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:
Introduction to Statistical Learning is also available online for free.
 
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.
 
"      Hands-On Machine Learning with Scikit-Learn and TensorFlow
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.