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IBM Machine Learning Professional Certificate

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Brief Introduction

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. Also gain practice in specialized topics such as Time Series Analysis and Survival Analysis.

Course Summary

This professional certificate program offered by IBM introduces you to the fundamentals of machine learning and helps you develop skills in data analysis, machine learning algorithms, and statistical modeling.

Key Learning Points

Learn to use Python libraries like Scikit-learn, Pandas, and Numpy for data analysis
Understand the basics of machine learning algorithms such as regression and clustering
Develop skills in statistical modeling and evaluation of machine learning models

Job Positions & Salaries of people who have taken this course might have

Data Analyst
- USA: $60,000 - $110,000
- India: ₹400,000 - ₹1,000,000
- Spain: €28,000 - €50,000
Machine Learning Engineer
- USA: $90,000 - $150,000
- India: ₹500,000 - ₹2,000,000
- Spain: €40,000 - €70,000
Data Scientist
- USA: $80,000 - $150,000
- India: ₹600,000 - ₹2,500,000
- Spain: €35,000 - €60,000

Learning Outcomes

Ability to perform data analysis using Python libraries like Scikit-learn and Pandas
Knowledge of machine learning algorithms and statistical modeling
Skills to evaluate machine learning models and select the best approach for a given problem

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Understanding of basic statistics concepts
Familiarity with data analysis techniques

Course Difficulty Level

Intermediate

Course Format

Online Self-paced Learning
Video Lectures
Hands-on Exercises
Quizzes and Assignments

Similar Courses

Applied Data Science with Python
Data Science Essentials
Applied AI

Related Education Paths

Notable People in This Field

Co-founder of Coursera and Founder of deeplearning.ai
Principal Data Scientist at Booz Allen Hamilton

Related Books

Description

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 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, 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.

Summary of User Reviews

Find out what users have to say about the IBM Machine Learning professional certificate course on Coursera. Most users have found this course to be comprehensive, well-structured, and practical, with valuable hands-on experience. However, some users have expressed concerns about the course being too fast-paced and lacking in-depth explanations.

Key Aspect Users Liked About This Course

The practical approach and the hands-on experience are highly praised by many users.

Pros from User Reviews

Comprehensive and well-structured course materials
Practical approach and hands-on experience
Opportunities to work on real-world projects
Good support from instructors and peers
Useful insights and techniques for data analysis and machine learning

Cons from User Reviews

Some users find the course too fast-paced and challenging
Lack of in-depth explanations on some topics
Limited flexibility in scheduling and deadlines
Expensive compared to other similar courses
The course may require prior knowledge of basic statistics and programming skills

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