Data Science and Machine Learning

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Harvard’s Data Science and Machine Learning course: Learn popular ML algorithms and perform regularization by building a movie recommendation system.

Last updated on October 22, 2021 12:29 am

Data Science and Machine Learning

What is Data Science?

What is Data Science and ML Image for EDX Platform

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. Let’s understand what is Data Science and Machine Learning, basically, data science is the process of extracting value from data and it usually requires an understanding of scientific methods and processes.

As with other forms of experiments, data science requires you to make observations, ask questions, form hypotheses, create tests, analyze results, and come up with practical recommendations. So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science), and machine learning.

About Machine Learning

Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

This process of learning always begins with observations or data, such as direct experience, examples, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.

The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. However, using the classic algorithms of machine learning, the text is considered as a sequence of keywords.

“We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The end result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives.”

–Amy Stapleton

About the Data Science and Machine Learning course

This course is a part of the Professional Certificate Program, you will be able to learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

What you will learn from this course

In this course, you will learn about training data, a set of data used to discover potentially predictive relationships and how the data can come in the form of the outcome we want to predict and features that we will use to predict this outcome.

As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

  • In-depth knowledge of fundamental data science concepts through motivating real-world case studies
  • The basics of machine learning
  • How to perform cross-validation to avoid overtraining
  • Several popular machine learning algorithms
  • How to build a recommendation system
  • What is regularization and why it is useful?

Syllabus for the Data Science and Machine Learning course

Week 1

 Introduction to Data Science and Machine Learning

  • Welcome and overview of the course.
  • Introduction to some of the terminology and concepts

Week 2

Basics on Data Science and Machine Learning

  • How to start building a machine learning algorithm using training and test data sets and the importance of conditional probabilities for machine learning.

Week 3

Linear Regression for Prediction, Smoothing and Working with Matrices

  • Why linear regression is a useful baseline approach but is often insufficiently flexible for more complex analyses.
  • How to smooth noisy data
  • How to use matrices for machine learning.

Week 4

Distance, Knn, Cross-Validation, and Generative Models

  • Different types of discriminative and generative approaches for machine learning algorithms.

Week 5

Classification with More than Two Classes and the Caret Package

  • How to overcome the curse of dimensionality using methods that adapt to higher dimensions.
  • How to use the caret package to implement many different machine learning algorithms.

Week 6

Model Fitting and Recommendation Systems

  • How to apply the machine learning algorithms you have learned.

Week 7 and 8

Data Science and Machine Learning Assessment

  • Final Assessment and Course Wrap-Up

Note:

If you have already done this course on data science and machine learning, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

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Verified Certificate at

$99.00

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  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Beginner
  • 1-3 Months
  • Free Course (Affordable Certificate)
  • English
  • R
  • RStudio
  • Recommended Preceding Courses
  • Data Science Machine learning Probability Recommendation Systems Regression Analysis
Learning Experience
8
PROS: Good course with popular data science methodologies You will learn about training data, and use a set of data to discover potentially predictive relationships
CONS: Need to improve assignments Some important algos not included in the Course content (SVM, Boosting)

Description

Data Science and Machine Learning

What is Data Science?

What is Data Science and ML Image for EDX Platform

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data. Let’s understand what is Data Science and Machine Learning, basically, data science is the process of extracting value from data and it usually requires an understanding of scientific methods and processes.

As with other forms of experiments, data science requires you to make observations, ask questions, form hypotheses, create tests, analyze results, and come up with practical recommendations. So, Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science), and machine learning.

About Machine Learning

Machine Learning

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.

This process of learning always begins with observations or data, such as direct experience, examples, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.

The primary aim is to allow the computers to learn automatically without human intervention or assistance and adjust actions accordingly. However, using the classic algorithms of machine learning, the text is considered as a sequence of keywords.

“We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The end result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives.”

–Amy Stapleton

About the Data Science and Machine Learning course

This course is a part of the Professional Certificate Program, you will be able to learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.

What you will learn from this course

In this course, you will learn about training data, a set of data used to discover potentially predictive relationships and how the data can come in the form of the outcome we want to predict and features that we will use to predict this outcome.

As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.

  • In-depth knowledge of fundamental data science concepts through motivating real-world case studies
  • The basics of machine learning
  • How to perform cross-validation to avoid overtraining
  • Several popular machine learning algorithms
  • How to build a recommendation system
  • What is regularization and why it is useful?

Syllabus for the Data Science and Machine Learning course

Week 1

 Introduction to Data Science and Machine Learning

  • Welcome and overview of the course.
  • Introduction to some of the terminology and concepts

Week 2

Basics on Data Science and Machine Learning

  • How to start building a machine learning algorithm using training and test data sets and the importance of conditional probabilities for machine learning.

Week 3

Linear Regression for Prediction, Smoothing and Working with Matrices

  • Why linear regression is a useful baseline approach but is often insufficiently flexible for more complex analyses.
  • How to smooth noisy data
  • How to use matrices for machine learning.

Week 4

Distance, Knn, Cross-Validation, and Generative Models

  • Different types of discriminative and generative approaches for machine learning algorithms.

Week 5

Classification with More than Two Classes and the Caret Package

  • How to overcome the curse of dimensionality using methods that adapt to higher dimensions.
  • How to use the caret package to implement many different machine learning algorithms.

Week 6

Model Fitting and Recommendation Systems

  • How to apply the machine learning algorithms you have learned.

Week 7 and 8

Data Science and Machine Learning Assessment

  • Final Assessment and Course Wrap-Up

Note:

If you have already done this course on data science and machine learning, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.

FAQ

Specification:

  • EDX
  • Harvard University
  • Online Course
  • Self-paced
  • Beginner
  • 1-3 Months
  • Free Course (Affordable Certificate)
  • English
  • R
  • RStudio
  • Recommended Preceding Courses
  • Data Science Machine learning Probability Recommendation Systems Regression Analysis

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Data Science and Machine Learning
Data Science and Machine Learning

$99.00

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