Have you ever amazed by how google home and amazon Alexa assist in finding information.
When asked over voice or have you ever wondered
How e-commerce companies send you personalized emails for shopping suggestions
based on the products that you bought online recently
well it's all because of machine learning that these virtual assistants
and product recommendations work.
We will give you this step-by-step process
that you need to follow to build a successful career in machine learning
So what is machine learning
ML is a subset of artificial intelligence
that uses data algorithms and statistical techniques to build intelligent systems
These systems learn by themselves and improve with experience
The goal of machine learning is to create computer models
That can imitate
Our behavior ML has found its usage in almost every business sector
From self-driving cars medical imaging and
Diagnostics speech recognition facial recognition to online fraud detection
All these are possible because of ML
In recent years ai and ml technologies have made several breakthroughs and
The rising demand for ai applications across different industries
has led to the significant growth of machine learning.
As for markets and markets.com
the machine learning market expected to grow to 9 billion by 2022 at ac agr of 44%.
Another report from verified market research.com suggests
that the machine learning market valued at 2.4 billion us dollars in 2019.
and projected to reach 47.29 million usc by 2027
growing at a cagr of 44.9 percent from 2020 to 2027.
keeping these facts and forecasts in mind
let's look at the roadmap and critical skills required
to build a career in machine learning
1 - Programming Skills
For a machine learning engineer programming languages
form the building blocks to develop complex machine learning models
you need to learn at least one programming language
python or
R.
you should be familiar with various computer science concepts
such as data structures including stack cues trees and graphs
algorithms for searching and sorting dynamic and
greedy programming space and time complexity etc
you also need to know
libraries like numpy pandas matplotlib seaborn d player tidier and ggplot
for data analysis and visualization
2 - Applied Mathematics
While solving business problems using machine learning
you have to use machine learning algorithms
to understand the mechanisms behind the algorithms
you need to have a good knowledge of mathematical concepts
Similar as linear algebra calculus statistics and probability
so mathematics and machine learning is
not processing the numbers
but understanding what is happening
why it's happening and how to the best results.
3 - Data Wrangling And Sql
Data analysts and machine learning specialists
often work with raw data
collected from various sources that are not fit for analysis
it has observed that 80 percent of data analysis spend too much time on data wrangling
so it's crucial for machine learning experts to clean structure and
enrich raw data into desired format and make
it ready for analysis using data wrangling techniques
sql is another crucial set that you should carry
machine learning tasks involve using data stored in the form of tables
that are present inside relational databases
A good understanding of sql commands enables
you to store manipulate retrieve and handle structured data.
4 - Machine learning algorithms
It's essential to grasp all the standard machine learning algorithms
Implementing any machine learning techniques requires choosing
the suitable model determining the correct learning method and
an in-depth understanding of hyper parameter tuning.
so you should be good with different supervised unsupervised and
reinforcement learning algorithms.
such as linear regression logistic regression
svm knn decision trees k means clustering etc.
5 - Data modeling and evaluation
Aim of a machine learning engineer is to train
the best performing model possible
Depending on the problem at hand you will need to choose a suitable error measure and
An evaluation strategy for a machine learning model
The most vulnerable part of a machine learning candidate's
resume is the absence of experience working on diverse machine learning projects
With all the essential skills acquired
you can now build an impressive machine learning portfolio
Highlighting some exciting machine learning projects
Machine learning engineers need a portfolio that showcases
their expertise to put in place machine learning techniques to real-world problems,
With your resume ready you can now look for
the best machine learning jobs in top product companies and startups.
You can become a machine learning engineer a data scientist
a data analyst or a research scientist.
Some of the top companies hiring for machine learning roles are
Amazon
Ibm
Uber
Graminer
Navidia and
Linkedin.
Machine learning engineers are some of the highest paid professionals in the world
According to glassdoor.com the national average salary for machine learning engineers
In the united states is one lakh 31 000 us dollars per year in india you can earn 8 lakh rupees per annum
This payment may vary based on your experience
the industry you are applying for and the company policy
There are immense opportunities in machine learning for sectors
Such as e-commerce manufacturing logistics retail and healthcare.