Scode network's Python is perhaps the most popular choice for beginner machine learning programmers. Python is a programming language that offers many libraries and frameworks to simplify development and cut development time. There are numerous machine learning and artificial intelligence libraries that you can use to automate tasks and analyze data. If you've ever wanted to learn about machine learning, Our training course will teach you the basic concepts and algorithms necessary for this exciting field. You'll learn how to build ensembles and predictive models, and you'll learn to utilize Python's libraries to implement machine learning algorithms. You'll also develop a real-world project to present to your classmates and faculty. Afterwards, you'll have to submit your completed project for peer evaluation.
Category
Education
Machine Learning
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Machine Learning with Python – Basics .................................................................................................1
What is Machine Learning? ....................................................................................................................1
Need for Machine Learning ....................................................................................................................1
Why & When to Make Machines Learn?................................................................................................1
Machine Learning Model........................................................................................................................2
Challenges in Machines Learning............................................................................................................4
Applications of Machines Learning.........................................................................................................4
1. Machine Learning with Python – Python
Ecosystem............................................................................6
An Introduction to Python ......................................................................................................................6
Strengths and Weaknesses of Python ....................................................................................................6
Installing Python .....................................................................................................................................7
Why Python for Data Science? ...............................................................................................................9
Components of Python ML Ecosystem.................................................................................................10
Jupyter Notebook .................................................................................................................................10
Types of Cells in Jupyter Notebook.......................................................................................................12
2. Python Machine Learning – Methods for Machine
Learning .............................................................17
Different Types of Methods..................................................................................................................17
Tasks Suited for Machine Learning.......................................................................................................20
3. Machine Learning with Python – Data Loading for ML
Projects ........................................................22
Consideration While Loading CSV data.................................................................................................22
Methods to Load CSV Data File.............................................................................................................23
Load CSV with NumPy...........................................................................................................................24
Load CSV with Pandas...........................................................................................................................25
4. Machine Learning with Python – Understanding Data with Statistics...............................................27
Introduction..........................................................................................................................................27
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Looking at Raw Data .............................................................................................................................27
Checking Dimensions of Data ...............................................................................................................29
Getting Each Attribute’s Data Type ......................................................................................................29
Statistical Summary of Data..................................................................................................................30
Reviewing Class Distribution.................................................................................................................31
Reviewing Correlation between Attributes ..........................................................................................32
Reviewing Skew of Attribute Distribution ............................................................................................33
5. Machine Learning with Python – Understanding Data with Visualization.........................................35
Introduction..........................................................................................................................................35
Univariate Plots: Understanding Attributes Independently .................................................................35
Density Plots .........................................................................................................................................37
Box and Whisker Plots ..........................................................................................................................38
Multivariate Plots: Interaction Among Multiple Variables ...................................................................39
Correlation Matrix Plot .........................................................................................................................39
Scatter Matrix Plot ................................................................................................................................41
6. Machine Learning with Python – Preparing
Data ...............................................................................43
Introduction..........................................................................................................................................43
Why Data Pre-processing?....................................................................................................................43
Data Pre-processing Techniques...........................................................................................................43
Normalization .......................................................................................................................................44
Types of Normalization .........................................................................................................................45
Binarization ...........................................................................................................................................46
Standardization.....................................................................................................................................48
Data Labeling ........................................................................................................................................49
What is Label Encoding?.......................................................................................................................49
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7. Machine Learning with Python – Data Feature
Selection ..................................................................51
Importance of Data Feature Selection..................................................................................................51
Feature Selection Techniques...............................................................................................................51
Recursive Feature Elimination ..............................................................................................................53
Principal Component Analysis (PCA).....................................................................................................54
Feature Importance..............................................................................................................................55
MACHINE LEARNING ALGORITHMS –
CLASSIFICATION .......................................................56
8. Classification – Introduction................................................................................................................57
Introduction to Classification................................................................................................................57
Types of Learners in Classification ........................................................................................................57
Building a Classifier in Python...............................................................................................................57
Classification Evaluation Metrics ..........................................................................................................61
Confusion Matrix ..................................................................................................................................61
Various ML Classification Algorithms ...................................................................................................63
Applications ..........................................................................................................................................63
9. Classification Algorithms – Logistic Regression ..................................................................................64
Introduction to Logistic Regression ......................................................................................................64
Types of Logistic Regression .................................................................................................................64
Logistic Regression Assumptions..........................................................................................................64
Binary Logistic Regression model .........................................................................................................65
Implementation in Python....................................................................................................................66
Multinomial Logistic Regression Model................................................................................................69
Implementation in Python....................................................................................................................69
10. Classification Algorithms – Support Vector Machine (SVM) ..............................................................71
Introduction to SVM .............................................................................................................................71
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Working of SVM....................................................................................................................................71
Implementing SVM in Python...............................................................................................................72
SVM Kernels..........................................................................................................................................76
Pros and Cons of SVM Classifiers..........................................................................................................79
11. Classification Algorithms – Decision Tree ...........................................................................................80
Introduction to Decision Tree...............................................................................................................80
Implementing Decision Tree Algorithm................................................................................................81
Building a Tree ......................................................................................................................................81
Implementation in Python....................................................................................................................82
12. Classification Algorithms - Naïve Bayes ..............................................................................................86
Introduction to Naïve Bayes Algorithm ................................................................................................86
Building model using Naïve Bayes in Python........................................................................................86
Pros & Cons...........................................................................................................................................88
Applications of Naïve Bayes classification ............................................................................................89
13. Classification Algorithms – Random
Forest.........................................................................................90
Introduction..........................................................................................................................................90
Working of Random Forest Algorithm..................................................................................................90
Implementation in Python....................................................................................................................91
Pros and Cons of Random Forest..........................................................................................................93
MACHINE LEARNING ALGORITHMS -
REGRESSION .............................................................95
14. Regression Algorithms – Overview......................................................................................................96
Introduction to Regression ...................................................................................................................96
Types of Regression Models .................................................................................................................97
Building a Regressor in Python .............................................................................................................97
Types of ML Regression Algorithms...................................................................................................100
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Applications ........................................................................................................................................100
15. Regression Algorithms – Linear Regression ......................................................................................101
Introduction to Linear Regression ......................................................................................................101
Types of Linear Regression .................................................................................................................102
Multiple Linear Regression (MLR).......................................................................................................106
Python Implementation......................................................................................................................107
Assumptions .......................................................................................................................................108
MACHINE LEARNING ALGORITHMS –
CLUSTERING ...........................................................110
16. Clustering Algorithms - Overview......................................................................................................111
Introduction to Clustering ..................................................................................................................111
Cluster Formation Methods................................................................................................................111
Measuring Clustering Performance....................................................................................................112
Silhouette Analysis..............................................................................................................................112
Analysis of Silhouette Score................................................................................................................112
Types of ML Clustering Algorithms.....................................................................................................113
Applications of Clustering...................................................................................................................114
17. Clustering Algorithms – K-means Algorithm.....................................................................................115
Introduction to K-Means Algorithm....................................................................................................115
Working of K-Means Algorithm ..........................................................................................................115
Implementation in Python..................................................................................................................116
Advantages and Disadvantages ..........................................................................................................119
Applications of K-Means Clustering Algorithm...................................................................................120
18. Clustering Algorithms – Mean Shift Algorithm .................................................................................121
Introduction to Mean-Shift Algorithm................................................................................................121
Working of Mean-Shift Algorithm ......................................................................................................121
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Implementation in Python..................................................................................................................121
Advantages and Disadvantages ..........................................................................................................123
19. Clustering Algorithms – Hierarchical Clustering................................................................................124
Introduction to Hierarchical Clustering ..............................................................................................124
Steps to Perform Agglomerative Hierarchical Clustering ...................................................................124
Role of Dendrograms in Agglomerative Hierarchical Clustering.........................................................124
MACHINE LEARNING ALGORITHMS - KNN
ALGORITHM....................................................130 20. KNN Algorithm – Finding
Nearest Neighbors....................................................................................131
Introduction........................................................................................................................................131
Working of KNN Algorithm .................................................................................................................131
Implementation in Python..................................................................................................................132
KNN as Classifier .................................................................................................................................133
KNN as Regressor................................................................................................................................135
Pros and Cons of KNN ........................................................................................................................136
Applications of KNN............................................................................................................................136
21. Machine Learning Algorithms – Performance Metrics .....................................................................137
Performance Metrics for Classification Problems ..............................................................................137
Performance Metrics for Regression Problems..................................................................................141
22. Machine Learning with Pipelines – Automatic Workflows...............................................................143
Introduction........................................................................................................................................143
Challenges Accompanying ML Pipelines.............................................................................................144
Modelling ML Pipeline and Data Preparation ....................................................................................144
Modelling ML Pipeline and Feature Extraction...................................................................................145
23. Machine Learning – Improving Performance of ML Models ............................................................148
Performance Improvement with Ensembles......................................................................................148
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Ensemble Learning Methods ..............................................................................................................148
Bagging Ensemble Algorithms ............................................................................................................149
Boosting Ensemble Algorithms...........................................................................................................152
Voting Ensemble Algorithms ..............................................................................................................154
24. Machine Learning – Improving Performance of ML Model
(Contd…)..............................................157
Performance Improvement with Algorithm Tuning ...........................................................................157
Performance Improvement with Algorithm Tuning ...........................................................................157
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