Emad S.
Before 365:
Business Analyst | Snapp!
After 365:
Senior Data Analyst | RBS Global Asset Management
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Skill level:
Advanced
Duration:
5 hours
-
Lessons (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
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
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
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
Course OVERVIEW
Description
CPE Credits: 0.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
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
- 2. Linear Regression24 MinLinear 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 FreeReal World Business Problems FreeExample: Linear Regression FreeIntuition: Linear Regression FreeExercise FreeTraining Step-by-Step: Linear Regression FreeExercise FreePrediction: Linear Regression FreeExercise FreeAssumptions: Linear Regression FreeExercise FreeAssumption #1: Model is linear in coefficients and error terms FreeExercise FreeAssumption #2: Homoscedasticity FreeExercise FreeAssumption #3: Multicollinearity FreeExercise FreeAssumption #4: Independence/Autocorrelation FreeExercise FreeAssumption #5: Normally Distributed Error Terms FreeExercise FreeAssumption #6: Outliers FreeInference - Interpreting Output FreeExercise FreeAB Testing Example FreeExercise FreeML Process: Linear Regression FreePros & Cons, When to Use FreeExercise Free
- 3. Ridge, Lasso, Elastic Net14 MinOne 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 FreeIntuition: Ridge, Lasso, Elastic Net FreePlain Definition: Ridge, Lasso, Elastic Net FreeExercise FreeShrinkage Methods vs. Feature Selection FreeStep-by-Step Intuition: Ridge, Lasso, Elastic Net FreeExercise FreeLasso Regression (L1) FreeRidge Regression (L2) FreeElasticNet (L1 + L2) FreeExercise FreeDetermining the Degree of Regularization FreeDifference between Lasso & Ridge FreeLink to resources FreeWhen to use: Ridge, Lasso, Elastic Net FreeExercise Free
- 4. Logistic Regression22 MinLike linear regression, logistic regression is one of the most powerful and straightforward models.Introduction: Logistic Regression FreeExample: Logistic Regression FreeIntuition: Logistic Regression FreeExercise FreeReal World Business Problems: Logistic Regression FreeExercise FreeWhat is Logit FreeExercise FreeStep-by-Step Prediction: Logistic Regression FreeStep-by-Step Training: Logistic Regression FreeExercise FreeAssumptions: Logistic Regression FreeExercise FreeUnderstanding Logistic Regression Output FreeExercise FreeMaximum Likelihood Explained FreeExercise FreeLog Loss FreeExercise FreePredicting Multiple Classes using Multinomial Logistic Regression FreeExercise FreeML Process: Logistic Regression FreeExercise FreeProsCons, When to Use Free
- 5. Gradient Descent10 MinGradient 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 FreeIntuition: Gradient Descent FreePlain Definition: Gradient Descent FreeExercise FreeStep-by-Step: Gradient Descent FreeExercise FreeAssumptions: Gradient Descent FreeExercise FreeParameter Tuning (Step size, Alpha) FreeGradient Descent Pros and Cons FreeStochastic Gradient Descent FreePros and Cons: Gradient Descent FreeExercise Free
- 6. Decision Trees18 MinDecision 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 FreeExample: Decision Trees FreePlain Explanation: Decision Trees FreeExercise FreeDifferent Components of Decision Trees Explained FreeExercise FreeReal World Business Example: Decision Trees FreeAssumptions: Decision Trees FreeTraining Step-by-Step: Decision Trees FreeExercise FreePrediction Step-by-Step: Decision Trees FreeAdditional Metrics: Decision Trees FreeTuning the Parameters: Decision Trees FreeExercise FreeML Process: Decision Trees FreeDecision Trees Assumptions FreePros and Cons: Decision Trees FreeWhen to Use Decision Trees. FreeExercise Free
- 7. Random Forest16 MinRandom 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 FreeIntuition: Random Forest FreeExample: Random Forest FreeExercise FreeReal World Business Problems: Random Forest FreeExercise FreePlain Definition: Bagging FreeWhere Bagging Fails FreePlain Definition: Random Forest FreeStep-by-Step (Training): Random Forest FreeStep-by-Step (Prediction): Random Forest FreeExercise FreeHow Random Forest give us Feature Importance FreeOut of Bag Error FreeML Process: Random Forest FreeWhen to use: Random Forest FreePros and Cons: Random Forest FreeExercise Free
- 8. Gradient Boosted Trees17 MinGradient-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 FreeExample: Gradient Boosted Trees FreeReal World Business Problems: Gradient Boosted Trees FreePlain Definition: Gradient Boosted Trees FreeExercise FreeTerminology: Gradient Boosted Trees FreeAssumptions: Gradient Boosted Trees FreeTraining Step-by-Step (Regression): Gradient Boosted Trees FreeTraining Step-by-Step (Classification): Gradient Boosted Trees FreeExercise FreePrediction Step-by-Step: Gradient Boosted Trees FreeWhat does the “Gradient” mean FreeHow Gradient Boosted Trees give us Feature Importance FreeML Process: Gradient Boosted Trees FreeWhen to use Gradient Boosted Trees FreeExercise Free
- 9. XGBoost15 MinXGBoost 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.
- 10. K Nearest Neighbors15 MinK-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.
- 11. K-Means Clustering16 MinK-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 FreeExample: K-Means Clustering FreePlain Definition: K-Means Clustering FreeExercise FreeReal World Business Problems: K-Means Clustering FreeStep-by-Step Training: K-Means Clustering FreeSelecting K FreeSilhouette Method FreeExercise FreeHard Clustering vs- Soft Clustering FreeDerivatives of K-Means FreeAssumptions: K-Means Clustering FreeML Process: K-Means Clustering FreeWhen do we use K means Clustering FreeExercise Free
- 12. Hierarchical Clustering10 MinHierarchical 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 FreeReal World Business Problems: Hierarchical Clustering FreeDefinition: Hierarchical Clustering FreeExercise FreeStep-by-Step Agglomerative Clustering FreeLinkages FreeDistance FreeExercise FreeML Process: Hierarchical Clustering FreePros and Cons: Hierarchical Clustering FreeWhen to Use: Hierarchical Clustering FreeExercise Free
- 13. Support Vector Machines18 MinSupport 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 FreeReal World Business Problems: SVM FreeStep-by-Step Training (Non-Technical): SVM FreeExercise FreeLoss Function FreeNonlinear Data FreePrediction (Step-by-Step): SVM FreeTerminology: SVM FreeAssumptions: SVM FreeSoft vs Hard Margins: SVM FreeExercise FreeHow to use SVMs as a multi-class classifier FreeHow does SVM Regression Work FreeML Process: SVM FreePros & Cons (Classifier): SVM FreeWhen to use an SVM Classifier FreeExercise Free
- 14. Artificial Neural Nets37 MinArtificial 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 FreeExercise FreeReal world Business Problems: Artificial Neural Nets FreeExample: Artificial Neural Nets FreeExercise FreeMulti-Layered Networks: Artificial Neural Nets FreeClassification - Activation Layers FreeVanishing Gradient Problem FreeExercise FreeActivation Layers FreeEmbeddings FreeExercise FreeTypes of ANNs FreeExercise FreeTransfer Learning FreeML Process: Artificial Neural Nets FreePros and Cons: Artificial Neural Nets FreeExercise Free
- 15. Collaborative Filtering - Non-Negative Matrix Factorization18 MinCollaborative filtering uses ratings from a group of (collaborative) users to infer the preference of another (filtering) user.Exercise FreeIntuition: Collaborative Filtering FreePlain Definition: Collaborative Filtering FreeReal world Business Problems: Collaborative Filtering FreeExercise FreeAssumptions: Collaborative Filtering FreeDifferent Approaches to Collaborative Filtering FreeMatrix Factorization Intuition FreeMatrix Factorization Definition FreeAssumptions: NMF FreeExercise FreeStep-by-Step (prediction): Collaborative Filtering FreeStep-by-Step (training): Collaborative Filtering FreeDetermining the ideal number of latent variables FreeAddressing the Cold-Start Problem FreeExercise FreeML Process: Collaborative Filtering FreeProsCons: Collaborative Filtering FreeWhen to use NMF FreeExercise Free
- 16. Naïve Bayes19 MinDiscover 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 FreeExercise FreeIntuition and Plain Definition FreeStep-By-Strp Explanation - First Part FreeStep-by-step Explanation - Second Part FreeExercise FreeWhy is Naive Baïve called Naïve? FreeThe types of Naïve Bayes FreeML Process: Naïve Bayes FreePros and Cons: Naïve Bayes FreeReal-Life Business Example FreeWhen to use Naïve Bayes FreeExercise Free
- 17. Practical projects76 MinIn this section you can find practical projects that will help you enhance your skills.
- 18. Course exam45 Min
Free preview
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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