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Data Science training road map to becoming Machine Learning Engineer
What is machine learning?
This technique begins with feeding the machines high-quality data and then trains the machines by creating different machine-learning models utilizing the data and different algorithms. (In brief, machines learn autonomously without human hand-holding.) The type of data we have and the sort of task we're seeking to automate will influence the algorithms we use.
Why is machine learning necessary to learn?
A tremendous amount of data is being generated every minute as the digital age advances, and high-speed internet is now widely available. This is a key aspect in the development of automated systems that can accurately use various algorithms for large data sets to handle data at such a threshold. This approach is now being used by businesses of all sizes to manage costs, reduce risk, and enhance the quality of their goods and services. Today, many sectors have adopted this technology, and it will soon play a significant role in our lives.
In this article, we will see the data science course roadmap and when you go through training what could be data science training syllabus will be
How to Start Learning Machine Learning?
This is a rough data science training roadmap. You can continue on your journey to becoming a machine learning engineer with incredible talent by doing so. Of course, you can always change the steps to suit your needs to achieve the desired outcome!
When we talk about data science course mind maps, first thing that comes to mind is models what are the different models will be having in machine learning mainly we have two types of models.
1. Supervised Learning
As the title suggests, supervised learning involves a supervisor who serves as a teacher. In its simplest form, supervised learning refers to the process of teaching or training a computer system utilizing labeled data. This implies that the appropriate response has already been given to the relevant data. For the supervised learning algorithm to analyze the training data (set of training examples) and provide an accurate result from labeled data, the machine is then given a fresh set of examples (data).
Consider the scenario when you receive a basket full of various fruits. The first step is to teach the machine each variety of fruit individually as follows:
- The label "Apple" will be present if the item is rounded, has a depression at the top, and is red.
- If an object has a long, curved cylinder shape and the hue green-yellow, it will be given the name "-Banana."
- Now imagine that you have provided a new, distinct fruit—say let's a banana from the basket—and asked someone to identify it after training the data.
As a result of earlier data, the machine has already learned certain things, therefore this time it must be used carefully. It will first categorize the fruit based on its shape and color, confirming that it is a BANANA and placing it in the category of Bananas. As a result, the computer gains expertise from training data (a fruit basket) and applies it to test data (new fruit).
Supervised learning is categorized into two categories of algorithms:
- Classification: When the output variable is a category, such as "blue" or "black," "disease" or "no disease," in that case we will use classification.
- Regression: When the output variable has a real value, like "price" or "weight," then we will use regression
Supervised learning works with or gains knowledge from "labeled" data. This suggests that some data has already been assigned the right response.
- Logistic Regression
- Naive Bayes Classifiers
- K-NN (k nearest neighbors)
- Neural networks
- Decision Trees
- Support Vector Machine
2. Unsupervised Learning
The process of teaching a computer to use unlabeled, unclassified data and allowing the algorithm to operate on the data without supervision is known as "unsupervised learning." Without any prior.
Unlike supervised learning, no supervisor is present, hence the machine won't be trained. As a result, the machine has a limited capacity to independently identify the hidden structure in unlabeled data.
Consider the scenario where it is shown a picture of dogs and cats that it has never seen before.
Therefore, we cannot classify the machine as "dogs and cats" because it is unaware of the characteristics of dogs and cats. However, it can divide them into groups based on their patterns, similarities, and differences, thus we can simply divide the image above into two halves. The first section might have only images of dogs, and the second segment might have just images of cats. You have no prior knowledge of this, hence there are no examples or training data.
It enables the model to function independently and find previously unnoticed patterns and information. It focuses primarily on unlabeled data.
By the kind of algorithms they use, unsupervised learning is divided into two categories:
- A clustering challenge involves finding the natural groupings in the data, such as classifying customers according to their shopping habits.
- An association rule learning challenge arises when you need to identify general principles for your data, such as "people who buy X also tend to buy Y."
- Hierarchical clustering
- K-means clustering
- Principal Component Analysis
- Singular Value Decomposition