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Machine Learning Tutorial For Beginners

This article gives you a clear understanding of the Machine Learning concept for beginners. So let’s start our Machine Learning Tutorial for Begnners blog post.

What is Machine Learning?

• Machine Learning is a science that allows the machine to learn from experience without being explicitly programmed. Or in other words, It’s the art of getting computers to learn and act as humans do.

What are Machine Learning pre-requisites?

Machine Learning Pre-requisites for Beginners are:

• Knowledge of probability and linear algebra.
• Knowledge of any computer programming language. Good to know Python programming language.
• Knowledge of Calculus, especially derivatives of a single variable and multivariate functions.

How does Machine Learning work?

Like a Brain!

Yes, Let me explain in simple terms,

If a “ model” is exposed to any image or an object, it will “predict” what the image/object is all about.

Here model and predict means:

• Model – is what we call a Machine Learning algorithm which is not coded i.e if the image is round and in orange color then it’s Orange fruit.
• Predict – As the name itself tells you that, the model will start predicting the objects, images, etc…which helps in storing the feedback. ( Image below)

In Technical terms

• When new input is given to the ML model, it analyzes the data and applies its learned patterns over the new data to make future predictions.
• Based on the final prediction, the model stores the feedback and can optimize the model using various standardized approaches.
• This approach is adopted in Machine Learning models.

Types of Machine Learning Algorithms

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Supervised Learning:

• This is a Task-Driven algorithm that uses training data and feedback from humans to learn the relationship of given inputs to a given output.

There are two categories of supervised learning:

Unsupervised Learning

• This is a Data-Driven  algorithm, this helps you when you do not know how to classify the data and you want the algorithm to find patterns and classify the data for you
• Examples of Unsupervised Learning: Apriori algorithm, K-means.

Reinforcement Learning:

• Reinforcement Learning is popularly known as the learning for error algorithm and this is one of the most popular types of Machine Learning Algorithm which is used in cars and industrial robotics.
• This algorithm aims to reach a goal while learning from the previous errors in a dynamic environment.
• Example: Markov Decision Process.

Why is Python a good choice for Machine Learning?

Here are some of the reasons :

• An extensive selection of libraries and frameworks
• The simplicity:  Python is Easy To Use
•  Python has Community and Corporate Support
• Python is Portable and Extensible
• A low entry barrier
• Flexibility
• Platform independence
• Good visualization options

Machine Learning Applications

Commute Estimation

• Google’s Map: Google Maps provide insights like traffic, construction roads, accidents by using the location data from smartphones. This is Machine Learning.
• Riding Apps:  Riding Apps like Uber OLA uses Al and ML to assist the user with the estimated price of a ride, optimal pickup location, and ensuring the shortest route of the trip.

Email Intelligence

• Spam Filters: Gmail uses AL and ML to filter Spam mail.
• Smart Replies: Gmail prompts simple phrases to respond to emails like “Thank You”, “Noted”, “Yes, I’m interested”. These responses are customized with the help of  ML and AI.

Evaluation and Assessment

• Plagiarism Checking: ML can be used to build a plagiarism detector.
• Robo-readers: With the help of AL and Ml algorithms “reading “ can be done by robots. Eg: the GRE uses a Robot called “ e-Rater”.

Social Networking

• Facebook: Friends suggestions, automatically reflecting friends’ faces is all because of AL, ML. Facebook uses AI and ML to identify faces.
• Instagram: With the help of ML algorithms, Instagram can make and auto-recommend emojis and emojis hashtags.

Medical Diagnosis and Healthcare

ML algorithms are highly used for:

• Handling inappropriate data.
• Explaining data generated by medical units.
• Also for effective monitoring of patients.

Machine Learning Limitations

• Ethics: Data mining has resulted in the collection of massive amounts of data, especially by large companies such as Facebook and Google. This is the highest level of privacy breach for the users which is unethical.
• DataLack of Data: If you feed a model with poor data, then the result will also be poor. This can happen because of two reasons: lack of data, and lack of good data.

So, Why should freshers learn Machine Learning?

Machine Learning is one of the important solutions for most of today’s problems be it a top MNC or a startup everyone prefers to go with Artificial Intelligence and Machine Learning. So, if you are a person who wishes to solve the problem and learn more about Machine Learning, check out our free PG program in machine learning that can help you to make your foundations much stronger.