IBM Introduction to Machine Learning Specialization

Learn machine learning through real use cases. Build the skills for a career in one of the most relevant fields of modern AI through hands-on projects and curriculum from IBM’s experts.

Instructors: Xintong Li +4 more

What you'll learn

  •   Understand the potential applications of machine learning
  •   Gain technical skills like SQL, machine learning modelling, supervised and unsupervised learning, regression, and classification.
  •   
  •   Identify opportunities to leverage machine learning in your organization or career
  •   Communicate findings from your machine learning projects to experts and non-experts

    • Skills you'll gain

  •   Machine Learning
  •   Unsupervised Learning
  •   Regression Analysis
  •   Applied Machine Learning
  •   Predictive Modeling
  •   Statistical Analysis
  •   Classification And Regression Tree (CART)
  •   Feature Engineering
  •   Machine Learning Algorithms
  •   Statistical Modeling
  •   Data Processing
  •   Statistical Inference

    • Specialization - 4 course series

    You are highly encouraged to compile your completed projects into an online portfolio that showcases the skills learned in this Specialization.

    By the end of this course you should be able to: Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud  Describe and use common feature selection and feature engineering techniques Handle categorical and ordinal features, as well as missing values Use a variety of techniques for detecting and dealing with outliers Articulate why feature scaling is important and use a variety of scaling techniques   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Machine Learning and Artificial Intelligence in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.

    By the end of this course you should be able to: Differentiate uses and applications of classification and regression in the context of supervised machine learning  Describe and use linear regression models Use a variety of error metrics to compare and select a linear regression model that best suits your data Articulate why regularization may help prevent overfitting Use regularization regressions: Ridge, LASSO, and Elastic net   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience  with Supervised Machine Learning Regression techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

    By the end of this course you should be able to: -Differentiate uses and applications of classification and classification ensembles -Describe and use logistic regression models -Describe and use decision tree and tree-ensemble models -Describe and use other ensemble methods for classification -Use a variety of error metrics to compare and select the classification model that best suits your data -Use oversampling and undersampling as techniques to handle unbalanced classes in a data set   Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Supervised Machine Learning Classification techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

    By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Unsupervised Machine Learning techniques in a business setting.   What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.

    Supervised Machine Learning: Regression

    Supervised Machine Learning: Classification

    Unsupervised Machine Learning

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