Machine Learning: A Beginner’s Guide

ml-a-beginners-guide

As we see the world pacing towards an era where people are thriving to inculcate automaticity in everything possible, your choice of the way for blending with the advancing technology seems to be apt enough.

We won’t bore you with the well-known and overly discussed applications of this tech, like Tesla’s self-driving cars, featured recommendations on Amazon, or how machine learning is profoundly contributing in analytics, predictions, and calculations all over the world.

Rather, we’ll provide you with what you’re here for, making you dive right into the process of how machine learning actually works.

Question: How does ML work?

Answer: Algorithms!

Question: What programming language should I use to learn ML?

Answer: We have listed the languages as per the area of application and current analysis.

  • Python (used by 57% of the users):
  • Areas of application:
  • Sentiment Analysis(44%)
  • Natural Language Processing/ Chatbots(42%)
  • Web Mining(37%)
  • C/C++ (used by 43% of the users):
  • Areas of application:
  • AI in games(24%)
  • Robot Locomotion(27%)
  • Network Security and Cyber Attack Detection(26%)
  • Java (used by 41% of the users):
  • Areas of application:
  • Customer Support Management(26%)
  • Network Security and Cyber Attack Detection(23%)
  • Fraud Detection(22%)
  • R (used by 31% of the users):
  • Areas of application:
  • Sentiment Analysis(13%)
  • Bioengineering/ Bioinformatics(9%)
  • JavaScript (used by 28% of the users):
  • Areas of application:
  • Customer Support Management(10%)
  • Search Engines(9%)

As you can see, currently, Python is prioritized and highly recommended for use in ML. Python provides some ML libraries like, Tensorflow, Scikit Learn, Seaborn, Matplotlib, which could be used according to your Machine Learning projects.

Question: What are the types of Machine Learning?

Answer: There are primarily three types of Machine Learning:

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Supervised Learning: Here, we make the machines learn by providing them with a training data set or labeled data, which includes the input data and the answers to it, called the response values. In supervised learning, by giving the machines these examples, we train them to make predictions for calculating the response to the new input values. So, we can say that supervised learning has model training values or the already labeled data used for predictions in future problems. Example: Using the previous house sales, we can predict the house sales in future.

Unsupervised Learning: Unlike supervised learning, in unsupervised learning, we do not have historical labels for prediction. In this case, we train the system to draw inferences from the input data without the labeled responses, look for patterns in the data and find a structure in it. For example, from the given information about heights and weights for the breeds of dogs, the computer can identify which breed does the dog belongs to.

Reinforcement Learning: We can say that this type of machine learning is inspired by behaviorist psychology, i.e., the computer learns decision making on its own using trial and error methods. Here, we train our computer by telling it, on its every move, that whether it made a right decision or not. This way, it gets to learn by its own behavior and implements this learning in the future decisions.

For problem-solving in ML, you’ll have to analyze the type of problem and try to figure out that which area of Machine Learning would be considered best for solving it. Once you know it, you can use the concerned algorithms for the solutions.

If you have decided to go further in this field, these are some of the algorithms you will get your hands on while solving the problems using ML:

Classification Algorithms

Anomaly Detection Algorithm

Regression Algorithm

Clustering Algorithm

Reinforcement Algorithm

For solving any problem using machine learning, you’ll have to carry out certain steps, that are, acquiring the data, cleaning the data, performing train test split, training the model using the training set, and at last, evaluating the model using the test set.

What we’ve talked about, is enough to give you a fair bit of insights into the world of Machine Learning. However, it goes without saying that AI and ML are the widest spreading fields of our time. It’s only fair to say that if you’re interested in this domain, you should look for courses that’ll help you get deeper insights.

Oh, did we say ML courses? We, at Coding Ninjas, have both offline as well as an online course for Machine Learning where we talk in depth about everything you need to know if you’re setting out to master Machine Learning. We recommend you give us a visit!