IBM Introduction to Machine Learning
| Learning Experience | 9.5 |
|---|---|
| Content Rating | 9 |
Build your career skills with the IBM introduction to machine learning specialization, hands-on projects and curriculum brought by IBM’s experts.
About this Course
In the IBM Introduction to Machine Learning specialization, you will 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. Machine learning skills are becoming more and more essential in the modern job market. In 2019, Machine Learning Engineer was ranked as the #1 job in the United States, based on the incredible 344% growth of job openings in the field between 2015 to 2018, and the role’s average base salary of $146,085 (Indeed).
What you will Learn from this course?
- 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.
There are four-courses in the IBM Introduction to Machine Learning Specialization will help you gain the introductory skills to succeed in an in-demand career in machine learning and data science. After completing this program, you’ll be able to realize the potential of machine learning algorithms and artificial intelligence in different business scenarios. You’ll be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You’ll also learn how to evaluate your machine learning models and to incorporate best practices.
By the end of this program, you will have developed concrete machine learning skills to apply in your workplace or career search, as well as a portfolio of projects demonstrating your proficiency. In addition to receiving a certificate from Coursera, you’ll also earn an IBM Badge to help you share your accomplishments with your network and potential employer.
You can also leverage the learning from the program to complete the remaining two courses of the six-course IBM Machine Learning Professional Certificate and power a new career in the field of machine learning.
Syllabus for the IBM Introduction to Machine Learning specialization
There are 4 Courses in this Specialization
Course 1. Exploratory Data Analysis for Machine Learning
This first course in the IBM Introduction to Machine Learning specialization introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.
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 from this course?
To make the most out of this course, you should have familiarity with programming in a Python development environment, as well as a fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.
Course 2. Supervised Learning: Regression
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
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 from this course?
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.
Course 3. Supervised Learning: Classification
This course of the IBM Introduction to Machine Learning specialization introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
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 under sampling 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 from this course?
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.
Course 4. Unsupervised Learning
This course of the IBM Introduction to Machine Learning specialization introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
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 from this course?
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.
Project for IBM Introduction to Machine Learning specialization
In this program, you’ll complete hands-on projects designed to develop your analytical and machine learning skills. You’ll also produce a summary of your insights from each project using data analysis skills, in a similar way as you would in a professional setting, including producing a final presentation to communicate insights to fellow machine learning practitioners, stakeholders, C-suite executives, and chief data officers.
You are highly encouraged to compile your completed projects into an online portfolio that showcases the skills learned in this Specialization.
Note: Your review matters
If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.
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Description
About this Course
In the IBM Introduction to Machine Learning specialization, you will 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. Machine learning skills are becoming more and more essential in the modern job market. In 2019, Machine Learning Engineer was ranked as the #1 job in the United States, based on the incredible 344% growth of job openings in the field between 2015 to 2018, and the role’s average base salary of $146,085 (Indeed).
What you will Learn from this course?
- 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.
There are four-courses in the IBM Introduction to Machine Learning Specialization will help you gain the introductory skills to succeed in an in-demand career in machine learning and data science. After completing this program, you’ll be able to realize the potential of machine learning algorithms and artificial intelligence in different business scenarios. You’ll be able to identify when to use machine learning to explain certain behaviors and when to use it to predict future outcomes. You’ll also learn how to evaluate your machine learning models and to incorporate best practices.
By the end of this program, you will have developed concrete machine learning skills to apply in your workplace or career search, as well as a portfolio of projects demonstrating your proficiency. In addition to receiving a certificate from Coursera, you’ll also earn an IBM Badge to help you share your accomplishments with your network and potential employer.
You can also leverage the learning from the program to complete the remaining two courses of the six-course IBM Machine Learning Professional Certificate and power a new career in the field of machine learning.
Syllabus for the IBM Introduction to Machine Learning specialization
There are 4 Courses in this Specialization
Course 1. Exploratory Data Analysis for Machine Learning
This first course in the IBM Introduction to Machine Learning specialization introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing.
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 from this course?
To make the most out of this course, you should have familiarity with programming in a Python development environment, as well as a fundamental understanding of Calculus, Linear Algebra, Probability, and Statistics.
Course 2. Supervised Learning: Regression
This course introduces you to one of the main types of modeling families of supervised Machine Learning: Regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare across different models. This course also walks you through best practices, including train and test splits, and regularization techniques.
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 from this course?
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.
Course 3. Supervised Learning: Classification
This course of the IBM Introduction to Machine Learning specialization introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
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 under sampling 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 from this course?
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.
Course 4. Unsupervised Learning
This course of the IBM Introduction to Machine Learning specialization introduces you to one of the main types of Machine Learning: Unsupervised Learning. You will learn how to find insights from data sets that do not have a target or labeled variable. You will learn several clustering and dimension reduction algorithms for unsupervised learning as well as how to select the algorithm that best suits your data. The hands-on section of this course focuses on using best practices for unsupervised learning.
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 from this course?
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.
Project for IBM Introduction to Machine Learning specialization
In this program, you’ll complete hands-on projects designed to develop your analytical and machine learning skills. You’ll also produce a summary of your insights from each project using data analysis skills, in a similar way as you would in a professional setting, including producing a final presentation to communicate insights to fellow machine learning practitioners, stakeholders, C-suite executives, and chief data officers.
You are highly encouraged to compile your completed projects into an online portfolio that showcases the skills learned in this Specialization.
Note: Your review matters
If you have already done this course, 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:
- Coursera
- IBM
- Microdegree
- Self-paced
- Intermediate
- 1-3 Months
- Paid Course (Paid certificate)
- English
- Python
- Basic Scripting in Python Fundamentals of calculus Linear Algebra Probability Basics Statistics Basics
- Artificial intelligence Data Analysis Feature Engineering Machine learning Practical Statistics Regression Analysis SQL for Data Science
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