About this Course
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Course 2 of 6 in the

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Flexible deadlines

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Intermediate Level

Approx. 10 hours to complete

Suggested: 24 hours/week...

English

Subtitles: English

Skills you will gain

Data ScienceArtificial Intelligence (AI)Machine LearningBig DataSpark

Course 2 of 6 in the

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 10 hours to complete

Suggested: 24 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Week 1: Introduction

6 videos (Total 44 min), 5 readings, 2 quizzes
6 videos
What is Big Data?11m
Data storage solutions5m
Parallel data processing strategies of Apache Spark7m
Functional programming basics6m
Resilient Distributed Dataset and DataFrames - ApacheSparkSQL6m
5 readings
Course Syllabus10m
Setup of the grading and exercise environment10m
Exercise 1 - working with RDD10m
Exercise 2 - functional programming basics with RDDs10m
Exercise 3 - working with DataFrames10m
2 practice exercises
Practice Quiz (Ungraded) - Apache Spark concepts8m
Apache Spark and parallel data processing
Week
2
1 hour to complete

Week 2: Scaling Math for Statistics on Apache Spark

5 videos (Total 29 min), 1 reading, 2 quizzes
5 videos
Standard deviation3m
Skewness3m
Kurtosis2m
Covariance, Covariance matrices, correlation13m
1 reading
Exercise 1 - statistics and transfomrations using DataFrames10m
2 practice exercises
Practice Quiz (Ungraded) - Statistics and API usage on Spark4m
Parallelism in Apache Spark 
Week
3
1 hour to complete

Week 3: Introduction to Apache SparkML

5 videos (Total 34 min), 2 readings, 3 quizzes
5 videos
Introduction to SparkML20m
Extract - Transform - Load3m
Introduction to Clustering: k-Means3m
Using K-Means in Apache SparkML2m
2 readings
Exercise 1: Modifying a Apache SparkML Feature Engineering Pipeline10m
Exercise 2 - Working with Clustering and Apache SparkML10m
3 practice exercises
Practice Quiz (Ungraded) - ML Pipelines4m
SparkML concepts 
Practice Quiz (Ungraded) - SparkML Algorithms
Week
4
1 hour to complete

Week 4: Supervised and Unsupervised learning with SparkML

4 videos (Total 18 min), 2 readings, 2 quizzes
4 videos
LinearRegression with Apache SparkML6m
Logistic Regression1m
LogisticRegression with Apache SparkML4m
2 readings
Exercise 1 - Improving Classification performance10m
Course Project10m
2 practice exercises
Practice Quiz (Ungraded) - SparkML Algorithms (2)4m
Course Project Quiz

Instructor

Avatar

Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT

About IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

About the IBM AI Engineering Professional Certificate

The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning. You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers. You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders. By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer....
IBM AI Engineering

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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