Never imagined online learning could be so much fun and so much in-depth. A lot of attention and effort has been put in to craft the training material including all the videos, exercises and quizzes so that every student can grasp the concepts with ease. The pace is excellent and the videos cover the foundation of what machine learning is all about.

After this course I understand what regression is - linear, non-linear or logistic. Understand how neural networks work, the idea behind their working and how other systems such as recommenders or anomaly detection can be built on top of these ideas. Practical examples such as hard-writing recognition, Photo OCR and many more provide real-life scenarios and applicability of machine learning in day to day life, which is actually cool!

I loved doing all the exercises even though I must admit some of them seemed difficult at first, but with guidance provided in the write-up, path became much easier. Submitting was fun because it was so simple using Octave and submission token.

Andrew Ng is such an excellent and knowledgeable teacher that I am looking forward to more courses from him and of course, other teachers on Coursera. His communication and teaching style is perfect. It is a great gift in my opinion to be able to take such high quality and informative courses online, while still being able to do my everyday job at my work place.

Almost feel little emotional now that the course has ended after months into it, listening to all the videos and doing the exercises. Feels great to become a student again!

I came to know about this course while attending one of the webinar's on machine learning applications in VLSI design. I thought of exploring more about this topic and found this course.

Andrew Ng is one of the well known expert in AI and adjunct professor. He worked at Google as the founder of google brain project, Chief scientist at Baidu (equivalent of google search engine at China) under his leadership Baidu AI team grown to 1300+ team, Co-founder of coursera, Founder of landing.ai, deeplearning.ai

He touched up all the basics (linear algebra, probability, derivatives, matrix operations) required for this course. So, anyone can straight away jump into this course and start leaning the concepts of machine learning.

The following topics are covered as part of course : Supervised learning (linear regression, logistic regression, neural networks, SVM's). Unsupervised learning (K means-- I love this algorithm ,PCA, anamoly detection), advice on skewed datasets, advice on building machine learning system, handling large dataset , few realtime applications in AI like online shopping, face recognition, image compression techniques.

The best part is the course is every lecture comes with a project which needs to be implemented in Octave/Matlab and most of them are realtime problems which we can apply in their field of study (Kmeans, photo OCR, image compression, housing price prediction etc..)

If you are looking for a quality ML course, you have reached the correct location. Blindly signup for it without wasting your time and start learning.

Really an amazing and wonderful course for anyone who would like to dive into the depths of machine learning. I am a student who is completely from a very very different background. To be frank i didn't even expect that I could complete/understand anything about machine learning. But Dr. Andrews,.... sir hats off to you. You are the real hero. The course takes us straight off from the beginning to the end without any complications. You just need a passion for the subject, to learn, to understand. But i would also like to point out some stuffs to the course coordinators. The prerequisite asked is just basic programming skills, but i doubt is that sufficient. I had to spend hours and days for getting the programming assignments coded correctly. I would like to request the team to either include more working examples in the programming part or clarify the programming side in a better way. Apart from this the course is just superb! I also take this opportunity to thank Coursera community, the group of mentors and the entire team that works behind the scenes to make it such a big success. The discussion forums and resources provided (including lecture notes, programming tips, etc) are just beyond words. I sincerely thank a course mentor Tom Mosher for spending his time and effort for the resources and ideas he has provided throughout this course. To wind up; do not hesitate just take up the course if you have a passion for it. Thank you Coursera, thank you team Machine Learning and last but not the least hats off Dr. Andrews. Good Luck

Amazing course.

Everything is very organized, explained very well that anybody who is willing to learn can understand it and build good intuition about the material.

Also, the Programming Assignment are awesome, a lot of the time contain some extra content and helps you understand the material. You also don't need to bother with creating the 'envelope' for your code - all the relevant code for plotting solutions and checking your answers is already included in the course!

I'm a 1st degree student for EE and took an introduction to ML course at my University, so I can't really tell from the perspective of a new learner. From me the course was complementary to the previous course I took and helped me develop more intuition about things that I already knew and learn new stuff (even though some of the things I already knew aren't included in this course)

For learner new to this subject this is my opinion:

Some topics that need some more deep mathematical background are skipped a bit, It is in order to focus on the Machine Learning aspect of the things, and also to enable people with more shallow background in math to take part in this world.

Even if this course is not all that is to Machine Learning (OFC it isn't! it is impossible to learn everything at once...) it is still really comprehensive and I think everybody that want to get into Machine Learning has to take this course. After taking it you can continue your learning independently because it gives you a really good, strong, comprehensive basis to ML.

Ty andrew and all the mentors.

Just finished the course. This is indeed an amazing course which can get started you in practical machine learning in less than 3 months. You will developing your own neural networks from the scratch. Below are the pros and not pros (i won't call it cons) that i experienced.

PROs :

Gets onto topics right away.

Information about practical implementation

Doable. Not too difficult and not downright easy. You have to put effort if you do not have backgroud in undergraduate mathematics to understand the concepts.

Prof. Andrew Ng. - He has knack of explaining something very complex in a very easy manner. Also, he justifies if he is not going to explain something

Assignments evaluation and practical scenarios.

Not PROs :

Very High Level : This course does NOT go in detail to explain the derivations and mathematics behind machine learning course. I think its OK and that is what makes the course doable. I find it really hard to accept a formula if the reasons are not known and hence, sometimes our only task was to learn the formula. For example : in SVMs.

What Next :

It is just a feedback. I think at the end of course, course team should guide students, what do to next. May be which course can be a good follow up course for this.

So, at the end, i just want to thanks Coursera team, Stanford Team, mentors, peers and Prof. Andrew Ng for spreading this knowledge for free. It is really helping people like me to study something not readily available in good quality in reach. Hopefully, i will also be able to give back to community some time soon.

Probably the best MOOC course on Machine Learning. Professor Andrew Ng is a great teacher - he makes complex algorithms and concepts very lucid and easy to understand, especially for people with no ML/ AI background. The course is very well structured and gives useful practical tips. It does get quite intense at times, especially the vectorization parts in the programming assignments - but the Discussion Forums are a huge help. Many thanks to all the mentors, especially Tom Mosher for his guidance and valuable insights. Two small pieces of feedback -

I ended up spending a lot more time on the programming assignments than on the videos themselves.The concepts were clear but the vectorization really made it very difficult to complete the assignments. Is it possible to use some other package instead of Matlab/ Octave, which is perhaps a little more high level and has functionality to do most of the stuff?

The second suggestion/ feedback is : I found the time estimates to be very aggressive for a beginner with no ML/ Octave background. So, most of my study planning would routinely get off track. Not sure if most of the people taking this course found them to be OK.

Is there a way to download Professor Ng's lecture notes for future reference? Not all the information is present on the slides. And it is difficult to bookmark videos - lecture notes would be a great help.

All in all, this was a very interesting course - one I would recommend to colleagues and friends to take. Many thanks to Prof Andrew for his guidance !

A huge thank to Andrew (and the team behind him)! Amazing introduction to ML. Educational, inspiring and enjoyable. The best first step on the path.

Andrew has explained everything very clearly and in the right details. (He has the unique skill to explain complex things simple way.) I personally liked Andrew's humble personality and teaching style as well. The lectures were enjoyable and easy to absorb. Hope he will have time to create new courses as the world of ML is progressing.

The students were really put first. Selecting Octave, to be able to focus on ML concepts and not on the programming language. (I have also questioned first, why Octave, but later realised that was a good choice.) The programming exercises were very well prepared, taking a lot of burden off the students, enabling sharp focus on practicing what we have leant that week, and did not have to spend extra hours on the scaffolding. (I have felt a little bit pampered, but without that help I am not sure I would have had enough free time every week to finish the assignments. )

The exercises were real, useful and fun. They helped to gain deeper understanding of the subjects but also showed real solutions for interesting problems. Before the course I could not imagine that I could gain the skills so quickly to solve these problems.

Also thank that: all the required math was explained in the course; the Octave demo was useful to use the language throughout the course; the exercise instructions had useful hints to solve the problems efficiently.

Dear Andrew,

I would very much like to take the time to thank you for this course, which has proven to be a blast and has lived 100% to its high expectation. Really happy that I have finally found the time to take this class which got my attention a while back. On top of introducing very fitting and relevant ML topics, I have really appreciated your skill in making the most complex and abstract notions very accessible and easy to understand. Extending the exercises with my own data and getting to"play around" with different parameters was also very fun. Especially useful for me were the insights regarding the "correct" mindset to have when approaching a ML problem (how to best spend your time, not losing the big-picture, inspecting your progress). As you can see from my pass ID, I am living in Germany. My employer is MediaMaktSaturn, the number one consumer electronics retailer in Europe and I am responsible for a department developing "classical" software. Since we have a lot of data available, I look forward to applying what I learned in this class. On a more personal note, I feel really attracted to reinforced learning and DQN, which definitely would have exceeded the introductory nature of this course. I would really appreciate some advice from you regarding what class to take next, regarding these topics - ideally taught by you or available at "coursera".

Having said that, allow me once again to show you my appreciation for this class and for your passion and effort - Thank you!

Sincerely Yours, Adrian

The first thing programmers say when I mention Machine Learning: "I want to do that, but I can't do/don't want to do/am afraid of maths". Well, ML concepts are intrinsically linked with mathematics, no getting around it; this course, however, takes the intimidating parts and breaks them down into easy step-by-step explanations. It's as close to making the calculus simple as anybody will ever get!

I came into this course after being out of formal education for a few years, but the intuitive manner in which the videos are presented meant that it all came flooding back very easily. The assignments consistently avoided being either too frustrating to complete or too facile to educate, each usually taking a few hours to solve and often producing that 'eureka!' moment when everything clicks together.

Additionally, resources available are top-notch; learners are advised to look at programming assignment tutorials after completing their own assignments for additional knowledge regarding the vectorisation of implementations.

(Quick advice for those with a science/mathematics background: for the programming assignments, don't make my mistake of sticking to the formulae with complete rigidity, especially where matrix multiplication/transposition is involved! Often you'll have to manipulate two matrices of incompatible size. Don't worry about transposing/reversing their position to make them fit, if it's what the algorithm demands in real terms. I know it's heresy, but hey, we're not in the theoretical world anymore!)

This course is brilliant. And yes just because its almost a decade old course doesn't mean the information is outdated or not useful. Infact, it is a complete opposite. This course is legend. At first I had the same feeling as should I start with this course... as many people recommended doing this before any other course. And it turns out that they were indeed 100% right.

The best thing about this course is that it teaches us the theory and many useful techniques in understanding the intuition behind many different machine learning algorithms. And yes this course uses Octave/Matlab as the language for programming assignments. Now many people will think that aren't they quite old and not used much anymore ( Octave ), but here's the thing... that this course teaches us such a good understanding behind these algorithms and the intuition behind them that the language we'll use won't really matter that much. And you can easily understand how versatile it is to implement those algorithms in any other languages. And also Octave is easy to learn. It's a prototype language ( I think? ) and so there shouldn't be any trouble understanding it, and if you know any other language already, then it will be walk in a park.

Finally, I would just like to say is that the video lectures in this course are really really really great. You will learn a lot from these videos, so you should definitely enroll this course if you're planning to do so. As the knowledge value in this course is absolutely epic!

I highly recommend all of those who have data-related background, are extremely interested and fascinated about the topics of machine learning, and would like to start building their career in this field to attend this course as their first step. Professor Ng is indeed very knowledgeable and is also a great lecturer. Throughout this course he not only well introduced and led me through all the basic concepts and techniques of machine learning, but also illustrated all the important and practical tips for realizing a real-world project, which are aside from the techniques and can be easily ignored, but may save you a lot of time and efforts and guide you much more easily to a more proper direction of achieving your objective.

Some people may find the concepts and programming assignments within the course more at entry-level and very simplified to understand and complete, while I think the course is still extremely helpful to me, as 1) it builds a great structure with integration of all necessary techniques under the umbrella of the topics of machine learning, which makes it much easier for self-trainers to extend their study above and beyond the course 2) it provides a completed set of background and extensive materials (e.g. Professor Ng's Stanford course) for people like me to deep dive their study under each topic.

All in all, I really appreciate Professor Ng and Coursera to offer this fascinating course, and thankful to be involved in such a great learning experience!

This is an excellent course by Prof. Andrew Ng. Learning from of the best in the industry has been truly an eye opening experience for me. Having a background with some level of software development experience, I have chosen to go with this course in particular (out of the many other courses that's available on the web) because I was motivated to not only understand how to use machine learning tools, but to get a concrete grasp of the theory behind machine learning algorithms, including concepts and intuitions. Short of going back to Uni to get this experience, I know there was a good chance Prof. Ng. would provide a similar academic experience in the course he provided.

An added bonus is that Prof. Ng also would provide advice and suggestion based on his own industry experiences leading engineering teams at world renowned internet companies. This reminded me a lot about the great academic teachers that I have had in my former years of university education (which I found to be invaluable). The landscape of machine learning is rapidly changing and evolving.

I feel like this course provided a solid foundation that grounded many fundamental concepts and motivations of machine learning in a very digestible way for its students. I would highly recommend this course to anyone interested in machine learning who not only wants to use the tools (as there are many guides out there already), but also wants to understand the deeper insights into these kind of technology.

Great hands on exercises and very clearly explained material. Was a bit slow for me I had to watch it at 2x: perhaps the simplest maths should be factored out into a separate mini course and assume a certain background for this one. The course is rather broad, though I was surprised not to hear once about Bayes or Markov (n-grams, HMM etc.). It might be a good idea to create a specialisation consisting of a separate basic maths part, all the methods presented here, methods applicable to bioinformatics and NLP too. And to top it all of Computer Vision. I assume the by-pixel techniques used in this course were just illustrating the points, as I would expect Gabor wavelets or something to reduce dimensionality and save ourselves from sliding windows (and rotations as a bonus). I am not sure if in this specialisation I would have liked to have all "science" points (how to run an experiment analyse results) separate from "how to implement an algorithm" and "why the algorithm works" or mixed in as this course does. I think either works. Some navigational infrastructure on coursera would be awesome (wiki style opportunities to jump around between "aspects" etc.). Finally, some summary notes would be very useful. When I do decide to implement any of this I will have to look through the exercise pdfs which are a bit long and at my code - perhaps, at the end, when you know someone has completed the exercises. Otherwise, the exercises are awesome.

This class taught me a lot of the nuts and bolts of machine learning, and by the end of it, I am much more confident in building machine learning algorithms, or joining a team in doing so. The instructor did an excellent job of explaining things slowly enough and in bite-sizes. The programming assignments were very tough (especially because I have very little knowledge of programming languages and Matlab) but very valuable in the end!

A couple of things I did wish for were: 1) A module or part of a module talking about bias of input data. I've heard a lot of about the effects of bias in data and how that can affect your machine learning algorithm output a lot and I wish the instructor told us his perspective on this. 2) Even though I probably would have dreaded how tough this would be, I still think it would be a huge value if we had a real life machine learning project we had to work on towards the end of the course from start to finish, from a fictitous client like Amazon or SalesForce etc. 3) I read about how machine learning programmer interns wrote about their experiences at the job and how they were so focused on getting the algorithm performance high but a lot of their job revolved around understanding the industry they were working in and what their company needed, because a perfect algorithm that has no value for the company is useless. So, I wish towards the end the instructor discussed this more to prepare us for a job.

An excellent course that provides both a good overview of machine learning technology and practical exercises that help reinforce the technology. I found it a challenging course as it requires a good knowledge of vector and matrix mathematics, Octave/Matlab programming and some mathematical concepts that I've not used to this extent. The work is ably assisted by an excellent group of tutorials and mentors which help ease what was quite a steep learning curve for me. I can highly recommend this course to learn what ML is about - don't let concerns about the level of mathematics or programming stop you from at least attempting this course. You will need enough free time to view the lectures and undertake the programming assignments and the course timings are pretty accurate, although a couple of the programming assignments took longer than expected due mostly to debugging my Octave code (often stupid errors that took me time to find and test).

In conclusion the course is an excellent balance of theory and practical work to see if you do actually understand what you've learnt in the lectures. Some basic skills in programming and mathematics (especially summation and vector arithmetic) would be of use, but this knowledge is not assumed and you should be able to complete the course and greatly expand your knowledge of machine learning principles, Octave/MATLAB programing and vector arithmetic, all in one course - bargain!

Outstanding course on machine learning fundamentals! A wide range of topics is covered in with a very carefully considered balance of understanding how the different algorithms work but at the same time not getting lost in all the mathematical details in the background. Although this means that you will not have seen all the mathematical proofs behind some of the equations that are presented (which would take considerably more time), it also makes for very effective use of your time if you are mainly interested in understanding the intuition of the machine learning algorithms and how to use them. Professor Ng has an incredibly clear and understandable way of explaining complicated topics, and his experience in the field shines through all the way. This course is not content with just throwing a bunch of equations at you but really conveys a very clear intuition about what they really mean, and gives a lot of practical advice on how to troubleshoot your machine learning algorithms, how to prioritise using your time in machine learning algorithm development etc. which is extremely helpful in guiding you on how to actually apply these algorithms to your own problems. The programming assignments are very well designed and will help you get practical confidence in making the algorithms work in practice (also the example applications are very cool and make it fun!) Thank you very much Andrew and all the course staff!

I should have never hesitated to take this course. It seems to me that anyone who is serious about learning machine learning (outside of a more structured environment such as a university program) absolutely must start with this course. With a tenuous grasp on Python, I am still not ecstatic about this course's use of Octave, but as others have said, one should not be deterred by this. And even though this course does not touch on all of the significant ML methods (e.g., random forests), it definitely delves (a purposefully chosen verb, mind you) into perhaps the most significant. Of note, the transition from one-versus-all logistic regression to neural networks was masterful. And while the dive into neural networks was unexpected for an 'introductory' course on machine learning, it was tremendously gratifying to learn (more than just the basics) about something that has only grown more prominent since the inception of this course. To cut to it, Dr. Ng is clearly a gifted, fantastic instructor. The balance of mathematics in this Coursera version of the course was perfect. I loved learning the mathematical meat of the algorithms and, and the same, *not* having to grapple with unnecessary proofs and the like. I feel deeply privileged to have been able to work through this course. And I am excited that Dr. Ng has now released a new specialization on deep learning (using Python, no less!). Thank you!

Great course. At the beginning, the of this course, I just want to learn something about neural network, but then I was fully attracted by this course. My major is biology but Andrew successfully makes me understand every point here. It is logical and understandable. It does not mean that it is an easy course, but reflects the elaborate work and deep understanding of Andrew. Now previous hard fields like computational biology and bioinformatics became quite easy to me.I can easily find out the algorithms they apply and know their shortages. If only I can know machine learning several years ago!

The course covers the underlying mathematical analysis of several famous algorithms like neural network, SVM, PCA and recommendation system. It contains clear instructions to answer 'what', 'why' and 'how' levels of them, and to their actual applications and limits including the workflow to check the quality of my product. It is magic to realize that the advanced technologies like face recognition and auto-driving are just built by such basic blocks.

Learners can have a solid understanding of the different fields in machining learning, and decide whether or not to go further. I proceeded to learn probabilistic graphic model, and hopefully it might be my key figure in my research paper on interfering casual relationship and influence of protein interaction during neural stem cell differentiation

First off, I think the course content is amazing! I really like that the instructor used Matlab that encourages the user to create vectorized solutions to the problems. I have heard many negative comments regarding the lack of use of Python, R, or some other library like Cafe or TensorFlow, but I believe all of that should follow after having the mathematical background to understand these principles. The content is not easy, and requires a fair bit of mathematical sophistication, but not so much to lose me, and hard enough to keep me engaged. I really enjoy how each learning unit builds off of the previous one, for instance, linear regression become logistic regression which becomes a neural network.

That being said, I really think that this course needs to have a fresh coat of paint on it. I believe it was filmed in 2008. I don't think the content really has been revamped since its release. The recordings look like they were filmed on an old web cam, not even as good as a modern iPhone. The slides should have some design work on them. I know it seems petty to stress over the presentation, but I think many people are turning to programs from Udacity that are very flashy, but not as technically rigorous mainly, I feel, just because of the presentation. I think that this course deserves a bit of energy polishing it up since it's still perhaps the most popular MOOC course out there.

Absolutely top notch class - I would say this is the best class, online or off, that I have ever taken:

Instruction is very well structured - building on prior components to build up to more complex advanced ones. This is especially important due to the mathematical and programming concepts required of the domain.

Quizzes are well timed to help evaluate learning - and really the primary purpose is to try to make students think about the subject material and reinforce the concepts. Programming exercises do require some basic knowledge of programming, but the use of Octave (or Matlab) as a tool and the prepared programs in which the student completes carefully chosen/defined missing components definitely reduces the programming burden and helps keep focus on the actual concepts.

The resources for lectures, quizzes, and programming assignments are invaluable, as well as the community built up around those questions. Using them during the course is essential.

Not least of all - the mentors - they are a great help in answering those odd, peripheral questions that really, fully, complete your personal understanding of the material (My personal thanks Tom and Neil for answering my quirky questions)

Kudos to Andrew and the Mentors for an exceptional class - I think it could really make an impact in education and helping realize the huge positive potential of this technology.

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Full disclosure: I am a mathematician, and therefore already well-trained in linear algebra, and I'm only 6 weeks into this course.

This course has been a near-perfect introduction to neural networks. Great pedagogical decisions were made to gradually bring the student from basic linear regression to motivating why a neural network is a logical next step to improve this process. This course has made me fluent in important terminology to deep learning and data analysis like bias, variance, precision, recall, and so much more. Not to mention, I've learned MatLab/Octave from scratch, which has turned out to be a nice programming language to add to my collection.

A caveat for non-mathematicians, or for that matter anyone not fluent in linear algebra: the neural networks will be a struggle. Some of the formulas relating to these may also be frustrating because I expect you won't understand why they work, or how to debug your code. I strongly recommend that you consider a rigorous linear algebra course, as a co- or pre-requisite.

To mathematicians: this course is a great starting-off point for learning about neural networks and other machine learning concepts. I already see applications to and from my research. In addition, I am able to explore the literature and decide on avenues for further exploration. Andrew Ng has truly provided a gift here to us.

I had always heard great things about this class during my research on what online courses to take next in my path towards becoming a data scientist. I was hesitant because it used Octave, a program I didn't think was going to be useful in my future roles. Then I completely bombed a phone interview in which they asked me the specific steps behind linear regression, and about gradient descent and the cost function. And I realized I didn't really know much about how these algorithms worked, although I used them in my day-to-day projects as a data analyst. So I finally decided to take this class, and after 11 weeks I can say that I experience WAY LESS imposter syndrome than before. I've still got a lot more to learn, but I'm confident that with everything Andrew Ng taught in this course, I will be able to hit the ground running on many other courses, with a thorough understanding of tools to use and the best ways to use them. In the end, Octave was just a little tiny component of a very scope of knowledge you'll attain after finishing this course. So if you're hesitant to take this course because of that, please don't be! I'm really grateful a course like this exists in a way that is easy to understand and is buildable. I'd consider this one of the best uses of my time outside of work, and I'd do it all over again now knowing what I have to gain from it.

This was my first on line course and the experience has been amazing. The lectures by Prof Andrew Ng were clear and the follow up programming exercises helped reinforce and enhance the concepts covered in the lectures.

The entire course was very well organized - videos, notes, discussion groups, suggestions and tips by the mentors was very easy to follow.

The focus of this course is on the practical application of machine learning techniques (supervised learning mostly but also some non supervised learning). Prof Andrew tries not to get into the advanced mathematical concepts but instead provides good intuition and then shows one how to apply the different ideas underpinning machine learning with some practical examples. In my view, this is an excellent way to quickly become familiar the concepts and to see machine learning in action. A student of the course will gain very good insights and can then follow up with the underlying math as needed.

The final two lectures on how to scale machine learning to large systems and a suggested systematic framework for figuring out which aspects of the design to focus on were very instructive.

In summary, i would highly recommend this course to anyone interested in the area of machine learning. Be prepared to work through some challenging (but very worthwhile) programming exercises to get the most out of the course.

It is a very nice introductory course in machine learning. It teaches a good range of machine learning algorithms and good advices on how to implement them effectively. At the end of the course I feel like I am ready to tackle some machine learning projects on my own and I am sure many others who finished will too.

For someone with a strong mathematical background like me (Have a undergraduate degree in maths and theoretical physics), the maths component is lacking. There are little derivations on equations and feels like I could've understood some of the algorithms deeper and instructor cannot teach it here. It feels especially true in supper vector machine. Also if you are strong in linear algebra and really familiar with matrix multiplication rules you can vectorize some implementation very effective and reduce what could be five lines of codes to one.

For someone without a strong mathematical background, I think the main difficulty is going through the messy indexing in equations and actually implementing algorithms with a lots of for-loops. It is also hard to fully understand some concepts behind some algorithms like support vector machine and relate the resulting algorithm with the concept.

I enjoyed the course a lot and really have learnt a lot. Now I feel ready to start some machine learning projects on my own and dive deeper into this field.