## Professional Certificate – 6-Course Series Prepare for a career in machine learning. In this program, you will learn essential skills such as artificial intelligence and machine learning to **be job-ready in less than 3 months.** Machine learning is the use and development of computer systems that can learn and adapt themselves using algorithms and statistical models to analyze and draw conclusions from data. **Machine learning is a branch of artificial intelligence (AI)** in which computers are taught to imitate human intelligence and solve complex tasks. Positions available to those with knowledge of machine learning include machine learning engineer, NLP researcher, and data engineer. This program includes courses that provide a solid theoretical understanding and extensive practice in the main algorithms, uses, and whether they lead to machine learning. Topics covered include **supervised and unsupervised learning, regression, classification, clustering, deep learning, and reinforcement learning.** You’ll learn to **code your own projects** using some of the most relevant open-source frameworks and libraries, and apply what you’ve learned across a range of courses by completing a final project. Upon completion of the courses, you’ll have a **portfolio of projects and a professional certificate** from IBM to showcase your expertise. You’ll also receive an IBM digital badge and access to career resources to help you with your job search, including sample interviews and resume support. ### Practical Learning Project This professional certificate emphasizes developing the practical skills needed to advance in a career in machine learning and deep learning. All courses include a series of hands-on labs and final projects to help you focus on a specific project that interests you. During this professional certificate, you will be exposed to a variety of tools, libraries, cloud services, data systems, algorithms, tasks, and projects that will provide you with practical skills for use in 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.
– **Algorithms:** Supervised and unsupervised learning, regression, classification, clustering, linear regression, ridge regression, machine learning (ML) algorithms, decision tree, learning scores, survival analysis, K-means clustering, DBSCAN, and data dimensionality reduction.
Exploratory data analysis for machine learning
Course 1 • 14 hours • 4.6
Course details
What you’ll learn
This course introduces you to machine learning and the content of the professional certificate. During the course, you will understand the importance of quality data. You will learn common techniques for collecting data, cleaning it, using feature engineering, and preparing it for initial analysis and hypothesis testing.
- Collect data from various sources: SQL, NoSQL databases, APIs, cloud
- Describe and use common techniques in feature selection and feature engineering
- Handle category and rating attributes, as well as missing values
- Use a variety of techniques to identify and handle outliers
- Explain why resizing is important and use a variety of resizing techniques.
Who should take this course?
This course is designed for those interested in becoming data scientists and looking to gain practical experience in machine learning and artificial intelligence in business settings.
Skills you will acquire
- Category: Cluster Analysis
- Category: Dimensional reduction
- Category: Unsupervised Learning
- Category: Principal Component Analysis (PCA)
- Category: K Means Cluster
Supervised Machine Learning: Regression
Course 2 • 20 hours • 4.7
Course details
What you’ll learn
This course introduces you to one of the key model types in supervised learning: regression. You will learn how to train regression models to predict continuous outcomes and how to use error metrics to compare different models.
- Distinguish between the uses and applications of classification and regression
- Describe and use linear regression models
- Use regularization regressions: Ridge, LASSO, and Elastic net
Who should take this course?
This course is designed for those interested in becoming data scientists and looking to gain practical experience in supervised machine learning regression techniques in business settings.
Skills you will acquire
- Category: Unsupervised Learning
- Category: Machine Learning (ML) Algorithms
- Category: Supervised Learning
- Category: Classification algorithms
- Category: Decision Tree
Supervised Machine Learning: Classification
Course 3 • 24 hours • 4.8
Course details
What you’ll learn
This course introduces you to one of the key types of supervised machine learning model families: classification. You will learn how to train predictive models to classify categorical outcomes.
- Distinguish between the uses and applications of classification and classification ensembles
- Describe and use logistic regression models
- Use a variety of error metrics to compare and choose the classification model that best fits your data
Who should take this course?
This course is designed for those interested in becoming data scientists and looking to gain practical experience with supervised machine learning classification techniques in business settings.
Skills you will acquire
- Category: Artificial Neural Networks
- Category: Data Analysis
- Category: Python Programming
- Category: Supervised Learning
- Category: Unsupervised Machine Learning
Unsupervised machine learning
Course 4 • 23 hours • 4.7
Course details
What you’ll learn
This course introduces you to one of the key types of learning in machine learning: unsupervised learning. You will learn how to find insights from data sets that do not have a target variable or classifier.
- Explain the types of problems that are suitable for unsupervised learning approaches.
- Describe and use common clustering and dimensionality reduction algorithms
Who should take this course?
This course is designed for those interested in becoming data scientists and looking to gain practical experience with unsupervised learning techniques in business settings.
Skills you will acquire
- Category: Artificial Neural Networks
- Category: Reinforcement Learning
- Category: Machine Learning
- Category: Deep Learning
- Category: keras
Deep learning and reinforcement learning
Course 5 • 31 hours • 4.6
Course details
What you’ll learn
This course introduces you to two of the most sought-after specializations in machine learning: deep learning and reinforcement learning.
- Explain the problem providers suitable for unsupervised learning approaches
- Describe and use common clustering and dimensionality reduction algorithms.
Who should take this course?
This course is designed for those interested in becoming data scientists and looking to gain practical experience in deep learning and reinforcement learning.
Skills you will acquire
- Category: Linear Regression
- Category: Machine Learning (ML) Algorithms
- Category: Ridge Regression
- Category: Supervised Learning
- Category: Regression Analysis
