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Stacey McBrine
There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more. Adding all of these algorithms to your skillset is crucial for selecting the best tool for the job.
Read more
There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more. Adding all of these algorithms to your skillset is crucial for selecting the best tool for the job.
Read more
There are numerous types of machine learning algorithms, each of which has certain characteristics that might make it more or less suitable for solving a particular problem. Decision trees and support-vector machines (SVMs) are two examples of algorithms that can both solve regression and classification problems, but which have different applications. Likewise, a more advanced approach to machine learning, called deep learning, uses artificial neural networks (ANNs) to solve these types of problems and more. Adding all of these algorithms to your skillset is crucial for selecting the best tool for the job.
This fourth and final course within the Certified Artificial Intelligence Practitioner (CAIP) professional certificate continues on from the previous course by introducing more, and in some cases, more advanced algorithms used in both machine learning and deep learning. As before, you'll build multiple models that can solve business problems, and you'll do so within a workflow.
Ultimately, this course concludes the technical exploration of the various machine learning algorithms and how they can be used to build problem-solving models.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Build Decision Trees and Random Forests
You've built machine learning models from fundamental linear regression and classification algorithms. These algorithms can get you pretty far in many scenarios, but they are not the only algorithms that can meet your needs. In this module, you'll build machine learning models from decision trees and random forests, two alternative approaches to solving regression and classification problems.
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Syllabus
Build Decision Trees and Random Forests
You've built machine learning models from fundamental linear regression and classification algorithms. These algorithms can get you pretty far in many scenarios, but they are not the only algorithms that can meet your needs. In this module, you'll build machine learning models from decision trees and random forests, two alternative approaches to solving regression and classification problems.
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Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops more advanced machine learning algorithms, a valuable skillset for data scientists
Provides hands-on experience through labs and interactive materials
Suitable for intermediate learners looking to build on current skills
Provides advanced learning in machine learning, suitable for professionals in the field
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Reviews summary
Advanced ai models: comprehensive & practical
According to students, this course offers a strong foundational understanding of advanced machine learning and deep learning algorithms. Learners particularly appreciate the hands-on approach to building models like Decision Trees, SVMs, and various Neural Networks (MLP, CNN, RNN), which helps solidify theoretical concepts. Many found the practical project at the end to be highly beneficial for applying learned skills to business problems. However, some learners noted that due to the broad scope of topics, the depth of coverage for each algorithm could sometimes feel limited, potentially requiring additional self-study for mastery. The course is generally considered well-structured and a valuable continuation for those progressing through the CAIP certificate.
Best for learners with existing machine learning basics.
"Coming from the previous course in the certificate, the pacing felt appropriate, but new learners might find it steep."
"I found this course built well on foundational concepts, but I recommend ensuring you have a solid grasp of ML basics beforehand."
"It's clearly designed as a continuation, so be prepared with your prior ML knowledge; it doesn't re-explain everything."
Instructor's explanations simplify complex concepts effectively.
"The instructor did an excellent job of breaking down complex topics like ANNs into understandable segments."
"I found the explanations for SVMs particularly clear, which helped me grasp a concept I previously struggled with."
"The way concepts were presented made even the more advanced neural network architectures seem approachable."
Explores a wide range of advanced ML/DL algorithms.
"I appreciated the course's attempt to cover such a wide array of advanced algorithms from decision trees to CNNs and RNNs."
"It's a great overview of different machine learning and deep learning models, giving me a solid starting point for each."
"The scope of algorithms covered, including SVMs and neural networks, provided a good foundation for understanding modern AI."
Emphasizes building models for real-world problems.
"The hands-on coding and projects are the strongest part of the course for me, making theoretical concepts concrete."
"I learned how to apply these algorithms to practical scenarios, which is crucial for my professional development."
"Building multiple models throughout the course really helped solidify my understanding and practical skills."
Some advanced topics may lack sufficient depth.
"While broad, I felt some algorithms like CNNs or RNNs could use more in-depth coverage, especially on advanced architectures or tuning."
"For someone looking for a deep dive into neural network optimization, this course might feel a bit too high-level."
"I wish there were more time spent on hyperparameter tuning and fine-tuning for the deep learning models introduced."
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 Build Decision Trees, SVMs, and Artificial Neural Networks with these
activities:
Review Linear Regression and Classification Algorithms
Show steps
Reinforce your knowledge of linear regression and classification algorithms, which will serve as a strong foundation for understanding more advanced algorithms in this course.
Browse courses on
Linear Regression
Show steps
-
Review the course notes on linear regression and classification algorithms
-
Complete the practice exercises in the course module on linear regression and classification algorithms
Review book: Deep Learning with Python
Show steps
Reinforce your understanding of deep learning algorithms and reinforce your understanding of deep learning algorithms used in this course.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read chapters 1-3 of the book
-
Summarize the key concepts of each chapter
-
Complete the practice exercises at the end of each chapter
Practice Decision Tree and Random Forest Algorithms
Show steps
Practice using decision tree and random forest algorithms to solve regression and classification problems.
Browse courses on
Decision Trees
Show steps
-
Complete the practice exercises in the course module on Decision Trees and Random Forests
-
Find additional practice exercises online or in textbooks
-
Implement a Decision Tree or Random Forest algorithm from scratch in a programming language of your choice
Four other activities
Expand to see all activities and additional details
Show all seven activities
Start a Project on Object Detection Using Deep Learning
Show steps
Further develop your project building skills by initiating a project on object detection, solidifying your grasp of this specific deep learning application.
Browse courses on
Object Detection
Show steps
-
Gather a dataset of images with objects of interest
-
Choose a deep learning model for object detection, such as YOLO or Faster R-CNN
-
Train the model on the dataset
-
Deploy the model to an application
Follow tutorial on Convolutional Neural Networks (CNNs)
Show steps
Expand your knowledge of convolutional neural networks, particularly for computer vision tasks.
Browse courses on
Convolutional Neural Networks
Show steps
-
Find a tutorial on CNNs, such as the one provided by TensorFlow
-
Follow the steps in the tutorial to build a CNN model
-
Test your model on a dataset of images
Create a presentation on Deep Learning Applications
Show steps
Expand your understanding of deep learning applications, particularly in natural language processing.
Browse courses on
Deep Learning Applications
Show steps
-
Identify an application of deep learning that interests you, such as natural language processing
-
Research the topic and gather information on how deep learning is used in that application
-
Create a presentation that explains the application and how deep learning is used to solve problems in that domain
Build a Deep Learning Model for a Real-World Problem
Show steps
Solidify your understanding of the course material by applying your knowledge to a real-world problem.
Show steps
-
Identify a real-world problem that can be solved using deep learning
-
Gather data and pre-process it
-
Build a deep learning model to solve the problem
-
Evaluate the performance of your model
Review Linear Regression and Classification Algorithms
Show steps
Reinforce your knowledge of linear regression and classification algorithms, which will serve as a strong foundation for understanding more advanced algorithms in this course.
Browse courses on
Linear Regression
Show steps
Show steps
Show steps
- Review the course notes on linear regression and classification algorithms
- Complete the practice exercises in the course module on linear regression and classification algorithms
Review book: Deep Learning with Python
Show steps
Reinforce your understanding of deep learning algorithms and reinforce your understanding of deep learning algorithms used in this course.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3 of the book
- Summarize the key concepts of each chapter
- Complete the practice exercises at the end of each chapter
Practice Decision Tree and Random Forest Algorithms
Show steps
Practice using decision tree and random forest algorithms to solve regression and classification problems.
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Complete the practice exercises in the course module on Decision Trees and Random Forests
- Find additional practice exercises online or in textbooks
- Implement a Decision Tree or Random Forest algorithm from scratch in a programming language of your choice
Four other activities
Expand to see all activities and additional details
Show all seven activities
Start a Project on Object Detection Using Deep Learning
Show steps
Further develop your project building skills by initiating a project on object detection, solidifying your grasp of this specific deep learning application.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Gather a dataset of images with objects of interest
- Choose a deep learning model for object detection, such as YOLO or Faster R-CNN
- Train the model on the dataset
- Deploy the model to an application
Follow tutorial on Convolutional Neural Networks (CNNs)
Show steps
Expand your knowledge of convolutional neural networks, particularly for computer vision tasks.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Find a tutorial on CNNs, such as the one provided by TensorFlow
- Follow the steps in the tutorial to build a CNN model
- Test your model on a dataset of images
Create a presentation on Deep Learning Applications
Show steps
Expand your understanding of deep learning applications, particularly in natural language processing.
Browse courses on
Deep Learning Applications
Show steps
Show steps
Show steps
- Identify an application of deep learning that interests you, such as natural language processing
- Research the topic and gather information on how deep learning is used in that application
- Create a presentation that explains the application and how deep learning is used to solve problems in that domain
Build a Deep Learning Model for a Real-World Problem
Show steps
Solidify your understanding of the course material by applying your knowledge to a real-world problem.
Show steps
Show steps
Show steps
- Identify a real-world problem that can be solved using deep learning
- Gather data and pre-process it
- Build a deep learning model to solve the problem
- Evaluate the performance of your model
Career center
Learners who complete Build Decision Trees, SVMs, and Artificial Neural Networks will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist is responsible for collecting, analyzing, and interpreting data. They use this information to develop models and make predictions that can help businesses make better decisions. This course can help you build the skills you need to become a successful Data Scientist. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills are essential for Data Scientists who want to develop accurate and reliable models.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, builds, and maintains machine learning models. They work closely with Data Scientists to develop models that can solve real-world problems. This course can help you build the skills you need to become a successful Machine Learning Engineer. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills are essential for Machine Learning Engineers who want to develop accurate and reliable models.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops, and maintains artificial intelligence systems. They work on a variety of projects, including natural language processing, computer vision, and robotics. This course can help you build the skills you need to become a successful Artificial Intelligence Engineer. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills are essential for Artificial Intelligence Engineers who want to develop intelligent systems that can solve complex problems.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data. They use this information to help businesses make better decisions. This course can help you build the skills you need to become a successful Data Analyst. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills are essential for Data Analysts who want to develop accurate and reliable models that can help businesses make better decisions.
Business Analyst
Business Analyst
A Business Analyst helps businesses improve their performance by identifying and solving problems. They use a variety of techniques, including data analysis, process mapping, and stakeholder interviews. This course can help you build the skills you need to become a successful Business Analyst. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills can help you develop insights that can help businesses make better decisions.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical techniques to analyze financial data. They use this information to make investment recommendations. This course can help you build the skills you need to become a successful Quantitative Analyst. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills can help you develop accurate and reliable models that can help you make better investment decisions.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical techniques to solve business problems. They work on a variety of projects, including supply chain management, logistics, and healthcare. This course can help you build the skills you need to become a successful Operations Research Analyst. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills can help you develop accurate and reliable models that can help you solve complex business problems.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They work on a variety of projects, including web applications, mobile apps, and enterprise software. This course can help you build the skills you need to become a successful Software Engineer. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills can help you develop innovative and reliable software applications.
Robotics Engineer
Robotics Engineer
A Robotics Engineer designs, builds, and maintains robots. They work on a variety of projects, including industrial robots, medical robots, and autonomous vehicles. This course can help you build the skills you need to become a successful Robotics Engineer. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills can help you develop intelligent robots that can solve complex problems.
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer designs, develops, and maintains computer vision systems. They work on a variety of projects, including image recognition, object detection, and tracking. This course can help you build the skills you need to become a successful Computer Vision Engineer. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills can help you develop accurate and reliable computer vision systems that can solve complex problems.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer designs, develops, and maintains natural language processing systems. They work on a variety of projects, including machine translation, text summarization, and speech recognition. This course can help you build the skills you need to become a successful Natural Language Processing Engineer. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills can help you develop accurate and reliable natural language processing systems that can solve complex problems.
Research Scientist
Research Scientist
A Research Scientist conducts research in a variety of fields, including computer science, engineering, and medicine. They use a variety of techniques, including machine learning, to develop new technologies and solve complex problems. This course can help you build the skills you need to become a successful Research Scientist. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills can help you develop innovative and reliable technologies that can solve complex problems.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains data pipelines. They work on a variety of projects, including data warehousing, data mining, and data visualization. This course can help you build the skills you need to become a successful Data Engineer. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills can help you develop efficient and reliable data pipelines that can handle large amounts of data.
Database Administrator
Database Administrator
A Database Administrator designs, builds, and maintains databases. They work on a variety of projects, including database design, database optimization, and database security. This course may be useful for you if you want to become a Database Administrator. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills can help you develop efficient and reliable databases.
IT Architect
IT Architect
An IT Architect designs, builds, and maintains IT systems. They work on a variety of projects, including network design, server virtualization, and cloud computing. This course may be useful for you if you want to become an IT Architect. You'll learn how to build machine learning models, including decision trees, support-vector machines, and artificial neural networks. These skills can help you develop innovative and reliable IT systems.
For more career information including salaries, visit:
OpenCourser.com/course/1nfw4w/build
Reading list
We've selected 11 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
Build Decision Trees, SVMs, and Artificial Neural Networks.
Save
Comprehensive guide to deep learning, covering the latest research and techniques. It valuable resource for anyone who wants to learn more about deep learning, regardless of their background.
Practical guide to machine learning using Python. It covers a wide range of topics, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn more about machine learning in practice.
Provides a probabilistic perspective on machine learning. It covers a wide range of topics, from Bayesian inference to deep learning. It valuable resource for anyone who wants to learn more about the theoretical foundations of machine learning.
Comprehensive guide to pattern recognition and machine learning. It covers a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for anyone who wants to learn more about pattern recognition and machine learning.
Practical guide to machine learning for hackers. It covers a wide range of topics, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn more about machine learning in practice.
Is an introduction to machine learning. It covers a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for anyone who wants to learn more about the basics of machine learning.
Practical guide to deep learning using Python. It covers a wide range of topics, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn more about deep learning in practice.
Is an introduction to data science for business. It covers a wide range of topics, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn more about data science in the context of business.
Save
Is an introduction to machine learning for business. It covers a wide range of topics, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn more about machine learning in the context of business.
Practical guide to deep learning using Fastai and PyTorch. It covers a wide range of topics, from data preprocessing to model evaluation. It valuable resource for anyone who wants to learn more about deep learning in practice.
Save
Is an algorithmic perspective on machine learning. It covers a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for anyone who wants to learn more about the algorithmic foundations of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1nfw4w/build
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Effort
4 weeks of study, 2–3 hours per week
Level
Intermediate
Via
Coursera
Institution
CertNexus
Instructor
Stacey McBrine
Language
English
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This course belongs to a
collection
called
CertNexus Certified Artificial Intelligence Practitioner. It's comprised of five courses:
- Build Regression, Classification, and Clustering Models
- Build Decision Trees, SVMs, and Artificial Neural Networks (viewing)
- Solve Business Problems with AI and Machine Learning
- Follow a Machine Learning Workflow
- Preparing for Your CertNexus Certification Exam
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