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Mat Leonard, Parnian Barekatain, Eddy Shyu, Brok Bucholtz, Elizabeth Otto Hamel, Cindy Lin, Cezanne Camacho, Arpan Chakraborty, Luis Serrano, and Juan Delgado

What's inside

Syllabus

Linear regression is a very effective algorithm to predict numerical data.
Naive Bayesian Algorithms are powerful tools for creating classifiers for incoming labeled data.
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Clustering is one of the most common methods of unsupervised learning. Here, we'll discuss the K-means clustering algorithm.
Decision trees are a structure for decision-making where each decision leads to a set of consequences or additional decisions.
Learn the intuition behind the Kalman Filter, a vehicle tracking algorithm, and implement a one-dimensional tracker of your own.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Instructors include Mat Leonard, Parnian Barekatain, Eddy Shyu, Brok Bucholtz, Elizabeth Otto Hamel, Cindy Lin, Cezanne Camacho, Arpan Chakraborty, Luis Serrano, and Juan Delgado; some of these instructors may be recognized for their work
Develops core skills for decision-making, pattern recognition, and modeling
Taught by top-tier instructors from the field
Understand the intuition behind the Kalman Filter, a core machine learning algorithm
The course is offered through the provider Udacity

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning with these activities:
Explore Decision Tree Algorithms
Broaden your understanding of decision tree algorithms by exploring different resources and tutorials.
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  • Watch online videos or read articles about decision tree algorithms.
  • Follow along with interactive tutorials to build your own decision trees.
Review Linear Regression
Reinforce your understanding of linear regression by working through practice problems.
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  • Solve practice problems from the course textbook or online resources.
  • Create your own practice problems and solve them.
  • Participate in online forums or discussion groups to discuss linear regression concepts and solve problems with other students.
Peer-to-Peer Tutoring
Strengthen your own understanding while assisting others by participating in peer-to-peer tutoring.
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  • Join a study group or online forum where you can connect with other students.
  • Offer to help classmates with concepts they may be struggling with.
Four other activities
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Implement Naive Bayesian Classifier
Develop a deeper understanding of Naive Bayesian classifiers by implementing one from scratch.
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  • Choose a dataset to work with.
  • Write Python code to implement the Naive Bayesian algorithm.
  • Evaluate the performance of your classifier on the dataset.
Build a K-means Clustering Model
Apply your knowledge of K-means clustering by building a model for a real-world dataset.
Browse courses on K-Means Clustering
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  • Gather and prepare a dataset for clustering.
  • Choose the number of clusters and implement the K-means algorithm in Python.
  • Evaluate the performance of your model using metrics such as the silhouette score or Davies-Bouldin index.
Create a Kalman Filter Simulation
Deepen your understanding of the Kalman filter by implementing a simulation in Python.
Browse courses on Kalman Filter
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  • Learn the theoretical concepts behind the Kalman filter.
  • Write Python code to implement a one-dimensional Kalman filter.
  • Simulate the tracking of a moving object using your Kalman filter.
Contribute to Open Source Machine Learning Projects
Gain practical experience and contribute to the wider community by participating in open source machine learning projects.
Browse courses on Machine Learning
Show steps
  • Identify open source machine learning projects that align with your interests.
  • Read the project documentation and contribute code, documentation, or bug fixes.

Career center

Learners who complete Machine Learning will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. This course can build a foundation in the core algorithms that power nearly every machine learning system. Supervised learning is essential for classification and regression tasks. Unsupervised learning is essential for clustering and anomaly detection. This course also covers the Kalman Filter, which is used for vehicle tracking, robotics, navigation, control systems, and more.
Researcher
Researchers in the field of artificial intelligence will benefit from this course. Supervised learning is commonly used for tasks like classification and regression. Unsupervised learning is commonly used for tasks like clustering, anomaly detection, and dimensionality reduction. Decision trees are commonly used for tasks like decision support systems, predictive modeling, and expert systems.
Statistician
Statisticians use machine learning algorithms for a variety of applications in statistics. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting and risk modeling. Naive Bayes and decision trees are essential for tasks like classification and fraud detection.
Machine Learning Consultant
Machine Learning Consultants help businesses implement machine learning solutions. This course can provide a foundation for understanding the core algorithms that businesses use for machine learning. Supervised learning is commonly used for tasks like classification and regression. Unsupervised learning is commonly used for tasks like clustering, anomaly detection, and dimensionality reduction. Familiarity with these algorithms is essential for success as a Machine Learning Consultant.
Operations Research Analyst
Operations Research Analysts use machine learning algorithms for a variety of applications in operations research. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting and optimization. Naive Bayes and decision trees are essential for tasks like classification and fraud detection.
Financial Analyst
Financial Analysts use machine learning algorithms for a variety of applications in finance. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting and risk modeling. Naive Bayes and decision trees are essential for tasks like credit scoring and fraud detection.
Data Engineer
Data Engineers who work with machine learning systems will benefit from this course. This course will expose one to the core algorithms that power machine learning systems. Supervised learning is essential for tasks like classification and regression. Unsupervised learning is critical for tasks like clustering, anomaly detection, and dimensionality reduction. Decision trees are critical for tasks like decision support systems, predictive modeling, and expert systems.
Actuary
Actuaries use machine learning algorithms for a variety of applications in insurance and finance. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting and risk modeling. Naive Bayes and decision trees are essential for tasks like credit scoring and fraud detection.
Risk Manager
Risk Managers use machine learning algorithms for a variety of applications in risk management. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting and risk modeling. Naive Bayes and decision trees are essential for tasks like fraud detection and credit scoring.
Quant
Quantitative Analysts use machine learning algorithms for a variety of financial applications. This course can provide a foundation for understanding supervised and unsupervised machine learning. Linear regression is essential for tasks like forecasting, risk modeling, and portfolio optimization. Naive Bayes and decision trees are essential for tasks like credit scoring, fraud detection, and market prediction.
Data Analyst
Data Analysts use machine learning algorithms to explore and analyze data. This course can help build a foundation in the core algorithms that provide the basis for data analysis. Supervised learning is essential for classification and regression tasks, both of which are critical for data analysis. Unsupervised learning is also essential for tasks like clustering, anomaly detection, and dimensionality reduction.
Data Scientist
Data Scientists utilize machine learning algorithms to help businesses make informed decisions. This course can help one build a foundation in supervised and unsupervised machine learning. Topics like linear regression, Naive Bayes, clustering, and decision trees are all essential to the toolkit of any Data Scientist. This course may also provide a foundation for understanding more advanced topics like time-series analysis and natural language processing, which are extremely valuable for Data Scientists.
Software Engineer
Software Engineers who work on machine learning systems will benefit from this course. Supervised learning algorithms are commonly used for spam filtering, face detection, and digit recognition. Clustering algorithms are used for customer segmentation, fraud detection, and image recognition. Decision trees are critical for decision support systems, predictive modeling, and expert systems. Familiarity with these algorithms is critical for success as a Software Engineer.
Product Manager
Product Managers who work on machine learning products will benefit from this course. Supervised learning is commonly used for spam filtering, face detection, and digit recognition. Clustering algorithms are used for customer segmentation, fraud detection, and image recognition. Decision trees are critical for decision support systems, predictive modeling, and expert systems. Familiarity with these algorithms is critical for success as a Product Manager.
Business Analyst
Business Analysts use machine learning to improve business and organizational decision making. This course provides exposure to supervised and unsupervised learning, both of which have applications in business analytics. Supervised learning is commonly used for customer churn prediction, lead scoring, and fraud detection. Unsupervised learning is commonly used for customer segmentation, market basket analysis, and anomaly detection.

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning.
Provides a comprehensive overview of probabilistic graphical models, which are a powerful tool for modeling complex data distributions.
Provides a comprehensive overview of machine learning algorithms and techniques, making it a valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of Bayesian reasoning and its applications in machine learning.
Provides a probabilistic foundation for machine learning, offering a different perspective on the field.
Provides a gentle introduction to machine learning for beginners.

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