The course was highly informative and very well presented. It was very easier to follow. Many complicated concepts were clearly explained. It improved my confidence with respect to programming skills.
I'm extremely excited with what I have learnt so far. As a newbie in Machine Learning, the exposure gained will serve as the much needed foundation to delve into its application to real life problems.
By Christopher S•
Excellent content and relatable use cases. As a beginner in data science with no formal programming, the information is presented in a way to help you understand the fundamentals and then apply them using the pre-built python packages that are widely available. I started with data science 3 years ago and it was very difficult to get started without any programming or statistics background. This course does a tremendous job of making it accessible, understandable and quite frankly a lot fun in the process.
By Aniket A•
This course is fantastic, It has adequate amount of theory supplemented by labs. I also like the Watson Studio, and the fact that you actually learn to use some industry level tools in this course really takes the icing on top. The staff is supportive and wonderful, the community and cohorts are great. Overall I would happily recommend anyone who has absolutely no knowledge about Data Science to start right here with this course. Really enjoyed and thank you IBM for you digital badge. :-)
By Oleh L•
Well structured course, which will give you understanding of the applied way of working. The topics are explained in quite enought details, allowing you to use learned approach in practical way.
What I would personally wish - a bit more examples of different kinds. It should not be included into main structure of the course (to decrease a work load of Instructors and Students). It needs to go into Optional part, but I'm sure - who is interested in, will finish the task.
By Iskandar M•
This course needs basic knowledge on algorithm and programming experience. I really recommend this machine learning course for those who have computer science, statistics, or math background. The instructor is very clear, concise, and using simple diction when explaining the subject. All presented in here is valuable and worth reading and listening. The final task is somewhat challenging, but we'll have to really dig into the examples presented in the labs. Thank you!
By Peter P•
This course was perfect, especially in my situation. I know all of the math behind neural networks, and fitting, but there were many algorithms I've never been exposed to - and this course exposed me to a lot! I liked the hands-on coding labs and learned where to find a lot of Python stuff that I wasn't aware of. A lot of terminology that I'd heard about is now clear in my mind. And the amount math was balanced perfectly with the getting things done.
By Peruru S S•
I really enjoyed taking this course. The instructor is to the point, crystal clear. Nicely explains the essence of the topics in 5 to 6 minutes. I recommend this as a good introduction course to get a basic overview of different algorithms. However, if one wants a deeper understanding with specific details, this is not the course. This course will definitely serve as a good introduction which help us to get motivated to do more advanced courses.
By Ashit C•
I really enjoyed during this course . Gives you a lot of skills of how to deal with data ,predictions or recommendations. At the end i know how day to day life works based on machine learning as they quite kept few real world examples while explaining. Little bit of difficulty i faced while doing main project as there was less guidance on what we have to show at the end of project. But it was a great course. Worth spending time over it.
By Clarence E Y•
This course will challenge learners to commit to learning about the key objectives for using algorithmic approaches to answering important business questions using data. The lectures cover the theoretical foundations of the "relationship" algorithms used for classification and clustering methods. Additionally, the labs provide a fully integrated environment in which learners can do hands-on investigations to gain proficiency.
By Haroldo D Z•
Hay un nivel de Detalle en los Algoritmos de Machine learning, que ayuda a entender como pueden aportar realmente en diferentes problemas de regresión, clasificación, clusterring y recomendación. y la plataforma es muy practica para lograr entender como un lenguaje como python puede aportar a hacer mas sencillo la aplicación y uso de estos sin necesidad de instalar herramientas ni conocer los detalles del lenguaje.
By Niladri B P•
A lot of ground is covered here. So it won't make you an expert, but will provide a great base from which one can build further expertise. The videos explain the concepts very nicely, so it is important to sit, listen and take notes. The labs are also very detailed and occasionally a bit advanced with the code. Overall, however, the course makes you work but you can choose how much work to put into it. Recommended.
By Hussain A•
The best direct-to-the point instructor so far! After going through the major classes available on the net I found Dr. Saeed Aghabozorgi concise way of keeping videos short with no code and rely on labs with best example for each concept highly admirable in an intermediate course. It took me once 30 minutes for taking notes about a 5 minutes video, well worth it. I say keep it concise it becomes a reference!
By Andréas V J•
Fantastic course for quickly understanding the basic categories of machine learning algorithms and how they work. I would recommend this course to those who have some experience in computer science or software engineering with little-to-no experience in machine learning. Covered in this course: machine learning basics, data regression, classification algorithms, clustering algorithms and recommender systems.
Its a nice course for beginners! Gives clear explanations on some of the basic concepts! Python Notebooks give clear picture on basic code implementation aspects.
Suggestion - Week 6 there are 2 videos that need an update on logging into Watson Studio. Need to update the instructions with latest version. Its a minor correction; good if updated as our screens and options differ from your instructions.
By Jaime O•
GREAT CLASS !
IBM WATSON "JUPYTER" NOTEBOOK WORKED OUTSTANDINGLY WELL!
LEARNING FROM THE NOTEBOOKS IS AN IDEAL WAY TO LEARN THIS !
LECTURES ARE CONCISE BUT VERY CLEAR.
I FOUND MY PREVIOUS LEARNING/EXPOSURE TO MACHINE LEARNING VERY HELPFUL TO ENABLE ME TO ASSIMILATE THE (QUITE EXTENSIVE) MATERIAL!
MANY THANKS TO THE INSTRUCTOR AND TO IBM !!!
MANY THANKS TO THE INSTRUCTOR AND TO IBM !!!!!1
By Kolitha W•
Absolutely knowledgeable and interesting course with a plethora of insights and plenty of hands-on lab sessions to digest what you learn. I take this moment to thank all the resource collaborators and appreciate the immense effort they all have put into this course to keep it updated and attractive. I wish they could keep this up to help thousands of individuals to groom individually.
By William B L•
This course gives a good introduction (theory and applied) to a variety of machine learning methodologies. The presentations are well thought-out. The labs are great. I learned an enormous amount from doing the hands-on work in Watson Studio/Jupyter notebook.
This would be a bit much for a beginner in Python, but with a modest understanding of the language, this offers a lot!
By Christian C•
Excelente curso. Los contenidos se presentan de forma facil y comprensible. Hay un gran dominio por parte del instructor y ademas, los contenidos son cubiertos con suficiente profundidad.
Excellent course. The contents are presented in an easy and understandable way. There is great mastery on the part of the instructor and also, the contents are covered in sufficient depth.
By Timur U•
I really enjoyed this well-organized and professional course. I would like to show my appreciation to the manager of this course, especially for a video presentation for each module. The technique to have Query and then Solution is the outstanding feature and helped me to cover all course materials and implement the Assignment tasks on a high level. Thank you so much.
By Juan R•
This Course is awesome to learn the theory and practice of some Machine Learning Metods.
By the end I feel like I can tackle my own datasets and analyze them with various methods seeking the optimal one.
The only thing that could be better is if the course could go a bit deeper into the optimization algorithms (like gradient descent) even if it's a bit mathy.
By Wagner M•
[PT-br] Um dos melhores cursos onde se alinha teoria com a prática na área. Conteúdo bem completo, passando pelas diversas técnicas de ML, com vídeos muito bem explicados e conteúdos práticos que demonstram como aplicar cada técnica. Além disso, as provas são bem desafiadoras e o projeto final é bem completo, o que aumenta o valor do certificado ao final.
By Arindam G•
No Doubt COURSERA is always best AND MNC like IBM,Google courses associated with coursera are MIND-BLOWING.
The Instructors are so great at Explanation Part that hardly anyone won't Understand All the Topics
I would love to thank all the INSTRUCTORS who created such a Awesome Content for us.
My Personal Ratings For All the Instructors: 100 / 100
By PRINCE G•
The course was amazing to get started with machine learning. You are going to learn about some amazing machine leaning algorithms and for the capstone project you have to use them to find best accuracy for a dataset. Peer reviewed assignments are really good as they help every student to know different techniques each person use and can learn from them.
By Lior B•
Great introductory course. Clear explanations and good homework to get your hands dirty and see results of algorithms.
A rather minimal mathematical understanding is assumed by the course so begginers would not be overwhelmed.
Keep in mind this course will not make you an expert or teach you how to write some of the more advanced algorithms by yourself.
By Robinson P P•
Excellent Course. Course cover.
2) Classification- Algorithm :KNN , Decision Tree , SVM , Logistic Regression etc.,
3) Clustering- K-Means , Hierarchical ,Agglomerative ,DBSCAN
4) Recommender System - Content-Based and Collaborative Filtering
5) sci-kit learn and SciPy details with Practical labs on Jupyter Notebook on IBM Watson Platform