dimensionality Reduction
4.3.1
The Challenge of High Dimensionality
4.3.2
Key Objectives of Dimensionality Reduction
4.3.3
Role in Feature Engineering
4.3.4
Common Dimensionality Reduction Techniques
4.4
Anomaly Detection
Appendix
A
Declaration
References
Published by intel-unnati
Intel Powered Foundation Course in Machine Learning
References