Online Course
The Machine Learning Algorithms A-Z

Name: The Machine Learning Algorithms A-Z Course
Price: 36 USD

Master the core concepts of popular ML algorithms: understand when and how to apply different machine learning techniques effectively

4.9

808 reviews on

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10264 students already have enrolled

Skill level:

Advanced

Duration:

5 hours

Lessons (5 hours)

CPE credits:

0.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Acquire ML skills that connect theory with practical application.
Understand the strengths and limitations of ML models.
Select the optimal algorithm for specific use cases.
Gain business intuition to identify ML-appropriate problems.
Boost your career with in-demand machine learning expertise.

Topics & tools

Random ForestXGBoostK Nearest NeighborsHierarchical ClusteringGradient Boosted TreesGradient DescentK-Means ClusteringSupport Vector MachinesMachine LearningNaïve BayesCollaborative FilteringArtificial IntelligenceDecision TreesMachine and Deep LearningPython

Your instructor

Jeff Li

Senior Data Scientist at Netflix

Track record

Bringing real-world expertise from leading global companies

Academic background

Bachelor's degree, Data Science

Media and recognition

Built Netflix's first ad-supply forecasts; drove $1B+ revenue
Led Spotify's podcast ads forecasting; impacted $100M revenue
Co-authored Spotify & DoorDash posts on experiments, ML
Developed Dataquest courses used by 10,000+ learners

Ask Jeff a question

Ken Jee

Data Science Leader | Podcast Host | Z by HP Global Data Science Ambassador

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Computer Science

Media and recognition

Founder of #66DaysOfData movement
Z by HP Global Data Science Ambassador
Podcast host: Ken's Nearest Neighbors & The Exponential Athlete

Ask Ken a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 0.5 Field of Study: Information Technology
Delivery Method: QAS Self Study

Looking to break into machine learning? Need to review the ins and outs of each algorithm? Preparing for an interview? Curious to see how these algorithms are applied to business? As ML practitioners, the true value of ML is not in memorizing complicated formulas. It’s not using the latest, greatest deep learning architecture. It’s knowing when to use an algorithm and how to maximize the impact of that model. It’s knowing how to use them to solve REAL business problems. The true value of ML is not ML. It’s solving important business problems. Whether you need to build a forecasting model that forms the backbone of the ads business, builld a recommender system powering millions of purchases or build a fraud detection system that catches bad apples, this course will arm you with both the ML knowledge AND the know-how on how to apply it to your business problem. We don’t want you to leave this course just knowing ML. We want you to leave this course to leave as ML practitioners.

Prerequisites

Basic understanding of machine learning concepts.

Advanced preparation

Curriculum

189 lessons 106 exercises 1 exam

1. Course Introduction

6 min

6 min

In this introductory section, you’ll learn which of the course is best for you, the types of ML issues businesses need to solve, and additional resources from the course authors.

Introduction Free

ML Algorithms course - GitHub repository Free

How to Use this Course Free

Types of ML Problems Free

Exercise Free

Additional Resources Free
2. Linear Regression

24 min

24 min

Linear regression is the most dynamic model out of all we review. It’s an exceptional framework for making predictions and extracting insight into relationships between variables.

Linear Regression Free

Real World Business Problems Free

Example: Linear Regression Free

Intuition: Linear Regression Free

Exercise

Training Step-by-Step: Linear Regression Free

Exercise

Prediction: Linear Regression Free

Exercise

Assumptions: Linear Regression Free

Exercise

Assumption #1: Model is linear in coefficients and error terms Free

Exercise

Assumption #2: Homoscedasticity Free

Exercise

Assumption #3: Multicollinearity Free

Exercise

Assumption #4: Independence/Autocorrelation

Exercise

Assumption #5: Normally Distributed Error Terms

Exercise

Assumption #6: Outliers

Inference - Interpreting Output

Exercise

AB Testing Example

Exercise

ML Process: Linear Regression

Pros & Cons, When to Use

Exercise
3. Ridge, Lasso, Elastic Net

14 min

14 min

One way to limit some of the negatives of linear regression is via regularization. We can regularize a linear regression model using ridge, lasso, and elastic net algorithms.

Ridge, Lasso, Elastic Net

Intuition: Ridge, Lasso, Elastic Net

Plain Definition: Ridge, Lasso, Elastic Net

Exercise

Shrinkage Methods vs. Feature Selection

Step-by-Step Intuition: Ridge, Lasso, Elastic Net

Exercise

Lasso Regression (L1)

Ridge Regression (L2)

ElasticNet (L1 + L2)

Exercise

Determining the Degree of Regularization

Difference between Lasso & Ridge

Link to resources

When to use: Ridge, Lasso, Elastic Net

Exercise
4. Logistic Regression

22 min

22 min

Like linear regression, logistic regression is one of the most powerful and straightforward models.

Introduction: Logistic Regression

Example: Logistic Regression

Intuition: Logistic Regression

Exercise

Real World Business Problems: Logistic Regression

Exercise

What is Logit

Exercise

Step-by-Step Prediction: Logistic Regression

Step-by-Step Training: Logistic Regression

Exercise

Assumptions: Logistic Regression

Exercise

Understanding Logistic Regression Output

Exercise

Maximum Likelihood Explained

Exercise

Log Loss

Exercise

Predicting Multiple Classes using Multinomial Logistic Regression

Exercise

ML Process: Logistic Regression

Exercise

ProsCons, When to Use
5. Gradient Descent

10 min

10 min

Gradient descent is an optimization algorithm that powers many of our ML algorithms. Think of it as a helper algorithm, enabling us to find the best formulation of our ML model.

Gradient Descent

Intuition: Gradient Descent

Plain Definition: Gradient Descent

Exercise

Step-by-Step: Gradient Descent

Exercise

Assumptions: Gradient Descent

Exercise

Parameter Tuning (Step size, Alpha)

Gradient Descent Pros and Cons

Stochastic Gradient Descent

Pros and Cons: Gradient Descent

Exercise
6. Decision Trees

18 min

18 min

Decision trees work for classification and regression problems. While individual decision trees don’t typically produce the best predictive outcomes in real life, they are highly interpretable.

Decision Trees

Example: Decision Trees

Plain Explanation: Decision Trees

Exercise

Different Components of Decision Trees Explained

Exercise

Real World Business Example: Decision Trees

Assumptions: Decision Trees

Training Step-by-Step: Decision Trees

Exercise

Prediction Step-by-Step: Decision Trees

Additional Metrics: Decision Trees

Tuning the Parameters: Decision Trees

Exercise

ML Process: Decision Trees

Decision Trees Assumptions

Pros and Cons: Decision Trees

When to Use Decision Trees.

Exercise
7. Random Forest

16 min

16 min

Random forest is one of the most popular models for classification and regression. It works exceedingly well with datasets with many categorical values or a mix of categorical and continuous variables.

Random Forest

Intuition: Random Forest

Example: Random Forest

Exercise

Real World Business Problems: Random Forest

Exercise

Plain Definition: Bagging

Where Bagging Fails

Plain Definition: Random Forest

Step-by-Step (Training): Random Forest

Step-by-Step (Prediction): Random Forest

Exercise

How Random Forest give us Feature Importance

Out of Bag Error

ML Process: Random Forest

When to use: Random Forest

Pros and Cons: Random Forest

Exercise
8. Gradient Boosted Trees

17 min

17 min

Gradient-boosted trees are a way for our models to learn from their mistakes. Unlike random forest—where we grow our trees in parallel, then aggregate the results—with gradient-boosted trees, we grow trees sequentially.

Gradient Boosted Trees

Example: Gradient Boosted Trees

Real World Business Problems: Gradient Boosted Trees

Plain Definition: Gradient Boosted Trees

Exercise

Terminology: Gradient Boosted Trees

Assumptions: Gradient Boosted Trees

Training Step-by-Step (Regression): Gradient Boosted Trees

Training Step-by-Step (Classification): Gradient Boosted Trees

Exercise

Prediction Step-by-Step: Gradient Boosted Trees

What does the “Gradient” mean

How Gradient Boosted Trees give us Feature Importance

ML Process: Gradient Boosted Trees

When to use Gradient Boosted Trees

Exercise
9. XGBoost

15 min

15 min

XGBoost is a famous open-source gradient-boosting algorithm for supervised learning tasks, including regression and classification problems. Reducing errors during each iteration makes better predictions by combining several decision trees.

Intuition: XGBoost

Real World Business Problems: XGBoost

Plain Definition: XGBoost

Exercise

XGBoost Algorithm Improvements

System Improvements

ML Process: XGBoost

When to use XGBoost

Pros and Cons: XGBoost

Exercise
10. K Nearest Neighbors

15 min

15 min

K-nearest neighbors is a popular machine learning algorithm for classifying and predicting data. It works by finding the k closest data points to a new, unknown data point and categorizing it based on the most common class among its neighbors.

Intuition: KNN

Example: KNN

Plain Definition: KNN

Assumptions : KNN

Exercise

Training Step-by-Step: KNN

Prediction Step-by-Step: KNN

Tuning Parameters: KNN

ML Process: KNN

When to use KNN

Exercise
11. K-Means Clustering

16 min

16 min

K-means clustering is the first unsupervised learning method we will introduce. As a reminder, unsupervised learning means that our data doesn’t have specific target labels we try to classify. Instead, we consider data as a whole and see algorithmically what groups can be made from it.

Intuition: K-Means Clustering

Example: K-Means Clustering

Plain Definition: K-Means Clustering

Exercise

Real World Business Problems: K-Means Clustering

Step-by-Step Training: K-Means Clustering

Selecting K

Silhouette Method

Exercise

Hard Clustering vs- Soft Clustering

Derivatives of K-Means

Assumptions: K-Means Clustering

ML Process: K-Means Clustering

When do we use K means Clustering

Exercise
12. Hierarchical Clustering

10 min

10 min

Hierarchical clustering is similar to how you organize files into folders on your computer. Whenever we organize our files into their folders, we perform hierarchical clustering.

Intuition: Hierarchical Clustering

Real World Business Problems: Hierarchical Clustering

Definition: Hierarchical Clustering

Exercise

Step-by-Step Agglomerative Clustering

Linkages

Distance

Exercise

ML Process: Hierarchical Clustering

Pros and Cons: Hierarchical Clustering

When to Use: Hierarchical Clustering

Exercise
13. Support Vector Machines

18 min

18 min

Support vector machines (SVMs) work by identifying a hyperplane that best separates the data into different classes or predicts the target variable for regression. It also aims to find the optimal hyperplane that maximizes the margin between the classes while minimizing the misclassification error.

Intuition: SVM

Real World Business Problems: SVM

Step-by-Step Training (Non-Technical): SVM

Exercise

Loss Function

Nonlinear Data

Prediction (Step-by-Step): SVM

Terminology: SVM

Assumptions: SVM

Soft vs Hard Margins: SVM

Exercise

How to use SVMs as a multi-class classifier

How does SVM Regression Work

ML Process: SVM

Pros & Cons (Classifier): SVM

When to use an SVM Classifier

Exercise
14. Artificial Neural Nets

37 min

37 min

Artificial neural nets (ANNs) are machine learning algorithms based on the structure and function of biological neurons. Layers of interconnected nodes process and transform input data to produce predictions. The neural network learns by adjusting the weights and biases of the connections between the nodes based on the error of the predictions.

Intuition: Artificial Neural Nets

Exercise

Real world Business Problems: Artificial Neural Nets

Example: Artificial Neural Nets

Exercise

Multi-Layered Networks: Artificial Neural Nets

Classification - Activation Layers

Vanishing Gradient Problem

Exercise

Activation Layers

Embeddings

Exercise

Types of ANNs

Exercise

Transfer Learning

ML Process: Artificial Neural Nets

Pros and Cons: Artificial Neural Nets

Exercise
15. Collaborative Filtering - Non-Negative Matrix Factorization

18 min

18 min

Collaborative filtering uses ratings from a group of (collaborative) users to infer the preference of another (filtering) user.

Exercise

Intuition: Collaborative Filtering

Plain Definition: Collaborative Filtering

Real world Business Problems: Collaborative Filtering

Exercise

Assumptions: Collaborative Filtering

Different Approaches to Collaborative Filtering

Matrix Factorization Intuition

Matrix Factorization Definition

Assumptions: NMF

Exercise

Step-by-Step (prediction): Collaborative Filtering

Step-by-Step (training): Collaborative Filtering

Determining the ideal number of latent variables

Addressing the Cold-Start Problem

Exercise

ML Process: Collaborative Filtering

ProsCons: Collaborative Filtering

When to use NMF

Exercise
16. Naïve Bayes

19 min

19 min

Discover the intricacies of the Naïve Bayes algorithm, exploring Bayes Theorem, its intuitive definition, why it's called "Naïve", the ML process, the pros and cons of this algorithm, a real-life business example, and effective use cases.

Bayes Theorem

Exercise

Intuition and Plain Definition

Step-By-Strp Explanation - First Part

Step-by-step Explanation - Second Part

Exercise

Why is Naive Baïve called Naïve?

The types of Naïve Bayes

ML Process: Naïve Bayes

Pros and Cons: Naïve Bayes

Real-Life Business Example

When to use Naïve Bayes

Exercise
17. Practical projects

76 min

76 min

In this section you can find practical projects that will help you enhance your skills.

Regression project

Classification project
18. Course exam

45 min

45 min

Course exam

Free lessons

1.1 Introduction

2 min

1.2 ML Algorithms course - GitHub repository

1 min

1.3 How to Use this Course

1 min

1.4 Types of ML Problems

1 min

1.6 Additional Resources

1 min

2.1 Linear Regression

1 min

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