Artificial intelligence and machine learning are in buzz these days and more and more people are interested to learn about it. It got a major breakthrough when Google made AI history by creating an algorithm that mastered Go. And the technological advancement is creating more jobs as companies need high-skilled AI talents to develop and maintain a wide range of applications.
If you are interested in becoming a machine learning expert but don't know where to start from? Don't worry we got you covered. In this article, we will show you the top-down approach for getting started in applied machine learning.
Heres What You Should Do Before You Get Started With Machine Learning
ML is all about applying statistics and computer science to data. You really do not need to be a professional programmer, mathematician to learn ML, but to master it, one has to be good at maths, programming and have some domain knowledge.
There are many programming languages which provide ML capabilities. But Python and R are most commonly used languages. So, before entering into the world of ML, choose one of these two programming languages Python or R.
Python is naturally disposed towards machine learning and is preferred by tech companies where they need end-to-end integration and develop analytics-based applications, leveraging analytics-friendly libraries. If you want more theoretical knowledge about different machine learning algorithms, you can also read Python Machine Learning Edition 2 written by a machine learning researchers Sebastian Raschka and Vahid Mirjalili. The book also covers large varieties of Practical Algorithms with Python, as well as using it with sci-kit learn API and replaying it with Tensorflow API.
R as a language for statistical inference has made its name in data analysis and is preferred by companies which are primarily focused on advanced analytics and pretty much become a lingua franca for data science.
Learn Statistics For Machine Learning
You will be benefited with the knowledge in statistics, mainly the probability of Bayesian, as it is mainly needed for the algorithms of machine learning. And if you are willing to learn the statistics basics then you have the option of signing in the statistics which are descriptive and the inferential statistics courses as well which are offered by Udacity. Both are cost-effective.
ML Courses To Sharpen Your Knowledge
If you want to have a foundation of machine learning which is strong, you need to grab as much knowledge as you can. For machine learning, there are several courses which are there to learn.
This course is mainly for the one who begins with machine learning, data mining and the pattern recognition which is statistical This course is taught by Andrew Ng and covers all basic algorithms. Topics include:
1.Supervised learning (parametric/nonparametric algorithms, support vector machines, kernels, neural networks)
2.Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning)
3.Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI)
This course will help you learn what the foundation of deep learning is and you will easily get to know how deep learning actually works. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. Once you get the Certificate enrolment, you can easily access all the videos, quizzes and all the assignments of programming.
Here in this course you will get the theory which is basic, and applications and algorithms.thsi course covers with the whole theory part and the practice and it covers all the aspects of learning. You need to have more knowledge of programming as it contains math. You have multiple homework sets also.
This course is something where you can grab knowledge in 15 hours and here you deal with the real world case studies, many new video lectures too and have 40+exercises which will help you to learn the concepts of machine learning. this course is mainly designed by Google for its employees to give a practical intro to the fundamentals of machine learning.