Machine Learning Mastery Track - Online Learning with Certificate

Machine Learning Mastery

Intermediate

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Master the full spectrum of machine learning with Python, combining practical skills with strong theoretical foundations. Build models quickly with sc…

188

Chapters

Learning hours

182

Assignments

AI Assistant

Shareable Certificate

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What you will learn

Building machine learning models with scikit-learn

Implementing regression techniques for forecasting

Understanding reinforcement learning basics

Preprocessing data using Scikit-learn tools

Applying classification methods to complex data

Designing and training neural networks

Creating pipelines for efficient data processing

Exploring clustering for pattern recognition

Evaluating models with classification metrics

Learning track content

Learn the Machine Learning concepts and the ML project workflow.

What is ML

Types of Machine Learning

Training Set

Types of Data

Machine Learning Workflow

Preprocessing is probably the most important stage of an ML project. This chapter covers the preprocessing steps needed for almost any dataset.

Scikit-learn Concepts

Getting Familiar with Dataset

Dealing with Missing Values

Challenge: Imputing Missing Values

OrdinalEncoder

One-Hot Encoder

LabelEncoder

Challenge: Encoding Categorical Variables

Why Scale the Data?

StandardScaler, MinMaxScaler, MaxAbsScaler

Challenge: Scaling the Features

A pipeline is a neat way to combine all the preprocessing steps as well as a model. Pipelines make it much easier to train and use a model.

What is Pipeline

ColumnTransformer

Efficient Data Preprocessing with Pipelines

Challenge: Creating a Pipeline

Final Estimator

Challenge: Creating a Complete ML Pipeline

Modeling is the most fun stage of an ML project. Let's learn to build, fine-tune and evaluate the model!

Challenge: Evaluating the Model with Cross-Validation

GridSearchCV

The Flaw of GridSearchCV

Challenge: Tuning Hyperparameters with RandomizedSearchCV

Modeling Summary

Challenge: Putting It All Together

Explore the foundation of mathematical functions. Learn different types of algebraic and transcendental functions, their properties, and how to implement them in Python to solve real-world problems.

Introduction to Functions

Jupyter Notebook Basics

Implementing Basic Functions in Python

Algebraic Functions

Implementing Identity-Quadratic Functions in Python

Implementing Rational Functions in Python

Challenge: Maximizing Profit Using Quadratic Functions

Transcendental Functions

Implementing Exponential-Log Functions in Python

Implementing Sinusoidal-Tangent Functions in Python

Master the concepts of sets and series, from basic operations to practical applications. Gain hands-on experience implementing set operations and working with arithmetic and geometric series in Python.

Introduction to Sets

Implementing Sets in Python

Challenge: Customer Segmentation Using Sets

Introduction to Series

Implementing Series in Python

Challenge: Predicting Savings Growth

Develop a solid understanding of limits, derivatives, integrals, and partial derivatives. Connect theory to practice by implementing these concepts in Python and applying them to optimization through gradient descent.

Introduction to Limits

Implementing Limits in Python

Introductions to Derivatives

Implementing Derivatives to Python

Introduction to Integrals

Implementing Integrals in Python

Introductions to Partial Derivatives

Implementing Partial Derivatives in Python

Gradient Descent

Implementing Gradient Descent in Python

Challenge: Fitting a Line with Gradient Descent

Build strong knowledge of vectors, matrices, and transformations. Learn decomposition methods and eigenvalue analysis, while reinforcing concepts with Python coding challenges and practical data science applications.

Introductions to Vectors

Implementing Vectors in Python

Matrix Operations

Matrix Operations in Python

Introduction to Matrix Transformations

Implementing Matrix Transformation in Python

Challenge: Combined Transformations of a Vector

Introduction to Matrix Decomposition

Implementing Matrix Decomposition in Python

Challenge: Solving a Linear System with LU Decomposition

Introduction to Eigenvectors & Eigenvalues

Implementing Eigenvectors & Eigenvalues in Python

Dive into probability theory and statistics. Study conditional probability, Bayes' theorem, and statistical measures. Implement key concepts in Python, simulate distributions, and strengthen your skills through challenges and quizzes.

Understanding Probability Basics

Implementing Probability Basics in Python

Understanding Conditional Probability & Bayes' Theorem

Implementing Conditional Probability & Bayes' Theorem in Python

Understading Sampling

Implementing Sampling to Python

Understanding Central Tendency & Spread

Implementing Spread in Python

Challenge: Quality Control Probability Analysis

Understanding Probability Distributions

Implementing Probability Distributions to Python

Challenge: Quality Control Sampling

Master the foundational steps for cleaning and preparing raw data for analysis and machine learning.

Understanding Data Quality

Handling Missing Values

Dealing with Duplicates and Outliers

Challenge: Data Cleaning

Transform raw data into usable features for machine learning models.

Scaling and Normalization

Encoding Categorical Variables

Feature Transformation and Extraction

Challenge: Transformation Grid

Engineer and select features to improve model performance and interpretability.

Feature Selection Basics

Creating Interaction Features

Automating Preprocessing with Pipelines

Challenge: Preprocessing Pipeline

Let's start with the simplest Linear Regression model! You will learn the idea behind Linear Regression and how to make predictions in Python.

What is Linear Regression

Finding the Parameters

Building Linear Regression Using NumPy

Building Linear Regression Using Statsmodels

Challenge: Predicting House Prices

Most real-world prediction tasks involve more than one feature. You will learn how to handle Linear Regression with multiple features.

Linear Regression with Two Features

Linear Regression with N Features

Building Multiple Linear Regression

Choosing the Features

Challenge: Predicting Prices Using Two Features

A straight line does not always describe the data well. Let's learn how to build a more complex model for prediction! That's what the Polynomial Regression is suited for.

Quadratic Regression

Polynomial Regression

Building Polynomial Regression

Interpolation vs Extrapolation

Challenge: Evaluating the Model

Now that you know how to build many Linear Regression models, you need a way to choose the best one. This is achievable using metrics. This section explains the most used ones and the difficulties you can face using them.

Metrics

Overfitting

R-squared

Challenge: Predicting Prices Using Polynomial Regression

Discover how the k-nearest neighbors algorithm makes predictions based on similarity. Learn to handle multiple features, tune parameters, and apply cross-validation to improve accuracy.

What is Classification

What is k-NN

k-NN with Multiple Features

Implementing k-NN

Multi-Class Classification

Train-test Split and Cross Validation

Challenge: Choosing the Best K Value

Understand how logistic regression models probabilities and classifies outcomes. Practice implementing it, interpreting decision boundaries, and applying regularization to prevent overfitting.

What is Logistic Regression

Finding the Parameters

Challenge: Implementing Logistic Regression

Decision Boundary

Overfitting and Regularization

Challenge: Classifying Inseparable Data

Learn how decision trees split data into meaningful groups based on feature values. Explore how parameters like tree depth and minimum samples per leaf affect model performance and generalization.

What is Decision Tree

Splitting the Nodes

Preventing Overfitting in Decision Trees

Challenge: Implementing a Decision Tree

Explore how random forests combine multiple decision trees to improve accuracy and robustness. Understand the role of randomness and apply this ensemble method to real-world data.

What is Random Forest

The Randomness of Forest

Challenge: Implementing a Random Forest

Evaluate models using metrics such as accuracy, precision, recall, and F1-score. Learn to interpret confusion matrices and compare multiple classifiers to identify the best-performing model.

Confusion Matrix

Metrics

Challenge: Comparing Models

Summary

Dive into the fundamentals of clustering and discover how it differs from classification. Explore essential algorithms, tools, and libraries that power this unsupervised learning technique to uncover hidden patterns in data.

Introduction to Clustering

Clustering Vs Classification

Clustering Algorithms and Libraries

Gain a solid understanding of key preprocessing techniques that ensure effective clustering. Learn how to handle missing values, encode categorical features, normalize data, and choose appropriate distance measures and linkages to boost clustering accuracy.

Missing Values Handling

Categorical Features Encoding

Data Normalization

Distance Measures

Linkages

Challenge: Preprocessing the Dataset

Master the skills needed to apply K-Means clustering effectively. Learn how the algorithm works, determine the optimal number of clusters, and gain hands-on experience by implementing K-Means on both synthetic and real-world datasets.

What is K-Means Clustering?

How K-Means Algorithm Works?

Finding Optimal Number of Clusters Using WSS

Finding Optimal Number of Clusters Using Silhouette Score

Implementing on Dummy Dataset

Implementing on Real Dataset

Challenge: Implementing K-Means Clustering

Explore the essentials of hierarchical clustering and learn how to group data into meaningful clusters using dendrograms. Build confidence in identifying the optimal number of clusters and implementing the technique on both synthetic and real-world datasets.

How Hierarchical Clustering Works?

Optimal Number of Clusters

Implementing on Dummy Dataset

Implementing on Customers Dataset

Challenge: Implementing Hierarchical Clustering

Discover how DBSCAN excels at detecting clusters of varying shapes and handling noise in data. Learn the mechanics behind this density-based algorithm, how to assign points to clusters, and apply it to both synthetic and real datasets with confidence.

Why DBSCAN?

How DBSCAN Works?

How to Assign Points to the Clusters?

Implementing on Dummy Dataset

Implementing on Real Dataset

Challenge: Implementing DBSCAN

Gain a solid understanding of Gaussian Mixture Models and how they use probability to model complex cluster shapes. Learn the principles of Gaussian distribution, explore how GMMs work, and build confidence by applying them to both dummy and real-world data.

Problem Statement

What is Gaussian Distribution?

How GMMs Work?

Implementing GMM on Dummy Data

Implementing GMM on Real Data

Challenge: Implementing Gaussian Mixture Models

Conclusion

Explore the fundamental metrics used to evaluate classification models, including their definitions, formulas, Python implementations, and interpretation. Includes advanced metrics and model validation techniques relevant to classification.

Understanding Classification Evaluation

Accuracy, Precision, and Recall

F1 Score

ROC Curve and AUC

Confusion Matrix

Cross-Validation for Classification

Challenge: Classification Metrics

Delve into the core metrics for evaluating regression models, including their mathematical foundations, Python implementations, and result interpretation. Includes advanced validation techniques relevant to regression.

Key Concepts in Regression Evaluation

MSE, RMSE, and MAE

R² Score

Challenge: Regression Metrics

Examine the key metrics for evaluating unsupervised learning models, including clustering, dimensionality reduction, and anomaly detection. Learn their mathematical foundations, Python implementations, and interpretation.

Introduction to Unsupervised Evaluation

Clustering Metrics

Dimensionality Reduction Evaluation

Anomaly Detection Metrics

Challenge: Unsupervised Metrics

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