Coursera

Coursera Online Courses

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  • Course platform: Coursera
  • Offered by: IBM
  • Level: Beginner
  • Price: Paid (Certificate paid)
  • Flexible deadlines
  • Class length: Approx. 10 months.
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The score is based on the quality of the course content & user experience as rated by the learners.
Learner rating
9.2
Content rating
9.1
PROS:
  • The course module has been designed especially for beginners to understand the basics of Data Science.
  • Hands-on projects will help you to clear your technical concepts.
  • Course provides detail and clear information about Pandas, Numpy, statistical and data analysis.
CONS:
  • Users expect more exercise involving the tools of data science methodology.
  • Should have prior knowledge of calculus and linear algebra.
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  • Course platform: Coursera
  • Offered by: IBM
  • Level: Intermediate
  • Price: Paid course (Paid Certificate)
  • Flexible deadlines
  • Class length: Approx. 8 months
  • Subtitle in 6 language
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The score is based on the quality of the course content & user experience as rated by the learners.
Learner rating
8.8
Content rating
8.8
PROS:
  • Useful information on an introduction to Deep Learning and Neural Networks with Keras.
  • Demonstration of an understanding of supervised deep learning.
  • You can apply your AI and Neural Network skills to a real-world challenge also can demonstrate your ability to communicate project outcomes.
CONS:
  • Need to improve the content on Scalable Machine Learning and on Big Data using Apache Spark.
  • Lengthy elementary content.
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  • Course platform: Coursera
  • Offered by: MICHIGAN University
  • Level: Intermediate
  • Price: FREE (Certificate paid)
  • Flexible deadlines
  • Class length: Approx. 5 Months.
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The score is based on the quality of the course content & user experience as rated by the learners.
Learner rating
9
Content rating
9.3
PROS:
  • Programming assignments are packed with coding questions that will help you revise what you have learned.
  • Overall you will find good introduction to python for data science.
  • Content covered in this specialization program would definitely meet your expectations for learning Data science with Python.
CONS:
  • The video content is too general and has little connection with assignment.
  • Need to put efforts into designing a curriculum.
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  • Course platform: Coursera
  • Offered by: NRU
  • Level: Advanced Level
  • Price: FREE (Certificate paid)
  • Flexible deadlines
  • Subtitle in 6 languages
  • Class length: Approx. 10 Months.
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The score is based on the quality of the course content & user experience as rated by the learners.
Learner rating
8.8
Content rating
8.7
PROS:
  • In terms of quality of the material this course will absolutely meet your requirements.
  • One with graduate level math skills can also commence with this course.
  • Assignments are good for getting to know python tools.
CONS:
  • Need to improve the quality of content in Bayesian methods for machine learning course.
  • Require quite a bit of probability theory knowledge.
  • The teachers should put more time into explaining the models and their details.
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  • Course platform: Coursera
  • Offered by: NRU
  • Level: Advanced
  • Price: Paid (Certificate paid)
  • Subtitles in 6 languages
  • Flexible deadlines.
  • Class length: Approx. 53 hrs.
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The score is based on the quality of the course content & user experience as rated by the learners.
Learner rating
9.4
Content rating
9.4
PROS:
  • Good and easy explanations on validation and metrics.
  • Excellent framework for additional applications of projects.
  • Chock full of practical information that is presented clearly and concisely.
  • Packed with full of tips and tricks and techniques that are well explained and very useful for data science.
CONS:
  • There are some pretty advanced tricks.
  • Need to improve content on introductory-level material.
  • Bit complex for those who do not have a good knowledge base on the subject of Machine learning.
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  • Course platform: Coursera
  • Offered by: Stanford University
  • Level: All Level
  • Subtitles in 7 languages
  • Price: Paid course (Certificate paid)
  • Flexible deadlines
  • Class length: Approx. 60 hrs.
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The score is based on the quality of the course content & user experience as rated by the learners.
Learner rating
9.8
Content rating
9.7
PROS:
  • Excellent content and Intelligible for Beginners of Machine Learning.
  • Enormous amount of information about Machine Learning.
  • Instructor’s explanation techniques are very simple and easy to understand.
CONS:
  • Need to upgrade more information about Calculus & Linear Algebra.
  • Should focus on meaningful points in the video tutorials.
  • Not much information about real-world tools
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  • Course platform: Coursera
  • Offered by: IBM
  • Level: Beginner
  • Price: FREE (Certificate paid)
  • Flexible deadlines
  • Class Length: Approx. 10 hrs.
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The score is based on the quality of the course content & user experience as rated by the learners
Learner rating
9.4
Content rating
9.6
PROS:
  • Perfect for absolute beginners.
  • Practical implementation and application.
  • of concept.
  • Interesting experiences shared by Data scientist professionals.
  • Simplified content & easily understandable.
CONS:
  • Very basic information
  • Lengthy introductory content
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  • Course platform: Coursera
  • Offered by: University of Michigan
  • Level: Intermediate
  • Price: FREE (paid Certificate)
  • Class Length: Approx. 4 weeks (16 hrs/week)
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The score is based on the quality of the course content & user experience as rated by the learners
Learner rating
9
Content rating
9.2
PROS:
  • Minimum duration of course will help you achieve more knowledge in less time
  • Overall the good introductory course of python for data science.
  • Detailed information about Python pandas library & Data Frames
CONS:
  • It should have covered the basics in more details
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  • Course platform: Coursera
  • Offered by: IBM
  • Level: Beginner
  • Flexible schedule
  • Subtitles in 4 languages
  • 7 months (3 hours/week)
 
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The score is based on the quality of the course content & user experience as rated by the learners
Learner rating
9.2
Content rating
9.5
PROS:
  • The course module has been designed especially for beginners to understand the basics of AI.
  • The course on building AI-powered chatbots seems to be very informative, logically laid out, and well presented, as it is a great platform to learn AI-powered Chatbots without programming with Watson Assistant.
  • Excellent course for understanding the basics of python and introduction to Pandas and Numpy.
  • Useful information on an introduction to computer vision modeling, OpenCV, and its related projects.
CONS:
  • Users expect more exercise involving the tools of Watson.
  • Building an AI power chatbot course should be updated to reflect the latest changes in the Watson Conversation interface.
  • In the python for data science & AI course, the instructions for IBM Watson and how to set up the assignments are outdated.
  • The final assignment seems to be much harder.
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  • Course Platform: Coursera
  • Offered by: Michigan Univ.
  • Level: Beginner
  • Flexible schedule
  • Subtitles in 5 languages
  • 8months (3 hours/week)
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The score is based on the quality of the course content & user experience as rated by the learners
Learner rating
9.6
Content rating
9.8
PROS:
  • Extremely useful to quickly and efficiently learn python basics.
  • Python data structure course concepts explained with lucid and engaging talks.
  • Meaningful learning experience on deeper understanding and doing the auto grader assignments.
  • Conceptual overviews are excellent in the 'Database with python course'.
  • Capstone course: Great to see how Python can be used for data visualization.
CONS:
  • Assignments/examples intended for beginners only (need practical examples).
  • Python to access web data course is challenging.
  • The Capstone course lacks organized content.
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  • Course platform: Coursera
  • Offered by: Johns Hopkins Univ.
  • Level: Beginner
  • Flexible schedule
  • Subtitles in 10 languages
  • 11 months (7 hours/week)
 
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The score is based on the quality of the course content & user experience as rated by the learners
Learner rating
9.0
Content ratings
9.3
PROS:
  • Data scientist toolbox is good to set you up for advance courses.
  • Insightful for those without technical/statistical background.
  • Assignments & quizzes are quite good & challenging.
  • Well understandable methods & construction of ML model.
  • After finishing this program one can start with KAGGEL datasets.
CONS:
  • Assignments in R prog. are too hard and challenging
  • Practical ML course does not cover how to write your own ML algorithms but it trains to use existing algorithms
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  • Course platform: Coursera
  • Offered by: Stanford Univ.
  • Level: All levels
  • Flexible deadlines
  • Subtitles in 6 languages
  • Approx. 54 hrs
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The score is based on the quality of the course content & user experience as rated by the learners
Learner rating
9.8
Content rating
9.7
PROS:
  • A complete and outstanding summary of main learning algorithms.
  • Precise presentation of mathematical and statistic concepts behind each algorithm.
CONS:
  • Python has not been included
  • The quizzes are very basic
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  • Course platform: Coursera
  • Offered by: Deeplearning.ai
  • Level: Intermediate
  • Flexible schedule
  • Subtitles in 12 languages
  • 4 months (5 hours/week)
 
More details +
The score is based on the quality of the course content & user experience as rated by the learners
Learner rating
9.6
Content rating
9.7
PROS:
  • Effective conceptualization on Neural Network and Deep Learning.
  • The Hyper parameter explanations are excellent.
  • Deeper insight into how to enhance your algorithm and neural network and improve its accuracy.
  • Content delivery from a very experienced deep learning practitioner
  • Overview of existing architectures and certain applications of CNN's
CONS:
  • Need's organized structure of assignment & exercises
  • Programming assignments are too simple.
  • TensorFlow framework uses some different jargon that is not really explained