Nilotpal C.
See all reviews
Skill level:
Advanced
Duration:
6 hours
-
Lessons (6 hours)
- Lessons (6 hours)
CPE credits:
7.5
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 real-world skills to deliver machine learning results.
- Master all stages of the machine learning lifecycle.
- Gain hands-on experience in data preprocessing for ML.
- Improve your ML model’s results with advanced feature engineering.
- Manage and execute complete ML projects independently.
Topics & tools
machine learningmodel evaluationdata preprocessingmachine learning processdata modelingdealing with imbalanced datacross validationfeature engineeringexploratory data analysismachine and deep learningpython
Your instructor
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
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
Course OVERVIEW
Description
CPE Credits: 7.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
Data science education focuses too much on the algorithm itself. In reality, we can have only four lines of code and use them for a variety of problems. The heavy lift of an ML model is the end-to-end process. Jeff Li and Ken Jee walk you step-by-step through this process, so you can successfully take your next project from start to finish. You will learn everything you need to know to set your projects up for success. The Machine Learning Process A-Z course gives you a deep understanding of what machine learning really is. It helps you understand when you should and shouldn’t use this powerful tool. Jeff and Ken break down the specifics of the different problems you can encounter and how machine learning is used in specific domains. In the second part of the course, you will learn the entire modeling process. Jeff and Ken show you how to pull real results and make the ML model work for others, not just yourself. You will learn how to perform essential steps like data preprocessing. In this section, they also show you how to deal with null values and outliers. Next, you’ll see how to explore your data to frame your analysis. Additionally, the course deals with some of the visualization techniques that can help you to see the relationships in your data. After that, we go into feature engineering—one of the most important steps for improving your model’s results. That leads to cross-validation and how to handle bias and variance trade-off in your analysis. Finally, the instructors touch briefly on the model tuning process and how to productionize your work and documentation.
Prerequisites
- Basic understanding of machine learning concepts.
Advanced preparation
Curriculum
145 lessons
1 exam
- 2. Intro to Machine Learning15 MinLearn when you should and shouldn’t use machine learning. We teach you when in your organization’s lifecycle to implement ML and what real-life problems you can solve. We also cover the types of ML problems and show you the difference between product analytics vs. data products.
- 3. The Modeling Process8 MinThe most important step in machine learning is not any fancy technique—it’s properly defining the problem. An incorrect definition can result in months—or even years—of wasted resources. That is why, before we can leverage the power of ML, we need a reason to use it. Whether it’s a directive from a manager, a pain point from a stakeholder, or a gap in the business, you need to properly frame the problem first and foremost.
- 4. Data Collection8 MinMost projects won’t give you the data in a neat and tidy .csv file like they do in class or on Kaggle. If you work at a big company, it is likely that you will have plenty of data readily available to you. However, you’ll still need to pull it yourself. In this section, we will talk about data collection techniques from most structured to least structured.
- 5. Data Preprocessing35 MinAfter defining our problem, we need to preprocess our data. Keep in mind that different models have different requirements. For example, linear regression cannot handle null values, whereas some tree-based models can. Additionally, deviant data points can significantly skew the results of some models and not others, e.g., linear models are affected by outliers whereas tree-based models—not as much. In that case, you may want to remove or adjust outliers in order to get the best performance from our models. After all, we want to make sure our data is of the highest quality before we start building our models.Intro FreeDealing with null values FreeTypes of null values FreeMissing values - Coding examples FreeMissing values - Coding portion FreeStrategies for handling null values FreeDealing with outliers FreeOutliers - Coding portion FreeImplications of outliers FreeDetecting outliers FreeCauses of outliers FreeTreating outliers FreeOutliers - Coding examples Free
- 6. Exploratory Data Analysis for ML52 MinExploratory data analysis is an important prerequisite to the modeling process. With EDA, we get a feel for the assumptions so we can choose the best model and get the most accurate results. The reason we perform this step is to understand the shape of our data and the relationships between the variables.Overview FreeWhy you need EDA FreeHow to approach EDA FreeDistributions and single variable plots FreeRelationships and multi variable plots FreeScatterplots FreeCorrelation FreeCorrelation matrix FreeBar charts FreeLine charts FreePivot table FreeHow to generate useful insights FreeBasic EDA - Coding examples FreeBasic EDA - Coding portion (1 of 2) FreeBasic EDA - Coding portion (2 of 2) Free
- 7. Feature Engineering44 MinThe feature engineering step will likely reap the highest accuracy gains in our modeling process—even more than the selection of the algorithm itself. The core principle is to find variables that have predictive power. So, regardless of how complex the feature is or how fancy of a technique, if it does not add predictive power to our model, then it doesn’t do anything for your model. Remember: complexity doesn’t create value. Creative and domain-specific features create value.Intro FreeCategorical features FreeCategorical features - Coding examples FreeCategorical features - Coding portion FreeOne-hot encoding FreeOrdinal encoding FreeFrequency encoding FreeTarget encoding FreeProbability ratio encoding FreeWeight of evidence encoding FreeBinning FreeFeature engineering for continuous variables FreeScaling FreeNormalization FreeMinMax scaling FreeZ-score normalization FreeRobust scaler FreeTypes of transformations FreeLogarithmic transformations FreeExponential transformations FreeSquare root transformations FreeBox-Cox transformations FreeArithmetic interactions FreeBinning FreeCreative features FreeContinuous feature scaling - Coding examples FreeContinuous feature scaling - Coding portion Free
- 8. Cross Validation33 MinCross-validation allows us to test our model on data that the model has not been trained on. If it performs well on this unseen data, we can be more confident that our predictions will generalize well.Cross validation FreeTrain-test-split FreeOverfitting and underfitting FreeBias/Variance tradeoff FreeBias FreeVariance FreeK-fold cross validation FreeLeave one out cross validation FreeTime series cross validation FreeMonte carlo cross validation FreeCross validation - Coding portion FreeCross validation - Coding examples Free
- 9. Feature Selection18 MinFeature selection is the process of reducing the model’s complexity by selecting only the highest signal features in the dataset. Typically, there are three classes of feature selection techniques. Wrapper methods require us to exhaustively search through all features and measure performance. They give us the best accuracy but are the most computationally expensive out of the three. Next, we have filter methods that pick up feature properties through univariate statistics. These tend to be faster and easier to check since they don’t rely on cross-validation. Lastly, the embedded methods are baked into the ML algorithm. We cover these in the companion course, Machine Learning Algorithms.
- 10. Dealing with Imbalanced Data26 MinHow do we deal with imbalance in our data? This is where oversampling, undersampling, and other techniques become useful. We show you how to perform these to optimize your model’s performance.
- 11. Modeling47 MinIn this section of the course, we’ll focus on ML modeling techniques such as the baseline model, model selection, hyperparameter tuning, and assembling.
- 12. Model evaluation53 MinWe can’t measure the value of our model without proper evaluation criteria. Earlier in this course, we touched on two types of Supervised ML problems—regression and classification. Since these problems use different target variables, we use different metrics to evaluate the quality of the model.Model evaluation FreeClassification evaluation FreeClassification metrics - Coding examples FreePrecision and Recall FreeAccuracy, Precision, Recall - Coding portion FreeROC curves FreeROC AUC FreeF1-Score FreePR-AUC FreeClassification metrics - Coding portion FreeLog loss FreeRegression FreeRegression metrics - Coding examples FreeR-squared / Adjusted R-squared FreeMean absolute error FreeRoot mean squared error FreeRegression notebook - Coding portion Free
- 13. Productionization7 MinThe goal of our work is to make it useful to other people. How do we do that? Through something called productionization. It is a little broader than simply deploying the model into your product; rather it is the delivery of our model to our end user in whatever form.
- 14. Conclusion6 MinAs the course goes through different steps of the modeling process, you’re probably wondering, “How am I supposed to remember each step?” The good news is that building an ML model typically follows a very similar process each time: (1) Problem Framing/Business Understanding; (2) Data Cleaning/Preparation; (3) Exploratory Data Analysis; (4) Feature Engineering; (5) Cross-Validation; (6) Modeling; (7) Evaluation
- 15. Course exam40 Min
Free preview
1.1 Introduction
3 min
1.2 ML Process Course - GitHub repository
1 min
1.3 Meet your instructors
1 min
1.4 How to use this course
1 min
1.5 Additional resources
1 min
1.6 Environment setup
1 min
9 in 10
of our graduates landed a new AI & data job
after enrollment
9 in 10
people walk away career-ready
with practical data and AI skills.
94%
of AI and data science graduates
successfully change
or advance their careers.
ACCREDITED certificates
Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global
institutions.
Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities
employers value most.
How it WORKS
Student REVIEWS
