Nowadays, Machine Learning is a buzzword as most of the computer giants are working on this field. Machine Learning evolves mainly from artificial intelligence and from recent years, it has been so pervasive as its big implications for our innovations, particularly several customers' favorite products like Apple's Siri, Tesla's self-driving cars,....
Why Machine Learning Matters?
According to U.S. Emerging Jobs Reports by LinkedIn, in 2017, it found that there are 9.8 times Machine Learning Engineers working today than five years ago, moreover, at the end of 2018, there is 12 times growth for the number of engineers. Machine Learning field is growing at the brisk pace since several industries such as transportation or advertisement rely heavily on it, which makes it a dream career field for engineering graduates across the globe for the year 2018. Therefore, as soon as you can utilize this kind of scientific models thoroughly, your career choices would be limitless.
With CoderSchool, finding out the places which we can study Machine Learning effectively for free.
- Online Machine Learning courses:
_ On Coursera:
_ In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
2. Applied Data Science with Python Specialization offered by University of Chicago
_ What will you learn:
- Analyze the connectivity of a social network
- Conduct an inferential statistical analysis
- Discern whether a data visualization is good or bad
- Enhance a data analysis with applied machine learning
3. Deep Learning Specialization with Andrew Ng, Kian Katanforoosh and Younes Bensouda Mourri
_ In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing...You will practice all these ideas in Python and in TensorFlow, which they will teach.
_ On Fastdotai (fast.ai):
_ A focus of the course is on teaching best practices.
_ Learn the most important machine learning models, including how to create them yourself from scratch, as well as key skills in data preparation, model validation, and building data products.
Having hands-on experience:
Don't just study all the time as you may remember a lot of knowledge but you don't really understand them. Having hands-on experience is essential since it reassures that you can work on your knowledge as well as better learning as a result.
Therefore, we strongly recommend you to do some practical projects because they are really beneficial to yourself.
- Implement Machine Learning Projects:
_ On Kaggle:
- Gender Recognition by Voice
- Students Alcohol Consumption
- Credit Fraud Detection
- TMBD 5000 Movie Dataset
Kaggle is where you encounter a lot of practical projects. You also need to consider to practice two more aspects in order to comprehend Machine Learning in depth. They are Computer Vision and Natural Language Process.
About Computer Vision:
Check out on PyImageSearch:
_ About Natural Language Processing:
- Introduction to Natural Language Processing (NLP)
- Named Entity Recognition
- Getting started in Natural Language Processing (NLP)
Updating your knowledge
Upskilling yourself every single day in the Tech world is indispensable because new technologies can make you out of date and easily get confused when you caught in a new conception. Spending from 1 to 2 hours exploring articles, researches,...from Techies is useful for everyone who cares about Tech.
- On Blogs Post and Tutorials:
_ You are into those blogs, you will reckon that they are easier to understand as they are more intuitive concept, particular articles basing on authors' personal experience. They don't just only provide multiple viewpoints to one sorely problem but also a vast amount of examples for it. You can give three websites below a try.
- Towards Data Science
- Forum Machine Learning Co Ban (Only Vietnamese)
_ Reading books is also pretty fun, as well as relaxing, especially for book-lovers. If you take time to read those academic books, you will gain fundamental principles on what you are doing so that you can create a strong foundation for yourself.
- An Introduction to Statistical Learning with Applications in R
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- Python Data Science Handbook
- Hands-On Machine Learning with Scikit-learn and Tensorflow
Always Be Learning
"Always Be Learning" is one of CoderSchool's core taglines. We know that in this day and age, you are really busy, following and finishing your online courses is hard enough to get done. So how can you get more time to check out those Blogs and Books above? Taking time to dig deeper into Machine Learning is good but if you can't do that, we will suggest you this back-up method which is following Tech experts or joining Machine Learning organizations on your social media accounts. So that you can get more helpful things while you are surfing your new feeds.
_ Yann Lecun: Chief Artificial Intelligence Scientist at Facebook, AI Research (FAIR)
_ Ian Goodfellow: Google Brain Researcher
_ FCollet: Deep Learning Researcher at Google
That's all of the things you would need. From Machine Learning team, we wish you having fun on your learning process.
This research was conducted by Machine Learning team of CoderSchool.