Machine Learning and AI with Python from edX

It’s time to make a decision: beach or mountains? When choosing where you want to go for vacation, it can be simple. The options may be a or b. From a decision-making standpoint, it’s easy for the brain to process this decision tree. But, what happens when you’re faced with more complex, multifaceted decisions? You might make a comprehensive pro/con list, rank ordering the most important considerations. But, that can take endless amounts of time that you might not have to spare. When parsing through thousands or millions of data points, you and your organization need to tap into a more sophisticated approach.

The solution? Harnessing the power of artificial intelligence (AI) through machine learning to enhance your decision-making processes. Machine learning with Python can not only help organize data, but machines can also be taught to analyze and learn from disparate data sets – forming hypotheses, creating predictions, and improving decisions.

In Machine Learning and AI with Python, you will explore the most basic algorithm as a basis for your learning and understanding of machine learning: decision trees. Developing your core skills in machine learning will create the foundation for expanding your knowledge into bagging and random forests, and from there into more complex algorithms like gradient boosting.

Using real-world cases and sample data sets, you will examine processes, chart your expectations, review the results, and measure the effectiveness of the machine’s techniques.

Throughout the course, you will witness the evolution of the machine learning models, incorporating additional data and criteria – testing your predictions and analyzing the results along the way to avoid overtraining your data, mitigating overfitting and preventing biased outcomes.

Put your data to work through machine learning with Python.

What's inside

Learning objectives

Explore advanced data science challenges through sample data sets, decision trees, random forests, and machine learning models
Train your model to predict the most effective way to handle a problem
Examine machine learning results, recognize data bias in machine learning, and avoid underfitting or overfitting data

Build a foundation for the use of python libraries in machine learning and artificial intelligence, preparing you for future python study
Build on your python experience, preparing you for a career in advanced data science
In this course, you will:

Explore advanced data science challenges through sample data sets, decision trees, random forests, and machine learning models
Train your model to predict the most effective way to handle a problem
Examine machine learning results, recognize data bias in machine learning, and avoid underfitting or overfitting data
Build a foundation for the use of python libraries in machine learning and artificial intelligence, preparing you for future python study
Build on your python experience, preparing you for a career in advanced data science
In this course, you will:

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches machine learning with Python, which is standard in industry

Provides hands on labs, which allows learners to implement learned concepts

Builds a strong foundation for beginners in machine learning

Examines decision trees, a machine learning algorithm, which is useful for making complex decisions

Develops core skills in machine learning, preparing learners for further study

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 and AI with Python with these activities:

Review and organize course materials

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Ensure you have a solid grasp of course materials by reviewing and organizing them.

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Organize your notes, assignments, and readings
Summarize key concepts and highlight important points
Create a study guide or outline for future reference

Review statistics and probability concepts

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Strengthen your foundation by reviewing statistics and probability, which are essential for machine learning.

Browse courses on Statistics

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Review concepts of probability distributions, hypothesis testing, and statistical inference
Practice solving probability and statistics problems

Review basic data science concepts

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Review core data science principles to strengthen your foundation for this course.

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Revisit fundamental data science concepts such as data types, data structures, and data manipulation techniques.
Brush up on key Python libraries for data science, such as NumPy and Pandas.
Review basic machine learning algorithms, including linear regression, logistic regression, and decision trees.
Complete practice exercises or tutorials to reinforce your understanding.

Ten other activities

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Refresh Python basics

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Brush up on your Python skills to ensure you have a strong foundation for this course.

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Review Python syntax
Practice writing and running simple Python programs

Attend group study sessions

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Engage with peers to deepen your understanding and exchange perspectives.

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Find or create a study group with fellow learners in the course.
Meet regularly to discuss course concepts, solve problems, and share insights.

Participate in peer-led study sessions

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Engage with fellow learners to clarify concepts, discuss ideas, and reinforce your understanding.

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Join or form a study group
Work together to review course materials
Engage in discussions and problem-solving

Solve data analysis coding challenges

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Engage in targeted practice to refine your data analysis skills.

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Find online coding challenges or exercises related to data analysis.
Attempt to solve the challenges using Python, focusing on efficient data processing and analysis techniques.
Review solutions and identify areas for improvement.

Guided tutorials on decision trees and machine learning models

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Enhance your understanding of decision trees and machine learning models through guided tutorials.

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Follow tutorials on decision tree algorithms
Explore tutorials on building and evaluating machine learning models
Practice implementing decision trees and machine learning models in Python

Create a visual representation of machine learning concepts

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Enhance your understanding and recall by creating a visual representation of key machine learning concepts.

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Choose a specific machine learning concept
Design a visual representation, such as a diagram, flowchart, or infographic
Explain the concept clearly and concisely using visuals

Practice drills on decision tree and machine learning algorithms

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Solidify your skills by completing drills and exercises on decision trees and machine learning algorithms.

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Solve problems involving decision tree algorithms
Practice building and evaluating machine learning models using different algorithms
Analyze results and identify areas for improvement

Explore advanced machine learning techniques

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Expand your knowledge of machine learning by delving into specialized topics.

Browse courses on Machine Learning

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Identify areas of machine learning you wish to deepen your understanding in, such as natural language processing or computer vision.
Seek out online tutorials or courses that cover these advanced topics.
Follow the tutorials and complete accompanying exercises to grasp the concepts.

Develop a machine learning model using Python

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Apply your knowledge by creating a machine learning model that addresses a specific problem.

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Identify a problem that can be solved using machine learning
Gather and prepare the necessary data
Build and train a machine learning model
Evaluate the performance of the model and make necessary adjustments

Build a machine learning model for a real-world problem

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Apply your skills to a practical problem to gain hands-on experience.

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Identify a real-world problem that can be addressed with machine learning.
Gather and clean the necessary data.
Choose and train a suitable machine learning model.
Evaluate the performance of your model and make improvements as needed.
Present your findings in a clear and concise manner.

Career center

Learners who complete Machine Learning and AI with Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists build machine learning models and improve them over time. This course can help build a foundation in AI and machine learning using Python. Decision trees and random forests are foundational AI algorithms that Data Scientists use every day to analyze data, perform predictions, and improve business outcomes. This course can help you develop the skills necessary to enter or advance in the exciting field of Data Science.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers develop machine learning models and ML-powered systems in a production environment. This course can help you develop a solid foundation in the fundamentals of ML, including decision trees, random forests, and model evaluation. Additionally, the use of Python libraries for machine learning will provide you with a valuable skill set for a career in Machine Learning Engineering.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Engineer

Artificial Intelligence Engineers develop and maintain AI-powered systems, using machine learning, natural language processing, and computer vision. This course will provide a solid foundation in machine learning algorithms like decision trees and random forests, as well as the use of Python libraries in AI development. These skills are highly sought after in the growing field of AI Engineering.

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Business Intelligence Analyst

Business Intelligence Analysts help businesses make better decisions by analyzing data. This course can help prepare you for a career in Business Intelligence by providing a foundation in decision trees and random forests, which are important algorithms for analyzing complex data. Additionally, the use of Python for machine learning will provide you with in-demand technical skills.

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Data Analyst

Data Analysts collect, organize, and interpret data to help businesses make better decisions. This course can provide you with a solid foundation in machine learning algorithms like decision trees and random forests, which are essential for analyzing and interpreting data. Additionally, the use of Python for machine learning will give you in-demand technical skills.

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Risk Analyst

Risk Analysts identify and assess risks. This course can help build a foundation in machine learning and AI using Python, which are becoming increasingly important in risk analysis. Decision trees and random forests are important algorithms for making predictions and analyzing data, and this course will provide you with a strong foundation in these techniques.

See salaries and explore the career path for Risk Analyst

Product Manager

Product Managers develop and manage products. This course can help build a foundation in machine learning and AI using Python, which are becoming increasingly important in product management. Decision trees and random forests are important algorithms for making predictions and analyzing data, and this course will provide you with a strong foundation in these techniques.

See salaries and explore the career path for Product Manager

Consultant

Consultants provide advice to businesses on a variety of topics. This course can help build a foundation in machine learning and AI using Python, which are becoming increasingly important in consulting. Decision trees and random forests are important algorithms for making predictions and analyzing data, and this course will provide you with a strong foundation in these techniques.

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Data Engineer

Data Engineers design, build, and maintain data infrastructure. This course can help build a foundation in machine learning and AI using Python, which are becoming increasingly important in data engineering. Decision trees and random forests are important algorithms for making predictions and analyzing data, and this course will provide you with a strong foundation in these techniques.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can help build a foundation in machine learning and AI using Python, which are becoming increasingly important in quantitative finance. Decision trees and random forests are important algorithms for making predictions and analyzing data, and this course will provide you with a strong foundation in these techniques.

See salaries and explore the career path for Quantitative Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk. This course can help build a foundation in machine learning and AI using Python, which are becoming increasingly important in actuarial science. Decision trees and random forests are important algorithms for making predictions and analyzing data, and this course will provide you with a strong foundation in these techniques.

See salaries and explore the career path for Actuary

Market Research Analyst

Market Research Analysts conduct research to understand consumer behavior. This course can help build a foundation in machine learning and AI using Python, which are becoming increasingly important in market research. Decision trees and random forests are important algorithms for making predictions and analyzing data, and this course will provide you with a strong foundation in these techniques.

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Software Engineer

Software Engineers design, develop, and maintain software systems. This course can help build a foundation in machine learning and AI using Python, which are becoming increasingly important in software development. Decision trees and random forests are important algorithms for making predictions and analyzing data, and this course will provide you with a strong foundation in these techniques.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course can help build a foundation in machine learning and AI using Python, which are becoming increasingly important in operations research. Decision trees and random forests are important algorithms for making predictions and analyzing data, and this course will provide you with a strong foundation in these techniques.

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Financial Analyst

Financial Analysts use financial data to make investment recommendations. This course can help build a foundation in machine learning and AI using Python, which are becoming increasingly important in financial analysis. Decision trees and random forests are important algorithms for making predictions and analyzing data, and this course will provide you with a strong foundation in these techniques.

See salaries and explore the career path for Financial Analyst