Machine Learning? What it is?
Machine learning is a subfield of artificial intelligence (AI). The aim of machine learning generally is to understand the data structure ( structure of data) and fit that data into models that can be understood and utilized by people.
There are lot of questions are comming in our conscious mind about machine learning.
Are you aware about it ? Obviously Yes, according to me!, thats why you are reading this.
Anyway, keep reading
here in this article following topic are covered.
- What is machine learning?
- What are the scope of machine learning ?
- Where you can start?
- Can you become a machine learner?
1. what is machine learning?
Introduction To Machine Learning
For example, one kind of algorithm is a classification algorithm. It can put data into different groups. The classification algorithm used to detect handwritten alphabets could also be used to classify emails into spam and not-spam.
“A computer program is said to learn from experience E with some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.” -Tom M. Mitchell.
Some OtherApplications of Machine Learning
-Predicting security breaches, finding malware and other anomalies in data.
-Personalized recommendations (ex: Netflix, Amazon).
-Improving online search results based on preferences.
-Natural language processing.
-Smart cars and smart homes (IoT).
2.What are the scope of machine learning ?
Now a days, machine learning is rapidly growing field in computer science and data science.
Engineering Students can make future in this field.
It is fast growing now in terms of job opportunities.
“It’s not magic,” says Greg Corrado, a senior research scientist at Google. “It’s just a tool. But it’s a really important tool.”
In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. Machine learning is a subset of AI. The theory is simple, machines take data and ‘learn’ for themselves.
3.Where you can start machine learning?
If you start with hardest stuff first,It will be way easier to get discouraged and give up so create small achievable goals during the learning process to stay motivated .
- Choose Programming Language .
- “Python is a Good Choice” scientific and numeric computing (with the help of libraries such as NumPy, SciPy, etc.), Support’s wide range of Libraries for various algorithms and have large community in ML .
- Basics Maths Knowledge about Algebra,Calculus,Probability & Statistics: (Optional: This is not must, Having some basic knowledge about it would be good, Since we can take the advantage of Python Scientific libraries like Numpy & Scipy ,because while learning different algorithms you need to make visualization about the data & use it’s properties in algorithm’s using algebra,calculus concept’s)
- Learn Python Libraries: : There are tons of machine learning libraries already written for Python. Just Learn it one by one .OpenCV : Helpful Analyzing Images/Videos and Applying Cascade’s and More etc.
- Andrew-Ng Course : There is a Excellent and Highly Recommended Free Course by Andrew Ng (Buy Book Now) at coursera, course is a very good starting point for you to get your understanding about algorithms in Theory and different concept’s to Machine Learning.
- Learn Scikit-Learn Library : (one of the most powerful API with different Algorithms,Powerful Data Encoders etc).
4.How you can implement machine learning ?
Benefits of Implementing Machine Learning Algorithms
You can use the implementation of machine learning algorithms as a strategy for learning about applied machine learning. You can also carve out a niche and skills in algorithm implementation.
Implementing a machine learning algorithm will give you a deep and practical appreciation for how the algorithm works. This knowledge can also help you to internalize the mathematical description of the algorithm by thinking of the vectors and matrices as arrays and the computational intuitions for the transformations on those structures.
There are numerous micro-decisions required when implementing a machine learning algorithm and these decisions are often missing from the formal algorithm descriptions. Learning and parameterizing these decisions can quickly catapult you to intermediate and advanced level of understanding of a given method, as relatively few people make the time to implement some of the more complex algorithms as a learning exercise.
You are developing valuable skills when you implement machine learning algorithms by hand. Skills such as mastery of the algorithm, skills that can help in the development of production systems and skills that can be used for classical research in the field.
Three examples of skills you can develop are listed include:
- Mastery: Implementation of an algorithm is the first step towards mastering the algorithm. You are forced to understand the algorithm intimately when you implement it. You are also creating your own laboratory for tinkering to help you internalize the computation it performs over time, such as by debugging and adding measures for assessing the running process.
- Production Systems: Custom implementations of algorithms are typically required for production systems because of the changes that need to be made to the algorithm for efficiency and efficacy reasons. Better, faster, less resource intensive results ultimately can lead to lower costs and greater revenue in business, and implementing algorithms by hand help you develop the skills to deliver these solutions.
- Literature Review: When implementing an algorithm you are performing research. You are forced to locate and read multiple canonical and formal descriptions of the algorithm. You are also likely to locate and code review other implementations of the algorithm to confirm your understandings. You are performing targeted research, and learning how to read and make practical use of research publications.
5.Can you become a machine learning Engineer?
he Answer is Yes. Nothing in impossible in this world but you have to give your 100% ability and need of your hardwork.
Is anyone can become a machine learning engineer?
No, no every one can become ML Engineer.
what are the prerequisites to become ML Engineer?
1.Excellent knowledge about computer programming.
2.Any programming language C, C++, JAVA, PYTHON(recommended) etc.
3. MATHS – more than average in Engineering mathematics.
4. Self Dedication & Hardwork – yes It is neccessory part to become a machine learning enginner
It is a subpart of Artificial Intelligenge (AI).
It is a future.
It is not so easy to learn, no everyone can do but You can.It matter of knowledge (any programming language ,Maths etc), Dedication and hard working.