Roadmap of Machine Learning
===⇒ Episode: 0 ⇐===
“A well-defined map is important to reach the destination. Likewise, a transparent path is necessary to excel in Machine Learning” — Sai
### Checking the learning path of ML
if path == "precise":
print("ML Master!!!")
else:
print("Stuck or confused with the procedure!!!")A precise roadmap was used to master the concepts of ML without any chaos on further employment. I am going to cover most commonly utilizing algorithms, data manipulation techniques, and furthermore in this blog series. In this particular episode, there will be a short note on the types of ML and the structured path of learning the concepts of ML.
What?
Machine Learning is one of the applications of Artificial Intelligence that supports the system with its ability to learn automatically and improve performance based on experience and without being specially programmed. The picture (a) gives you a clear idea about the types of Machine Learning, these are the types which are predominantly used in all sectors of industries based on their requirements. Additionally, I would like to add up few more types of ML that are used on the specific situations, namely transductive learning, active learning, inductive learning, deductive learning and multi-task learning.
Where?
Machine Learning has been used in multiple fields and industries. For instance,
- Medical field: Disease identification and diagnosis, robot surgeries…
- Social media services: People recommendations, face recognition…
- Predictions: Traffic predictions, weather forecast…
- Classifications: Email spam, malware filtering…
- Product recommendations: Amazon, Netflix…
- Virtual personal assistance: Alexa, Siri…
This blog series will cover the concepts of supervised learning and the last four nodes in the roadmap are making the ML applications in a structured way to maximize the performance. This organized roadmap notably gears up the knowledge, make ease of utilization of concepts on required situations. I believe that these concepts are extremely essential and could be suitable for many ML enthusiasts too. I will provide only vital information to master the concepts, and the info will be short and precise.
See you next week with episode#01, coming up with an interesting and frequently used algorithm. Until then Happy Learning
“If you light a lamp for somebody, it will also brighten your path”
Please share your comments and expectations. You can also email me directly or find me on LinkedIn. I’d love to hear from you if I can help you or your team with machine learning.
