The Machine Learning Algorithms A-Z

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

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

5 hours of content 8838 students

$99.00

Lifetime access

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14-Day Money-Back Guarantee

What you get:

5 hours of content
108 Interactive exercises
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

The Machine Learning Algorithms A-Z

A course by Jeff Li and Ken Jee

$99.00

Lifetime access

Buy now

14-Day Money-Back Guarantee

What you get:

5 hours of content
108 Interactive exercises
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

$99.00

Lifetime access

Buy now

$99.00

Lifetime access

Buy now

14-Day Money-Back Guarantee

What you get:

5 hours of content
108 Interactive exercises
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

What You Learn

Acquire machine learning skills that bridge theoretical knowledge and practical application
Gain insight into the strength and limitations of various machine learning models
Build confidence in selecting the optimal machine learning algorithm needed for specific use cases
Understand fundamental machine learning theory that will help you achieve outstanding real-world results
Acquire the business intuition to discern whether a problem requires machine learning or can be solved through simpler analytical methods
Improve your career prospects with in-demand machine learning skills, essential for your success in an AI-driven world

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

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.

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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

Curriculum

1. Course Introduction

5 Lessons 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
2 min
ML Algorithms course - GitHub repository Read now
1 min
How to Use this Course
1 min
Types of ML Problems
1 min
Additional Resources
1 min
2. Linear Regression

17 Lessons 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
1 min
Real World Business Problems
1 min
Example: Linear Regression
1 min
Intuition: Linear Regression
1 min
Training Step-by-Step: Linear Regression
2 min
Prediction: Linear Regression
1 min
Assumptions: Linear Regression
1 min
Assumption #1: Model is linear in coefficients and error terms
1 min
Assumption #2: Homoscedasticity
1 min
Assumption #3: Multicollinearity
3 min
Assumption #4: Independence/Autocorrelation
1 min
Assumption #5: Normally Distributed Error Terms
1 min
Assumption #6: Outliers
1 min
Inference - Interpreting Output
3 min
AB Testing Example
1 min
ML Process: Linear Regression
2 min
Pros & Cons, When to Use
2 min
3. Ridge, Lasso, Elastic Net

12 Lessons 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
1 min
Intuition: Ridge, Lasso, Elastic Net
1 min
Plain Definition: Ridge, Lasso, Elastic Net
1 min
Shrinkage Methods vs. Feature Selection
1 min
Step-by-Step Intuition: Ridge, Lasso, Elastic Net
2 min
Lasso Regression (L1)
1 min
Ridge Regression (L2)
1 min
ElasticNet (L1 + L2)
1 min
Determining the Degree of Regularization
1 min
Difference between Lasso & Ridge
1 min
Link to resources
1 min
When to use: Ridge, Lasso, Elastic Net
2 min
4. Logistic Regression

14 Lessons 22 Min

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

Introduction: Logistic Regression
1 min
Example: Logistic Regression
1 min
Intuition: Logistic Regression
2 min
Real World Business Problems: Logistic Regression
1 min
What is Logit
1 min
Step-by-Step Prediction: Logistic Regression
1 min
Step-by-Step Training: Logistic Regression
3 min
Assumptions: Logistic Regression
2 min
Understanding Logistic Regression Output
3 min
Maximum Likelihood Explained
2 min
Log Loss
1 min
Predicting Multiple Classes using Multinomial Logistic Regression
1 min
ML Process: Logistic Regression
2 min
ProsCons, When to Use
1 min
5. Gradient Descent

9 Lessons 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
1 min
Intuition: Gradient Descent
1 min
Plain Definition: Gradient Descent
2 min
Step-by-Step: Gradient Descent
1 min
Assumptions: Gradient Descent
1 min
Parameter Tuning (Step size, Alpha)
1 min
Gradient Descent Pros and Cons
1 min
Stochastic Gradient Descent
1 min
Pros and Cons: Gradient Descent
1 min
6. Decision Trees

14 Lessons 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
1 min
Example: Decision Trees
1 min
Plain Explanation: Decision Trees
1 min
Different Components of Decision Trees Explained
1 min
Real World Business Example: Decision Trees
1 min
Assumptions: Decision Trees
1 min
Training Step-by-Step: Decision Trees
4 min
Prediction Step-by-Step: Decision Trees
1 min
Additional Metrics: Decision Trees
1 min
Tuning the Parameters: Decision Trees
1 min
ML Process: Decision Trees
2 min
Decision Trees Assumptions
1 min
Pros and Cons: Decision Trees
1 min
When to Use Decision Trees.
1 min
7. Random Forest

14 Lessons 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
1 min
Intuition: Random Forest
1 min
Example: Random Forest
1 min
Real World Business Problems: Random Forest
1 min
Plain Definition: Bagging
1 min
Where Bagging Fails
2 min
Plain Definition: Random Forest
1 min
Step-by-Step (Training): Random Forest
1 min
Step-by-Step (Prediction): Random Forest
1 min
How Random Forest give us Feature Importance
1 min
Out of Bag Error
1 min
ML Process: Random Forest
2 min
When to use: Random Forest
1 min
Pros and Cons: Random Forest
1 min
8. Gradient Boosted Trees

13 Lessons 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
1 min
Example: Gradient Boosted Trees
1 min
Real World Business Problems: Gradient Boosted Trees
1 min
Plain Definition: Gradient Boosted Trees
1 min
Terminology: Gradient Boosted Trees
1 min
Assumptions: Gradient Boosted Trees
1 min
Training Step-by-Step (Regression): Gradient Boosted Trees
1 min
Training Step-by-Step (Classification): Gradient Boosted Trees
2 min
Prediction Step-by-Step: Gradient Boosted Trees
1 min
What does the “Gradient” mean
1 min
How Gradient Boosted Trees give us Feature Importance
1 min
ML Process: Gradient Boosted Trees
3 min
When to use Gradient Boosted Trees
2 min
9. XGBoost

8 Lessons 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
3 min
Real World Business Problems: XGBoost
1 min
Plain Definition: XGBoost
1 min
XGBoost Algorithm Improvements
4 min
System Improvements
2 min
ML Process: XGBoost
2 min
When to use XGBoost
1 min
Pros and Cons: XGBoost
1 min
10. K Nearest Neighbors

9 Lessons 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
1 min
Example: KNN
3 min
Plain Definition: KNN
1 min
Assumptions : KNN
1 min
Training Step-by-Step: KNN
1 min
Prediction Step-by-Step: KNN
1 min
Tuning Parameters: KNN
4 min
ML Process: KNN
1 min
When to use KNN
2 min
11. K-Means Clustering

12 Lessons 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
1 min
Example: K-Means Clustering
1 min
Plain Definition: K-Means Clustering
1 min
Real World Business Problems: K-Means Clustering
1 min
Step-by-Step Training: K-Means Clustering
1 min
Selecting K
2 min
Silhouette Method
1 min
Hard Clustering vs- Soft Clustering
1 min
Derivatives of K-Means
1 min
Assumptions: K-Means Clustering
1 min
ML Process: K-Means Clustering
3 min
When do we use K means Clustering
2 min
12. Hierarchical Clustering

9 Lessons 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
1 min
Real World Business Problems: Hierarchical Clustering
1 min
Definition: Hierarchical Clustering
1 min
Step-by-Step Agglomerative Clustering
1 min
Linkages
1 min
Distance
1 min
ML Process: Hierarchical Clustering
2 min
Pros and Cons: Hierarchical Clustering
1 min
When to Use: Hierarchical Clustering
1 min
13. Support Vector Machines

14 Lessons 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
1 min
Real World Business Problems: SVM
1 min
Step-by-Step Training (Non-Technical): SVM
3 min
Loss Function
1 min
Nonlinear Data
1 min
Prediction (Step-by-Step): SVM
1 min
Terminology: SVM
1 min
Assumptions: SVM
1 min
Soft vs Hard Margins: SVM
1 min
How to use SVMs as a multi-class classifier
2 min
How does SVM Regression Work
1 min
ML Process: SVM
2 min
Pros & Cons (Classifier): SVM
1 min
When to use an SVM Classifier
1 min
14. Artificial Neural Nets

12 Lessons 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
2 min
Real world Business Problems: Artificial Neural Nets
1 min
Example: Artificial Neural Nets
5 min
Multi-Layered Networks: Artificial Neural Nets
2 min
Classification - Activation Layers
2 min
Vanishing Gradient Problem
4 min
Activation Layers
3 min
Embeddings
5 min
Types of ANNs
7 min
Transfer Learning
1 min
ML Process: Artificial Neural Nets
2 min
Pros and Cons: Artificial Neural Nets
3 min
15. Collaborative Filtering - Non-Negative Matrix Factorization

15 Lessons 18 Min

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

Intuition: Collaborative Filtering
1 min
Plain Definition: Collaborative Filtering
1 min
Real world Business Problems: Collaborative Filtering
1 min
Assumptions: Collaborative Filtering
1 min
Different Approaches to Collaborative Filtering
1 min
Matrix Factorization Intuition
1 min
Matrix Factorization Definition
1 min
Assumptions: NMF
1 min
Step-by-Step (prediction): Collaborative Filtering
1 min
Step-by-Step (training): Collaborative Filtering
2 min
Determining the ideal number of latent variables
1 min
Addressing the Cold-Start Problem
2 min
ML Process: Collaborative Filtering
2 min
ProsCons: Collaborative Filtering
1 min
When to use NMF
1 min
16. Naïve Bayes

10 Lessons 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
4 min
Intuition and Plain Definition
1 min
Step-By-Strp Explanation - First Part
7 min
Step-by-step Explanation - Second Part
1 min
Why is Naive Baïve called Naïve?
1 min
The types of Naïve Bayes
1 min
ML Process: Naïve Bayes
1 min
Pros and Cons: Naïve Bayes
1 min
Real-Life Business Example
1 min
When to use Naïve Bayes
1 min
17. Practical projects

2 Lessons 76 Min

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

Regression project
39 min
Classification project
37 min

Topics

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

Tools & Technologies

Course Requirements

You need to complete an introduction to Python before taking this course
Basic skills in statistics, probability, and linear algebra are required
It is highly recommended to take the Machine Learning in Python course first
You will need to install the Anaconda package, which includes Jupyter Notebook

Who Should Take This Course?

Level of difficulty: Advanced

Aspiring data scientists and ML engineers
Existing data scientists and ML engineers who want to boost their skills and learn from world-class experts

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Jeff Li

Senior Data Scientist at

3 Courses

1820 Reviews

26310 Students

Jeff is a senior data scientist at a large music streaming platform and is focused on forecasting problems surrounding ads. He got into data science by trying to earn a living playing poker and previously spent two years at Door Dash on their core ML team (he was working on a wide variety of problems such as improving experimentation power, personalization, and supply/demand forecasting). In his courses with 365, Jeff is willing to share valuable practical insights he learned on the job. Prior to starting his data science career, Jeff worked in technology consulting. He graduated the University of Southern California.