Machine Learning Course Syllabus – 2022
Machine Learning Introduction
Machine learning is a
constituent of computer science that uses algorithms to mimic how humans learn.
It utilizes statistical strategies to train algorithms and make predictions.
The accuracy of these predictions enhances over time.
As the amount of data
increases and big data persists to rise, the demand for data scientists
increases. Machine learning is one of the most in-demand Data Science skills,
allowing data scientists to increase the predictive accuracy of software
applications without explicitly programming them to do so.
These algorithms use
historical data to predict output values, and these insights and predictions
enable businesses to make intelligent decisions.
Machine learning is
crucial because it gives companies insight into business patterns and customer
behavior trends. Most leading companies like Uber, Google, and Facebook focus
on machine learning as the main focus of their operations.
Understand the concept of machine learning
While many people
usually get muddied between machine learning and artificial intelligence,
machine learning refers to instructing a computer network on how to make
specific projections when fed data. Machine learning also allows computers and
modern machines to understand and improve automatically without being
explicitly programmed.
The machine learning
process begins by observing the data to look for similar patterns in the
information and to make more prudent judgments in the future based on the
standards provided. Further, machine learning involves examining vast amounts
of data as its results are delivered faster and more accurately, which helps in
identifying threats or opportunities that bring profit.
Being skilled in
machine learning and artificial intelligence is essential for developing modern
business fields because the algorithms in machine learning are challenging. It
involves ingenuity, experimentation, persistence, and guidance from the best
educational institute to help students get better job prospects.
Machine Learning Course Outline
The Machine Learning course syllabus is
divided into chapters to make learning easy for students.
“ Introduction
to Machine Learning and
Artificial Intelligence
” Supervised learning and linear regression
” Classification and logistic regression
” Decision tree and random forest
” Naive Bayes and Support Vector Machine
” Unsupervised learning
” Introduction to Deep Learning
In addition to those,
some of the core subjects that students understand in a machine learning course are as follows:
” Programming for problem-solving
” Engineering Physics.
” Mathematics.
” Application-based programming with Python.
” Database management system.
” Pattern recognition.
” Computational learning theory.
What are the essential skills for machine learning?
Here are some top
skills needed to become a machine learning expert.
Statistics
Algorithms
constructed on machine learning codes require statistics such as variance
research and theory testing.
In addition,
statistics play a vital role in developing machine learning algorithms.
Therefore, gaining knowledge of statistical mechanisms is critical to
accelerating your career to become an expert in machine learning.
Programming skills
Coding is vital in
every field of computer science, and it’s no different with machine learning.
Therefore, coding is one of the core skills that every company expects from a
machine learning candidate.
Additionally,
knowledge of Python coding helps in online scripting websites and other machine
learning requirements. Also, your core skills such as algorithms, computer
architecture, and data structures must be strong.
Two excellent book companions
In addition to taking
one of the video courses, if you’re pretty new to machine learning, you should
think of reading these books:
This book contains
detailed, straightforward explanations and examples that strengthen your
mathematical intuition for many basic machine learning techniques. This book is
more theoretical but contains many exercises and examples using the R
programming language.
Excellent addition to
the previous book, as this text, focuses more on the application of machine
learning using Python. Along with any of the courses below, this book will boost
your programming skills and lead you on how to involve machine learning in
projects instantly.
Best Machine Learning Courses in 2022:
Machine learning specialization Coursera
Deep Learning Specialization Coursera
Machine Learning Crash Course Google AI
Machine Learning with Python Coursera
1. Machine Learning Coursera
This is the course
against which all different machine learning courses are considered. This
beginner’s course is guided and prepared by Andrew Ng, Stanford professor,
co-founder of Google Brain, Coursera, and vice president, who has grown Baidu’s
AI team to thousands of scientists.
The course uses the
Octave open-source programming language for assignments instead of Python or R.
This may be a hurdle for some, but Octave is a simple way to learn the basics
of ML if you are a complete beginner.
Overall, the teaching
material is highly comprehensive and intuitively formulated by Ng. The math
needed to understand each algorithm is fully explained, with some explanations
of calculus and a refresher on linear algebra. The course is self-contained,
but prior knowledge of linear algebra would help.
Contributor:
Andrew Ng, Stanford
Price:
Free audit, $79 has to pay for
certification of this machine learning course
Then you can
comfortably move on to a more advanced or specialized topic like Deep Learning,
ML Engineering, or anything else that interests you.
This is indeed the
best course to start with a newbie.
2. Deep Learning Specialization Coursera
Also guided by Andrew
Ng, this specialization is a more refined series of courses for anyone
interested in neural networks and deep learning and how they solve many
problems.
The assignments and
lectures in each course use the Python programming language and the TensorFlow
library for neural networks. Naturally, this is an excellent follow-up to Ng’s Machine Learning course, as you’ll get
a similar style of lectures, but now you’ll be exposed to using Python for
machine learning.
Contributor:
Andrew Ng, deeplearning.ai
Price:
Free audit, $49/month has to pay for
certification of this machine learning course
3. Machine Learning Crash Course Google AI
This course is from
Google AI Education, a free platform that combines articles, videos, and
interactive content.
The Machine Learning
Crash Course wraps the topics required to solve ML problems as quickly as
possible. As in the previous course, the programming language is Python, and
TensorFlow is introduced. Each major part of the curriculum includes an
interactive Jupyter notebook hosted on Google Colab.
The video lectures
and articles are short, and to the point, so you can move quickly through the
course at your own pace.
Provider:
Google AI
Price:
One can get certification of this ML
course for free
This is the best machine learning course as its
certification is also free on this list if you’ve been playing around with
ML but want to cover all your bases. The course covers many nuances of machine
learning that could otherwise take hundreds of hours to learn without
difficulty.
There doesn’t seem to
be a certificate of completion at the time of writing, so if you’re looking for
something like that, this course may not be the best fit.
4. Machine Learning with Python Coursera
Another course for
beginners, but this one focuses only on the most basic machine learning
algorithms. The instructor, slide animations, and algorithm explanations
combine nicely to give you a reflexive feel for the basics.
This course uses
Python and is relatively lighter on the math behind algorithms. With each
module, you’ll be able to create an interactive Jupyter notebook in your
browser to work with the new concepts you’ve just learned. Each notebook
reinforces your knowledge and provides specific instructions for using the
algorithm for accurate data.
Provider:
IBM, Cognitive Class
Price:
Free audit, $39/month per certificate
One of the best
things regarding this course is the practical advice for each algorithm. When
the instructor becomes familiar with a new algorithm, he will provide you with
information about how it performs, its benefits and drawbacks, and in what
situations you should use it. These points are often missed in other courses,
and this information is essential for new students to understand. Wider
context.
Wrapping up
Machine learning is
fun and exciting to learn and experiment with, and I hope you’ve found the
course above that fits your journey into this exciting field.
Advanced Certification in Applied Data Science, Machine Learning & IoT By E&ICT Academy, IIT Guwahati
9 months
Certification in Data Science and Machine Learning With Python By The
IoT Academy Noida
180 Hrs
