Getting Started Career With Machine Learning

What is Machine Learning?

 

Machine Learning in the field of computer science basically originates from artificial intelligence so that we can also say that Machine Learning is the subfield of artificial intelligence.

Machine Learning involves an adaptive mechanism that enables the computer to learn from experience, learn by examples and these learning capabilities can improve the performance of the intelligence system over time.

you can read more on this article from Machine Learning introduction from APTRON.

 

Step involved in Machine Learning

 

  1. Problem understanding: In this process, we basically try to understand the client needs, what they want to achieve from their datasets. In this phase, we generally ask some questions like what you want to achieve from these datasets or what is your scientific goal?
  2. Data Preprocessing: In this phase, we generally import data and perform exploratory data analysis operations. In this phase basically, we transform raw data into an understandable format.
  3. Implementing model: In the previous phase, we got some pattern of data and also understood the problems type either classification or regression. We develop datasets and divide it into training and testing sets, fit it into our model, validate it. In this phase, we generally use different classifications or regressions algorithms like linear regression, logistic regression, support vector machine, etc.
  4. Improve results: In this, we try to improve our results, we try to find the best model, we also combine some algorithm (this process is also known as Ensemble methods) to make robust results.
  5. Communicate and visualize the results: In this phase, we have been achieved our goal which decided in the first phase, now time talk with clients to determine the result of the project of success or failures.

 

Getting Started With Machine Learning

 

Step 1. Programming Tools For Machine Learning: Although there are many programming languages that offer Machine Learning works but I prefer Python, Here I am confirming one thing is i prefer Python because of I have good grasp of Python than R or other programming languages but honestly I can say you that all languages or tools has it own pros and cons but a person who is keenly interested in this field always hands-on with these programming and tools and always know when and where he/she can uses these tools.

So I would like to recommend you for choosing Python or R according to your interest.

Step 2. Mathematics For Machine Learning

Mathematics is an essential part of Machine Learning. You have to good understanding of Statistics, Algebra, Probability, Calculus, Coordinate Geometry. You can take several online courses from different available resources like MOOCs, youtube channels, online blogs, online mathematics websites, etc.

If you are started with python for Machine Learning then you can take help of numpy, scipy, pandas libraries.

Step 3 Exploratory Data analysis / Data preprocessing:

This is the most important stage for Machine Learning projects. A good Machine Learning professional or Data scientist always give most of the time on this stage. In this step, you have to understand about data preprocessing means how to process with unstructured data and convert it into a structured way. In this way we basically deal with variable identifications, missing value, outliers, convert categorical data into a continuous format and another feature engineering according to the requirement, etc. We also do analysis or visualization of variables in the way called univariate analysis, bivariate analysis, multivariate analysis of data. This process involves in exploratory data analysis.

Step 4 Common Machine Learning for beginners

Remember Machine Learning involved step 3 in which we talked about the model, Model is nothing but Machine Learning algorithm which is created by training process, Since there are three types of Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

 

So we use algorithm according to our problems for continuous value prediction we use Regression algorithms like Linear regression, For classification, we use Logistic Regression, SVM etc For the unsupervised task we use Clustering and PCA etc. Here is a list of common Machine Learning algorithms.

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