Machine Learning and Reinforcement Learning in Finance Specialization

NYU Machine Learning Specialization Highlights:

• NYU via Coursera
• Learn for FREE, Up-gradable [Enroll to Specialization for FREE Now]
• 8 Months (3 hours weekly) of effort required
• 12,478+ already enrolled!
• ★★★★ 3.7 (1,008 Ratings)
• Language: English

Eager to learn at ML via NYU? Then you’ve clicked the right page. With New York University, Coursera has introduced the above specialization. Now, many of you can learn predictive modeling, financial engineering, Tensorflow, reinforcement learning, optimal trading, and Q-learning using financial problems. Specialize in the most popular field of technology that is to say machine learning. The course is a great opportunity for anyone at home looking for perfect hands-on specialization. Solve your financial problems, degree coursework, and institutional finance issues in the easiest way possible. The whole course comes with worth-mentioning benefits as follows:

• To solve multiple practical problems in finance
• Shareable in your LinkedIn bio/professional resume
• Pause your learning or end your subscription at any time.
• Specialization Certificate from Coursera

Specialization Courses in Machine Learning (4 Courses)

Brought to you by the New York University, the above said coursework intends to convey the exciting and impressive benefits of just using ML with its four specialization courses. Machine learning does have an impact on just about everything you can imagine. The course encourages you to begin adding ML in your financial settings that lets you access, control, and function properly with a deciding set of courses.

• Course 1: Guided Tour of Machine Learning in Finance(★★★★ 3.8 | 562 ratings | 179 reviews)

This fundamental course provided to you enhances your knowledge concerning ML. Here, you get introduced to various ML applications in finance. Brace yourself to understand where and when you can apply machine learning. The course covers all important points relevant to specialization and reinforced learning. The important advantage of learning this first-in-line course is that the knowledge will help you find the connections.

• Course 2:  Fundamentals of Machine Learning in Finance(★★★★ 3.8 | 277 ratings | 58 reviews)

Let’s get your mind working once you’ve understood the fundamentals of machine learning. The course is for all of you with little or no prior knowledge of ML. From identifying to finding an appropriate solution until implementation, you have all the necessary hands-on insight. Completing this course means you’re fully capable to perform and analyze practical ML issues in real life.

• Course 3: Reinforcement Learning in Finance (★★★★ 3.5 | 103 ratings | 28 reviews)

Realize the basic involvement of RL in finance. Develop applications of RL to deal with option valuation, trading, and asset management. Throughout the course, you’re going to learn quite important techniques. That being said, you’ll be qualified enough to solve iconic financial issues including portfolio optimization, optimal trading, effective pricing, and risk management.

• Course 4: Overview of Advanced Methods of Reinforcement Learning in Finance(★★★★★ 3.8 | 66 ratings | 11 reviews)

The final course in this specialization course delves deeper into RL. The above course enables you to find links between RL, option pricing, physics, and IR. Learn what’s best for you and your management. Practice the art of managing cryptocurrencies, peer-to-peer lending, and high-frequency trading. By the end of this course, you’re capable to explain, predict and discuss matters. With such well-‎rounded and perfect overview, you’re all set to change the world. You might also be interested in learning effective ways of R programming.

Reviews for Specialization Certification

To provide you a genuine insight, here you have a rampant indicator to save your time and energy before you dig-in into any of the above-listed 4 courses. Help yourselves and follow the best possible user experiences.

Positive Reviews:

• All the courses are simply impressive. Among all the courses I’ve taken so far this specialization provides the best overview of ML. The problem sets in the courses are insightful and unique that introduced me to useful API’s in sklearn and TensorFlow. Having said that, I compeletely recommend this specialization. (Zoltan S, ★★★★★)
• I decided to take “ reinforcement learning” and I find the course to be as excellent as I expected. With my experience, the course materials are clear in easy-to-understand given the PDF support. For everyone out there, you must take some time and take this course. (Oliver B, ★★★★★).
• I took this specialization and like many others, all the courses are extremely useful. Let me tell you that it may seem technical to anyone without a financial background but the instructor’s fantastic material provided a unique perspective to me. (Stéphane T, ★★★★☆).
• The course about reinforcement learning has been brilliant for me. I came to know about relevant tools because the instructor placed RL, option trading, and stock trading in a simplifying manner before me. Nevertheless, I must say, make sure you have a good command on python and ML libraries. (Hilmi E, ★★★★☆).
• Among the courses, I decided to take “fundamentals of ML”. The course gave me a perfect head-start before I began my work. The materials provided proved to be conveying concepts through better instructions that made learning easier. However, I do believe lab assignments need improvements. (Tunan N, ★★★★☆).
• I had taken reinforcement learning course. After having a great time learning innovative concepts in the best way, I can say it’s undoubtedly an amazing course of all. Although, the course can become many times better with the addition of numerical examples. (Marcelo R, ★★★★☆).
• Out of all the courses, the third course provides an amazing experience. The contents were easier to learn and practice with the help of peer-reviewed evaluation that is absolutely worth the time and effort. (Luis A, ★★★★★).

Negative Reviews:

• The course contents are indeed perfectly taught with good lectures and presentations. The only issue I came across is the theoretical gaps in the course and a bit too much effort. Also, completing this specialization requires a decent amount of effort. (Sridhar S, ★★★☆☆).
• I took the first course in this specialization and was about to rate 5. But then I found the lectures perfect and the exercises problematic. Another thing I encountered was the lack of TA’s on the specialization potential forum for more detailed clarification. (John G S, ★★★☆☆).
• Overall the courses were interesting to learn. But I would like to  mention that there seemed a big gap between lectures and coding assignments. Although the course quality has been good. (George D, ★★☆☆☆).
• I had taken a guided tour of this specialization. I must confess that the exercises seemed too poorly planned for the last week and gave me a hard time understanding. The course has been fine to work with even though. (Ronald B, ★☆☆☆☆).