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Machine Learning Fundamentals

Name: Machine Learning Fundamentals
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This course introduces the basics of machine learning, covering supervised and unsupervised learning, data preprocessing, and model evaluation. It provides a comprehensive foundation for understanding machine learning concepts and techniques. Learners will gain practical knowledge in applying machine learning principles.

See the syllabus

53chapters

141modules

314lessons

66quizzes

Syllabus

53 chapters, 141 modules, 380 lessons and quizzes.

01Getting Started1 modules · 2 lessons
Getting Started
- LessonBefore You Start: How This Course WorksEnroll to open
- LessonYour First PythonEnroll to open
02Python for Data Science1 modules · 4 lessons
Advantages of Python in Data Science
03Using scikit-learn for Machine Learning2 modules · 5 lessons
Overview of scikit-learn
- LessonWhat is scikit-learn?Enroll to open
Installing scikit-learn
04Key Libraries for Machine Learning6 modules · 8 lessons
Interactive Coding Environments
- LessonUsing Jupyter NotebookEnroll to open
Numerical Computing with NumPy
- LessonIntroduction to NumPy ArraysEnroll to open
- QuizNumPy Arrays QuizEnroll to open
Scientific Computing with SciPy
- LessonSciPy and Sparse Matrices: Reading What Gets StoredEnroll to open
Data Visualization with matplotlib
- LessonCreating Line Plots with matplotlibEnroll to open
Data Manipulation with pandas
- LessonIntroduction to pandas DataFramesEnroll to open
- Lessonpandas DataFrames PracticeEnroll to open
Additional Libraries and Tools
- LessonThe mglearn Library and Your Setup for Machine Learning CodeEnroll to open
05Setting Up Your Environment1 modules · 3 lessons
Library Versions and Their Importance
06Supervised Learning Algorithms5 modules · 23 lessons
Sample Datasets
k-Nearest Neighbors
Linear Models
Naive Bayes Classifiers
Decision Trees
07Understanding Classifier Uncertainty3 modules · 9 lessons
Decision Function in Binary Classification
Predicting Probabilities with Classifiers
Uncertainty in Multiclass Classification
08Exploring Unsupervised Learning Techniques2 modules · 5 lessons
Understanding Unsupervised Learning Transformations
Clustering Algorithms for Data Partitioning
- LessonIntroduction to Clustering AlgorithmsEnroll to open
- LessonClustering Algorithms in Practice: Grouping Similar Items Without LabelsEnroll to open
09Evaluating and Applying Unsupervised Learning2 modules · 4 lessons
Challenges and Evaluation
- LessonAssessing Unsupervised Learning OutcomesEnroll to open
Applications and Preprocessing
10Transforming Data for Better Analysis3 modules · 11 lessons
Principal Component Analysis (PCA)
Non-Negative Matrix Factorization (NMF)
Manifold Learning with t-SNE
11Data Clustering Techniques7 modules · 15 lessons
k-Means Clustering
Limitations of k-Means Clustering
- Lessonk-Means Clustering Failure Cases: When the Algorithm Gets the Groups WrongEnroll to open
Vector Quantization with k-Means
- LessonVector Quantization Concept: Representing Each Point by One CenterEnroll to open
- LessonComparing k-means with PCA and NMF: Why Clusters Are Not ComponentsEnroll to open
Agglomerative Clustering
- LessonAgglomerative Clustering BasicsEnroll to open
- LessonLinkage Criteria in Agglomerative ClusteringEnroll to open
Hierarchical Clustering and Dendrograms
- LessonHierarchical Clustering: Grouping Points in LayersEnroll to open
- LessonDendrograms: Reading Hierarchical Clusters as a TreeEnroll to open
DBSCAN Algorithm
- LessonDBSCAN Basics: Finding Clusters Through DensityEnroll to open
- LessonDBSCAN Parameters: How eps and min_samples Shape ClustersEnroll to open
Evaluating Clustering Algorithms
- LessonEvaluating Clustering with Ground TruthEnroll to open
- LessonCommon Mistakes in Evaluating ClusteringEnroll to open
12Mastering Unsupervised Learning and Estimator Interface2 modules · 6 lessons
Unsupervised Learning Essentials
- LessonUnsupervised Learning Algorithms OverviewEnroll to open
- QuizUnsupervised Learning Basics QuizEnroll to open
Estimator Interface Review
13Data Preprocessing Techniques5 modules · 9 lessons
Types of Data Preprocessing
- LessonPreprocessing Methods Overview: Four Ways to Rescale FeaturesEnroll to open
- QuizPreprocessing Methods QuizEnroll to open
Applying Data Transformations
- LessonUsing Preprocessing in Scikit-LearnEnroll to open
- LessonApplying Preprocessing Practice: Why the Test Set Does Not Land on 0 and 1Enroll to open
Scaling Training and Test Data
- LessonConsistent Scaling for Training and Test SetsEnroll to open
- QuizScaling Training and Test Data QuizEnroll to open
Efficient Preprocessing with fit_transform
- LessonUsing fit_transform for Efficient PreprocessingEnroll to open
Impact of Preprocessing on Supervised Learning
- LessonPreprocessing and Supervised Learning PerformanceEnroll to open
- LessonPreprocessing and Supervised Learning Exercise: How Scaling Changes an SVM's AccuracyEnroll to open
14Working with Categorical Data4 modules · 8 lessons
Understanding Categorical Variables
- LessonWhat are Categorical Variables?Enroll to open
One-Hot Encoding for Categorical Variables
Training a Machine Learning Model
- LessonTraining a Logistic Regression Model: Why Clean-Looking Data Can Still FailEnroll to open
- LessonTraining a Model PracticeEnroll to open
Preparing Data for Machine Learning
- LessonCategorical Variables Encoded as NumbersEnroll to open
- LessonUsing get_dummies with Numeric Categorical VariablesEnroll to open
15Feature Transformation for Linear Models4 modules · 4 lessons
Comparing Linear Models and Tree-Based Models
- LessonModel Comparison on Continuous DataEnroll to open
Binning and Discretization
- LessonCreating Bins and One-Hot EncodingEnroll to open
Impact of Binning on Model Performance
- LessonModel Performance on Binned DataEnroll to open
Assessment
- QuizFeature Transformation QuizEnroll to open
16Enhancing Linear Models with Interactions and Polynomials3 modules · 10 lessons
Creating Interaction Features
Using Polynomial Features
Real-World Application: Boston Housing Dataset
17Transforming Features for Linear Models3 modules · 4 lessons
Understanding Count Data and Its Challenges
- LessonCharacteristics of Count DataEnroll to open
Applying Nonlinear Transformations
- LessonLogarithmic Transformation for Count DataEnroll to open
- QuizNonlinear Transformations QuizEnroll to open
Impact on Linear Model Performance
- LessonImproving Linear Model Performance with TransformationsEnroll to open
18Unsupervised Clustering Techniques5 modules · 15 lessons
Introduction to Clustering
- LessonWhat is Clustering?Enroll to open
k-Means Clustering
Agglomerative Clustering
DBSCAN
Evaluating Clustering Algorithms
19Applying Domain Knowledge in Machine Learning4 modules · 11 lessons
Feature Engineering with Expert Knowledge
- LessonUsing Domain Knowledge to Inform Feature EngineeringEnroll to open
- LessonCase Study: Predicting Bicycle RentalsEnroll to open
Time Series Prediction and Feature Representation
Model Evaluation and Feature Engineering
Advanced Feature Engineering Techniques
20Effective Data Representation and Next Steps2 modules · 4 lessons
Data Representation and Feature Engineering
- LessonRepresenting Data for Machine LearningEnroll to open
- LessonFeature Engineering for Improved Model PerformanceEnroll to open
Next Steps in Machine Learning
- LessonEvaluating Model Performance and Selecting ParametersEnroll to open
- QuizData Representation and Feature Engineering QuizEnroll to open
21Evaluating Model Performance with Cross-Validation3 modules · 11 lessons
Introduction to Cross-Validation
Benefits and Strategies of Cross-Validation
Advanced Cross-Validation Techniques
22Evaluating Model Performance5 modules · 12 lessons
Understanding the End Goal
- LessonDefining Business MetricsEnroll to open
Metrics for Binary Classification
Taking Uncertainty into Account
Metrics for Multiclass Classification and Regression
- LessonMulticlass Classification Metrics: Why One Accuracy Number Is Not EnoughEnroll to open
- LessonRegression MetricsEnroll to open
Using Evaluation Metrics in Model Selection
- LessonUsing Scoring Metrics in scikit-learnEnroll to open
23Mastering Model Evaluation and Selection2 modules · 4 lessons
Effective Model Evaluation
Next Steps in Machine Learning Pipelines
- LessonIntroduction to Pipelines: Why Preprocessing Belongs in the SearchEnroll to open
24Applying Machine Learning Basics2 modules · 7 lessons
Recap of Key Concepts
- LessonSupervised Learning OverviewEnroll to open
- LessonDataset Structure: Reading X and y in the Iris DatasetEnroll to open
Model Training and Evaluation
25Machine Learning Fundamentals3 modules · 7 lessons
Limitations of Traditional Rule-Based Systems
- LessonLimitations of Handcoded RulesEnroll to open
Types of Machine Learning Tasks
Data Representation in Machine Learning
26Classifying Iris Species with Machine Learning4 modules · 12 lessons
Understanding the Iris Dataset
Preparing Data for Machine Learning
- LessonSplitting Data into Training and Testing SetsEnroll to open
- LessonData Splitting PracticeEnroll to open
Inspecting and Visualizing Data
Building and Evaluating a k-Nearest Neighbors Model
27Supervised Learning Tasks2 modules · 7 lessons
Classification Tasks
Regression Tasks
28Achieving Accurate Predictions on Unseen Data2 modules · 7 lessons
Understanding Generalization and Model Complexity
Dataset Size and Model Reliability
29Mastering Machine Learning Models3 modules · 12 lessons
Understanding Model Generalization
Machine Learning Model Overview
Model Selection and Evaluation
30Tuning Model Parameters with Grid Search5 modules · 16 lessons
Parallelizing Grid Search and Cross-Validation
- LessonParallelizing Grid SearchEnroll to open
- QuizParallelizing Grid Search QuizEnroll to open
Understanding Grid Search
The Danger of Overfitting Parameters
Grid Search with Cross-Validation
Advanced Grid Search Techniques
31Hyperparameter Tuning with Preprocessing2 modules · 6 lessons
The Problem with Preprocessing Outside Cross-Validation
Using Pipelines for Proper Hyperparameter Tuning
32Creating Processing Pipelines1 modules · 4 lessons
Understanding Pipelines
33Pipelines in Grid Searches and Cross-Validation2 modules · 6 lessons
Using Pipelines in Grid Searches
Illustrating Information Leakage
34Chaining Processing Steps with Pipelines3 modules · 8 lessons
Understanding Pipeline Basics
Creating and Accessing Pipelines
Grid Search with Pipelines
- LessonGrid Search with PipelinesEnroll to open
- LessonGrid Search with Pipelines ExerciseEnroll to open
35Tuning Preprocessing and Model Parameters1 modules · 4 lessons
Using Pipelines for Hyperparameter Tuning
36Combining Grid Search and Pipelines1 modules · 4 lessons
Grid Search over Multiple Models and Preprocessing Steps
37Mastering Machine Learning Pipelines2 modules · 6 lessons
Using Pipelines in Machine Learning
Model Evaluation with Pipelines
38Understanding String Data Types in Machine Learning1 modules · 4 lessons
Categorizing String Data
39Sentiment Analysis of Movie Reviews3 modules · 6 lessons
Understanding the Dataset
- LessonDataset Overview: Movie Reviews for Sentiment AnalysisEnroll to open
- LessonDataset StructureEnroll to open
Loading and Preprocessing the Data
Task Definition
- LessonSentiment Analysis Task: Classifying Movie ReviewsEnroll to open
40Text Representation with Bag-of-Words2 modules · 5 lessons
Understanding Bag-of-Words Representation
- LessonBag-of-Words BasicsEnroll to open
- LessonImplementing Bag-of-Words with CountVectorizerEnroll to open
Applying Bag-of-Words to Sentiment Analysis
41Removing Uninformative Words2 modules · 5 lessons
Understanding and Using Stopwords
Evaluating the Impact of Stopwords
- LessonImpact of Stopwords on Model PerformanceEnroll to open
- LessonStopwords Practice: Deciding Which Common Words to DropEnroll to open
42Rescaling Text Data for Better Analysis2 modules · 5 lessons
Understanding tf-idf Rescaling
- LessonWhat is tf-idf and How Does it Work?Enroll to open
- LessonImplementing tf-idf with TfidfVectorizerEnroll to open
Analyzing tf-idf Features
43Understanding Model Coefficients in Text Classification1 modules · 3 lessons
Interpreting Logistic Regression Coefficients
44Capturing Context with n-Grams3 modules · 8 lessons
Understanding n-Grams
Applying n-Grams to Text Classification
Interpreting n-Gram Features
- LessonUnderstanding Important n-Gram FeaturesEnroll to open
- Quizn-Gram Features QuizEnroll to open
45Enhancing Text Preprocessing with Advanced Techniques2 modules · 5 lessons
Understanding Token Normalization
- LessonThe Importance of Token NormalizationEnroll to open
- LessonStemming vs LemmatizationEnroll to open
Implementing Lemmatization in Text Preprocessing
46Uncovering Hidden Topics in Text Data2 modules · 6 lessons
Understanding Topic Modeling
- LessonWhat is Topic Modeling?Enroll to open
- LessonDocument Clustering vs. Topic ModelingEnroll to open
Latent Dirichlet Allocation (LDA)
47Text Processing Summary and Future Directions2 modules · 6 lessons
Key Takeaways from Text Processing
Advanced Text Processing Topics
48Tackling Machine Learning Problems Effectively2 modules · 6 lessons
Defining and Evaluating a Machine Learning Problem
The Role of Humans in Machine Learning
- LessonHumans in the Loop: Decision-Making and LimitationsEnroll to open
- QuizHumans in Machine Learning QuizEnroll to open
49Deploying Machine Learning Models to Production1 modules · 4 lessons
Production Environment Considerations
50Evaluating Machine Learning Systems in Production1 modules · 5 lessons
Offline and Online Evaluation
51Creating Custom Estimators in scikit-learn1 modules · 3 lessons
Implementing Custom Estimators
52Advancing Your Machine Learning Journey7 modules · 14 lessons
Deepening Your Understanding of Machine Learning Theory
Exploring Alternative Machine Learning Frameworks and Packages
- LessonLooking Beyond scikit-learn and PythonEnroll to open
Advanced Machine Learning Topics
- LessonRanking and Recommender SystemsEnroll to open
- LessonOther Advanced Machine Learning TopicsEnroll to open
Probabilistic Modeling and Inference
- LessonStructured Problems in Machine LearningEnroll to open
- LessonProbabilistic Modeling and ProgrammingEnroll to open
Neural Networks and Deep Learning
- LessonIntroduction to Neural Networks and Deep LearningEnroll to open
Scaling Machine Learning to Larger Datasets
Practicing Machine Learning Skills
- LessonMachine Learning Competitions and ResourcesEnroll to open
- LessonLimitations of Machine Learning CompetitionsEnroll to open
53Applying Machine Learning Effectively1 modules · 2 lessons
Key Takeaways from Machine Learning
- LessonMachine Learning Applications and PracticalityEnroll to open
- QuizMachine Learning Fundamentals QuizEnroll to open

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Machine Learning Fundamentals

Machine Learning Fundamentals

Syllabus

Getting Started

Advantages of Python in Data Science

Overview of scikit-learn

Installing scikit-learn

Interactive Coding Environments

Numerical Computing with NumPy

Scientific Computing with SciPy

Data Visualization with matplotlib

Data Manipulation with pandas

Additional Libraries and Tools

Library Versions and Their Importance

Sample Datasets

k-Nearest Neighbors

Linear Models

Naive Bayes Classifiers

Decision Trees

Decision Function in Binary Classification

Predicting Probabilities with Classifiers

Uncertainty in Multiclass Classification

Understanding Unsupervised Learning Transformations

Clustering Algorithms for Data Partitioning

Challenges and Evaluation

Applications and Preprocessing

Principal Component Analysis (PCA)

Non-Negative Matrix Factorization (NMF)

Manifold Learning with t-SNE

k-Means Clustering

Limitations of k-Means Clustering

Vector Quantization with k-Means

Agglomerative Clustering

Hierarchical Clustering and Dendrograms

DBSCAN Algorithm

Evaluating Clustering Algorithms

Unsupervised Learning Essentials

Estimator Interface Review

Types of Data Preprocessing

Applying Data Transformations

Scaling Training and Test Data

Efficient Preprocessing with fit_transform

Impact of Preprocessing on Supervised Learning

Understanding Categorical Variables

One-Hot Encoding for Categorical Variables

Training a Machine Learning Model

Preparing Data for Machine Learning

Comparing Linear Models and Tree-Based Models

Binning and Discretization

Impact of Binning on Model Performance

Assessment

Creating Interaction Features

Using Polynomial Features

Real-World Application: Boston Housing Dataset

Understanding Count Data and Its Challenges

Applying Nonlinear Transformations

Impact on Linear Model Performance

Introduction to Clustering

k-Means Clustering

Agglomerative Clustering

DBSCAN

Evaluating Clustering Algorithms

Feature Engineering with Expert Knowledge

Time Series Prediction and Feature Representation

Model Evaluation and Feature Engineering

Advanced Feature Engineering Techniques

Data Representation and Feature Engineering

Next Steps in Machine Learning

Introduction to Cross-Validation

Benefits and Strategies of Cross-Validation

Advanced Cross-Validation Techniques

Understanding the End Goal

Metrics for Binary Classification

Taking Uncertainty into Account

Metrics for Multiclass Classification and Regression

Using Evaluation Metrics in Model Selection

Effective Model Evaluation

Next Steps in Machine Learning Pipelines

Recap of Key Concepts

Model Training and Evaluation

Limitations of Traditional Rule-Based Systems