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Our Courses
Introduction to Topic Modelling in R
By the end of this project, you will know how to load and pre-process a data set of text documents by converting the data set into a document feature matrix and reducing it's dimensionality.
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Course by
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Self Paced
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3 hours
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English
Building Deep Learning Models with TensorFlow
The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems.
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Course by
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Self Paced
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7 hours
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English
Interpretable Machine Learning Applications: Part 1
In this 1-hour long project-based course, you will learn how to create interpretable machine learning applications on the example of two classification regression models, decision tree and random forestc classifiers. You will also learn how to explain such prediction models by extracting the most important features and their values, which mostly impact these prediction models. In this sense, the project will boost your career as Machine Learning (ML) developer and modeler in that you will be able to get a deeper insight into the behaviour of your ML model.
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Course by
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3 hours
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English
AI for Medicine
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.
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Course by
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Self Paced
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English
Data Wrangling with Python
This specialization covers various essential topics such as fundamental tools, data collection, data understanding, and data preprocessing. This specialization is designed for beginners, with a focus on practical exercises and case studies to reinforce learning. By mastering the skills and techniques covered in these courses, students will be better equipped to handle the challenges of real-world data analysis. The final project will give students an opportunity to apply what they have learned and demonstrate their mastery of the subject.
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Course by
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English
Machine Learning for Marketing
Understand the structure and techniques used in Machine Learning, Text Mining, and Decision Science for Marketing. Explore the fascinating world of Machine Learning and its transformative applications in marketing. Explain how analytics and decision science approaches for marketing can enhance the quality of marketing decision-making. Foundation in digital marketing analytics to understand the consumer journey, intent, and activity on your business website.
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Course by
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Self Paced
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English
IBM AI Foundations for Business
This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and basic concepts in AI. The second will introduce the technologies and concepts in data science. The third introduces the AI Ladder, which is a framework for understanding the work and processes that are necessary for the successful deployment of AI-based solutions.
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Course by
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Self Paced
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English
Computer Vision Basics
By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence.
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Course by
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13 hours
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English
BigQuery Soccer Data Analysis
This is a self-paced lab that takes place in the Google Cloud console. Learn the fundamentals of writing and executing queries to query soccer data stored in BigQuery tables. In this lab you will learn more fundamentals of sports data science by writing and executing queries to query data stored in BigQuery tables. The emphasis of the lab is to illustrate how the database works and answer some interesting questions related to the following topics in soccer.
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Course by
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1 hour
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English
Natural Language Processing and Capstone Assignment
Welcome to Natural Language Processing and Capstone Assignment. In this course we will begin with an Recognize how technical and business techniques can be used to deliver business insight, competitive intelligence, and consumer sentiment. The course concludes with a capstone assignment in which you will apply a wide range of what has been covered in this specialization.
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Course by
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5 hours
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English
How to Use The IFE-EFE Matrix for Strategic Analysis
In this 1-hour long project-based course, you will be able to analyze your organization and identify your competitive advantage with the Internal Factor Evaluation-External Factor Evaluation (IFE-EFE) matrix. IFE-EFE matrix is strategic management tools used for input stage of strategy formulation. The IFE is focused on the internal dimension of the organization by looking at the strengths and weaknesses. While the EFE is concerned with the external factors by focusing on the opportunities and threats the organization is exposed to.
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Course by
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Self Paced
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4 hours
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English
Machine Learning: Theory and Hands-on Practice with Python
In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems.
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Course by
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Self Paced
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English
Excel to MySQL: Analytic Techniques for Business
Formulate data questions, explore and visualize large datasets, and inform strategic decisions. In this Specialization, you’ll learn to frame business challenges as data questions. You’ll use powerful tools and methods such as Excel, Tableau, and MySQL to analyze data, create forecasts and models, design visualizations, and communicate your insights.
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Course by
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Self Paced
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English
Data Analysis and Visualization with Power BI
This course forms part of the Microsoft Power BI Analyst Professional Certificate. This Professional Certificate consists of a series of courses that offers a good starting point for a career in data analysis using Microsoft Power BI. In this course, you will learn report design and formatting in Power BI, which offers extraordinary visuals for building reports and dashboards. Additionally, you will learn how to use report navigation to tell a compelling, data-driven story in Power BI.
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Course by
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Self Paced
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30 hours
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English
Robotic Process Automation (RPA)
The Robotic Process Automation (RPA) specialization offers comprehensive knowledge and professional-level skills focused on developing and deploying software robots. It starts with the basic concepts of Robotic Process Automation. It builds on these concepts and introduces key RPA Design and Development strategies and methodologies, specifically in the context of UiPath products. A student undergoing the course shall develop the competence to design and develop automation solutions for business processes. This specialization also prepares you for UiPath Certified Professional - UiRPA exam.
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English
Sports Performance Analytics
Sports analytics has emerged as a field of research with increasing popularity propelled, in part, by the real-world success illustrated by the best-selling book and motion picture, Moneyball.
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Course by
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English
DeepLearning.AI TensorFlow Developer
TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models. In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects.
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Course by
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Self Paced
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English
Data Science with R - Capstone Project
In this capstone course, you will apply various data science skills and techniques that you have learned as part of the previous courses in the IBM Data Science with R Specialization or IBM Data Analytics with Excel and R Professional Certificate. For this project, you will assume the role of a Data Scientist who has recently joined an organization and be presented with a challenge that requires data collection, analysis, basic hypothesis testing, visualization, and modeling to be performed on real-world datasets.
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Course by
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Self Paced
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26 hours
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English
Unsupervised Text Classification for Marketing Analytics
Marketing data is often so big that humans cannot read or analyze a representative sample of it to understand what insights might lie within. In this course, learners use unsupervised deep learning to train algorithms to extract topics and insights from text data. Learners walk through a conceptual overview of unsupervised machine learning and dive into real-world datasets through instructor-led tutorials in Python. The course concludes with a major project. This course uses Jupyter Notebooks and the coding environment Google Colab, a browser-based Jupyter notebook environment.
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Course by
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Self Paced
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13 hours
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English
Implementing RPA with Cognitive Automation and Analytics
The explosive growth of Robotic Process Automation (RPA) in the past few years has created a tremendous demand to learn and become skilled in this exciting technology. This four course Specialization is designed to introduce RPA, provide a foundation of the RPA lifecycle--from design to bot deployment--and implement RPA with cognitive automation and analytics. Experienced and novice users and developers of RPA will all benefit from completing this Specialization.
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Course by
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Self Paced
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English
Explainable deep learning models for healthcare - CDSS 3
This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model-specific explanations. State-of-the-art explainability methods such as Permutation Feature Importance (PFI), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP) are explained and applied in time-series classification.
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Course by
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Self Paced
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30 hours
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English
Creating an Interactive KPI Management Dashboard in Tableau
In less than one hour, you will learn how to connect to data, create key performance indicators, create sparkline charts, create a dashboard map, create dual axis charts and put it all together in a well-formatted and interactive dashboard.
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Course by
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Self Paced
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4 hours
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English
Business Analytics
Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data.
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English
Introduction to Machine Learning: Supervised Learning
In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course.
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Course by
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Self Paced
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40 hours
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English
Everyday Excel
This Specialization is for learners wishing to learn Microsoft Excel from beginner level to expert level. The first two courses will teach learners the basics of Excel through the use of dozens of educational screencasts and a series of quizzes and in-application assignments. Finally, in Part 3 (Projects), learners will complete several "real world", somewhat open ended yet guided projects. In the Projects course, special emphasis is placed on dynamic array functions, which are new in Office 365 and have revolutionized the way that worksheet calculations are performed.
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Course by
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English