Introduction to Machine Learning
Machine Learning is a subset of artificial intelligence which focuses mainly on machine learning from their experience without being explicitly programmed and making predictions based on its experience. So instead of you writing the code, what you do is you feed data to the generic algorithm, and the machine builds the logic based on the given data.
Types of ML are –
- Supervised Learning – train me
- Unsupervised Learning – I am self-sufficient in learning
- Reinforcement Learning – hit and trial
Purpose of Machine Learning –
Machine learning is required for tasks that area unit too complicated for humans to code directly. Some tasks area unit therefore complicated that it’s impractical, if not not possible, for humans to figure out all of the nuances and code for them expressly. So instead, we provide a large amount of data to a ML algorithm and let the algorithm work it out by exploring that data and searching for a model that will reach what the programmers have set it dead set reach.
Is Machine learning hard to learn or is it difficult?
Machine Learning is in itself a huge learning. The term ML is self-explanatory. Machines learn to perform tasks that aren’t specifically programmed to try and do. It being a vast field, knowing just python is not well enough. There are lot many other things you should know to be a ML engineer. Continuous effort and hard work will make you better in this field.
Who can learn Machine Learning –
There is an increasing demand for skilled ML engineers across all industries. ML course for the following professionals in particular –
- People those have interest in learning
- Developers aspiring to be a data scientist or ML engineer
- Analytics managers who are leader of a team as analysts
- Business analysts those who want to be familiar data science techniques
- Information architects who want to gain expertise in ML algorithms
- Analytics professionals who want to work in ML or artificial intelligence
- Graduates looking to build a career in data science and ML
- Experienced professionals who would like to harness ML in their fields to get more insights
Prerequisites to start learning Machine learning –
Machine Learning curriculum doesn’t presume or need any previous information in machine learning. However, to know the ideas given and complete the exercises, we have a tendency to suggest that students meet the subsequent conditions –
- Linear Algebra
- Trigonometry
- Statistics
- Calculus
- Probability
- Graph Theory
- Differential Equations
- Python (or R)
How to start with Machine Learning –
For starting a career in ML is broken down into a 5-step process –
- Adjust Mindset – Believe yourself that you can practice and apply ML in your project.
- Pick a Process –Use a scheduled process to work through problems.
- Pick a Tool –Select a particular software or tool for your level and map it onto your process.
- Practice on Datasets –Select a real and valued datasets to work on and practice the process.
- Build a Portfolio –Collect results and check your skills.
Websites and Blogs to learn Machine Learning for free –
With the increased usage of ML applications in various sectors, it has become utmost important for a learner to be familiar to the various concepts involved in ML algorithms and ML models.
Here are few articles you can refers –
Ways You Can Succeed in A Machine Learning Career –
- Understand what machine learning is – Having experience and understanding of what ML is, understanding the basic math behind it, understanding the alternative technology.
- Be curious –ML is modern things that will only continue to evolve in the future, so having a healthy sense of curiosity and love of learning is essential to keep learning latest technologies and what goes with them.
- Learn Python and how to use machine learning libraries – Programming exercises in ML Crash Course are coded in Python using TensorFlow. No previous expertise with TensorFlow is needed, but you should feel comfortable reading and writing Python code that contains basic programming constructs, such as function definitions/invocations, lists and dicts, loops, and conditional expressions.
- Be a team player –Today, when you are working in ML, you are most likely working as part of a team, and this team would comprise people who have direct interaction with the business. So, it means if you want to be successful as a ML practitioner today, you must be ready and able to interact with the business and be a team player.
- Gain knowledge of the industry you want to work in –ML, much like any data-driven job, doesn’t exist in a vacuum. Every business and company has distinctive goals and wishes. That being the case, the a lot of you’ll study your required business, the higher off you will be.
Career choices in Machine Learning –
The jobs available are more specific –
- ML Researcher
- AI Engineer
- Data mining & analysis
- ML Engineer
- Data Scientist
- BI (Business Intelligence) Developer
Future of ML –
Google says “ML is the future,” and the future of ML is going to be very bright. As humans become more addicted to machines, we’re witnesses to a new revolution that’s taking over the world, and that is going to be the future of ML.
