IBM Machine Learning Professional Certificate
| Learning Experience | 9.3 |
|---|---|
| Content Rating | 9 |
The IBM Machine Learning certification provides you with solid theoretical understanding and considerable practice of the main algorithms.
About this Course
Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. In the IBM Machine learning certification program you will be able to get hands-pn practice in specialized topics such as Time Series Analysis and Survival Analysis. Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn).
This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis.
This program consists of 6 courses providing you with a solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning. You will follow along and code your own projects using some of the most relevant open-source frameworks and libraries.
Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, an interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning.
Syllabus
There are 6 Courses in the IBM Machine learning Professional Certification:
Course 1. IBM Machine Learning: Exploratory Data Analysis for Machine Learning
This first course in the IBM Machine Learning Professional Certificate which 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 on a Python development environment, as well as 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 modelling 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 a fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
Course 3. Supervised Learning: Classification
This course of the IBM Machine learning certification 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
- To 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 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 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.
Course 5. Deep Learning and Reinforcement Learning
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.
After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement 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 Deep Learning and Reinforcement Learning.
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, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.
Course 6. Specialized Models: Time Series and Survival Analysis
This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find analyze data with a time component and censored data that needs outcome inference. You will learn a few techniques for Time Series Analysis and Survival Analysis. The hands-on section of this course focuses on using best practices and verifying assumptions derived from Statistical Learning.
By the end of this course you should be able to:
- Identify common modeling challenges with time series data
- Explain how to decompose Time Series data: trend, seasonality, and residuals
- Explain how autoregressive, moving average, and ARIMA models work
- Understand how to select and implement various Time Series models
- Describe hazard and survival modeling approaches
- Identify types of problems suitable for survival analysis
Who should take this course?
This course targets aspiring data scientists interested in acquiring hands-on experience with Time Series Analysis and Survival Analysis.
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, Supervised Machine Learning, Unsupervised Machine Learning, Probability, and Statistics.
Project for IBM Machine Learning
This Professional Certificate has a strong emphasis on developing the skills that help you advance a career in Machine Learning. All the courses include a series of hands-on labs and final projects that help you focus on a specific project that interests you. Throughout this Professional Certificate, you will gain exposure to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will provide you with practical skills with applicability to Machine Learning jobs. These skills include:
Tools: Jupyter Notebooks and Watson Studio
Libraries: Pandas, NumPy, Matplotlib, Seaborn, ipython-sql, Scikit-learn, ScipPy, Keras, and TensorFlow.
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
Machine Learning, Time Series & Survival Analysis. Develop working skills in the main areas of Machine Learning: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. In the IBM Machine learning certification program you will be able to get hands-pn practice in specialized topics such as Time Series Analysis and Survival Analysis. Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn).
This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis.
This program consists of 6 courses providing you with a solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning. You will follow along and code your own projects using some of the most relevant open-source frameworks and libraries.
Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, an interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning.
Syllabus
There are 6 Courses in the IBM Machine learning Professional Certification:
Course 1. IBM Machine Learning: Exploratory Data Analysis for Machine Learning
This first course in the IBM Machine Learning Professional Certificate which 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 on a Python development environment, as well as 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 modelling 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 a fundamental understanding of Data Cleaning, Exploratory Data Analysis, Calculus, Linear Algebra, Probability, and Statistics.
Course 3. Supervised Learning: Classification
This course of the IBM Machine learning certification 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
- To 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 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 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.
Course 5. Deep Learning and Reinforcement Learning
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.
After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement 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 Deep Learning and Reinforcement Learning.
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, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.
Course 6. Specialized Models: Time Series and Survival Analysis
This course introduces you to additional topics in Machine Learning that complement essential tasks, including forecasting and analyzing censored data. You will learn how to find analyze data with a time component and censored data that needs outcome inference. You will learn a few techniques for Time Series Analysis and Survival Analysis. The hands-on section of this course focuses on using best practices and verifying assumptions derived from Statistical Learning.
By the end of this course you should be able to:
- Identify common modeling challenges with time series data
- Explain how to decompose Time Series data: trend, seasonality, and residuals
- Explain how autoregressive, moving average, and ARIMA models work
- Understand how to select and implement various Time Series models
- Describe hazard and survival modeling approaches
- Identify types of problems suitable for survival analysis
Who should take this course?
This course targets aspiring data scientists interested in acquiring hands-on experience with Time Series Analysis and Survival Analysis.
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, Supervised Machine Learning, Unsupervised Machine Learning, Probability, and Statistics.
Project for IBM Machine Learning
This Professional Certificate has a strong emphasis on developing the skills that help you advance a career in Machine Learning. All the courses include a series of hands-on labs and final projects that help you focus on a specific project that interests you. Throughout this Professional Certificate, you will gain exposure to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will provide you with practical skills with applicability to Machine Learning jobs. These skills include:
Tools: Jupyter Notebooks and Watson Studio
Libraries: Pandas, NumPy, Matplotlib, Seaborn, ipython-sql, Scikit-learn, ScipPy, Keras, and TensorFlow.
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
- Professional Certificate
- Self-paced
- Intermediate
- 3+ Months
- Paid Course (Paid certificate)
- English
- Python
- Jupyter Notebook Watson Studio
- Basic Scripting in Python Fundamentals of calculus Linear Algebra Probability Basics Statistics Basics
- Artificial intelligence Data Analysis Data Science Data Science with 'Python' Deep learning Machine learning Regression Analysis Reinforcement learning SQL for Data Science
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