12 avis pour Machine Learning in Python (Data Science and Deep Learning)
Note 4 sur 5
Kurtus Kobeck –
The sundog materials don’t work (Most likely I would have to download the materials, but thats not allowed on a company device). This course shows how to build your implementations of ML solutions. It doesn’t show you how to apply ML, DS or DL as problem solving tools, which is what actually empowers someone to create value using these skills.
Note 5 sur 5
Jonathan Carr –
This was an excellent introductory course. I have taken several AI/ML courses by different instructors, Sundog is by far the best to follow. The knowledge and course material is extensive and thorough.
Note 5 sur 5
Andrii Tverdokhlib –
This course is an impressive choice for both newcomers and amateurs ini Data Science, because learning basics is never redundant 🙂 Excellent and easy-to-understand submission of course material!
Note 4 sur 5
Rahutomo Mahardiko –
this course is something that needs deep understanding, not easy as the first step for data science.
overall, the whole materials are quite easy to be followed
Note 2 sur 5
Eric Glandian –
Lecturer touches on lots of topics but just reads through them. Not good for a beginner. But if you’re familiar with the material, it’s good reinforcement. Definitely not the place to start. As for going through the code, the lecturer just reads through the code. You could do this on your own and figure it out. Positive/negative comment about speaker’s voice. He has a FANTASTIC voice… one made for radio. Deep and soothing. But it puts me to sleep, lol. Double edged sword. Worth having in the library for reviews and additional commentary on things, but again, not where I would start.
Note 5 sur 5
Eduardo Azevedo de Mello Franco Menezes –
Very in-depth course in all topics helping a lot in the day to day of those who want to become a professional in the data science area. Attending Frank’s courses I became a big fan of his. Every time he takes a course I don’t even see what it is and buy without fear of regretting it. Thank you so much Mr Kane!
Note 3 sur 5
Robbe Vanhaesebroeck –
I took this course more as a refresher of the basic concepts and as an introduction to more advanced concepts.
I’d hoped to get more actual hands-on practice during the course. The ‘Activity’ lectures were basically looking at already created notebooks and maybe going back to change some stuff. I had expected some more actual exercises such as the final project. Even then, I felt the handholding was maybe a bit too much.
Another thing I was missing was a really structured approach to ML in real life. During the lectures you stretched the importance of data cleaning but you didn’t really go into feature engineering and feature selection. Also, the examples were almost always limited in number of features and I felt like the environment was maybe too controlled.
Note 4 sur 5
James Ramsay –
Overall a good course. I took this course as a supplement to my Machine Learning Master’s course and this followed along almost verbatim my Machine Learning class. This course dives into Tensorflow and Keras which was much needed. My biggest issue was getting Tensorflow to actually work. I was unable to get Spark, Tensorflow, and then Keras to run. I think some code and how-to’s need to be updated. Luckily Google Colab was my rescue. After much research and unable to get Tensorflow to work on my computer I jumped over to Colab and with a few extra code I was able to pull in files and run the code as is. The final project was by far my favorite. It really put everything together which was explain poorly in my Machine Learning class. Other than the issues with getting Tensorflow to work, this was an overall good course. Thank you!
Note 4 sur 5
Geoffrey Ponder –
Extensive quality content, with friendly and encouraging presentation.
However the course needs to be taken more than once as there is just so much to learn!
Note 5 sur 5
Md Kaavish Arbab –
Good course.. deep learning could have been a little more elaborate.
Note 5 sur 5
Allan Mulholland –
A great introduction to Machine Learning and explained in a very interesting way by a great lecturer. The hands on aspect of the course was very appealing as it really helped to cement concepts – the lectures to help set up everything in detail were very helpful in this respect.
Would highly recommend for getting a flavour of what machine learning is about and how difficult it can be!
Note 5 sur 5
Michael Harkins –
More than enough information and examples to get you way past intermediate level. You could revisit this course for years…
Kurtus Kobeck –
The sundog materials don’t work (Most likely I would have to download the materials, but thats not allowed on a company device). This course shows how to build your implementations of ML solutions. It doesn’t show you how to apply ML, DS or DL as problem solving tools, which is what actually empowers someone to create value using these skills.
Jonathan Carr –
This was an excellent introductory course. I have taken several AI/ML courses by different instructors, Sundog is by far the best to follow. The knowledge and course material is extensive and thorough.
Andrii Tverdokhlib –
This course is an impressive choice for both newcomers and amateurs ini Data Science, because learning basics is never redundant 🙂
Excellent and easy-to-understand submission of course material!
Rahutomo Mahardiko –
this course is something that needs deep understanding, not easy as the first step for data science.
overall, the whole materials are quite easy to be followed
Eric Glandian –
Lecturer touches on lots of topics but just reads through them. Not good for a beginner. But if you’re familiar with the material, it’s good reinforcement. Definitely not the place to start. As for going through the code, the lecturer just reads through the code. You could do this on your own and figure it out. Positive/negative comment about speaker’s voice. He has a FANTASTIC voice… one made for radio. Deep and soothing. But it puts me to sleep, lol. Double edged sword. Worth having in the library for reviews and additional commentary on things, but again, not where I would start.
Eduardo Azevedo de Mello Franco Menezes –
Very in-depth course in all topics helping a lot in the day to day of those who want to become a professional in the data science area. Attending Frank’s courses I became a big fan of his. Every time he takes a course I don’t even see what it is and buy without fear of regretting it. Thank you so much Mr Kane!
Robbe Vanhaesebroeck –
I took this course more as a refresher of the basic concepts and as an introduction to more advanced concepts.
I’d hoped to get more actual hands-on practice during the course. The ‘Activity’ lectures were basically looking at already created notebooks and maybe going back to change some stuff. I had expected some more actual exercises such as the final project. Even then, I felt the handholding was maybe a bit too much.
Another thing I was missing was a really structured approach to ML in real life. During the lectures you stretched the importance of data cleaning but you didn’t really go into feature engineering and feature selection. Also, the examples were almost always limited in number of features and I felt like the environment was maybe too controlled.
James Ramsay –
Overall a good course. I took this course as a supplement to my Machine Learning Master’s course and this followed along almost verbatim my Machine Learning class. This course dives into Tensorflow and Keras which was much needed. My biggest issue was getting Tensorflow to actually work. I was unable to get Spark, Tensorflow, and then Keras to run. I think some code and how-to’s need to be updated. Luckily Google Colab was my rescue. After much research and unable to get Tensorflow to work on my computer I jumped over to Colab and with a few extra code I was able to pull in files and run the code as is. The final project was by far my favorite. It really put everything together which was explain poorly in my Machine Learning class. Other than the issues with getting Tensorflow to work, this was an overall good course. Thank you!
Geoffrey Ponder –
Extensive quality content, with friendly and encouraging presentation.
However the course needs to be taken more than once as there is just so much to learn!
Md Kaavish Arbab –
Good course.. deep learning could have been a little more elaborate.
Allan Mulholland –
A great introduction to Machine Learning and explained in a very interesting way by a great lecturer. The hands on aspect of the course was very appealing as it really helped to cement concepts – the lectures to help set up everything in detail were very helpful in this respect.
Would highly recommend for getting a flavour of what machine learning is about and how difficult it can be!
Michael Harkins –
More than enough information and examples to get you way past intermediate level. You could revisit this course for years…