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What is the best way to learn Artificial Intelligence for a beginner?

What is the best way to learn Artificial Intelligence for a beginner?

HARIDHA P 64 16-Jan-2023

What is the best way to learn Artificial Intelligence for a beginner?

Fundamentals

Brushing up on the fundamentals is the best approach to get started with AI. You can start with elementary mathematics and ideally work your way up to a coding language after that. It's usually advised to utilize Python because of its sizable and helpful community as well as the abundance of packages and modules that will help you along the way.

Here is something you might want to quickly mention.

  • Basics of linear algebra and matrices.
  • Calculus.
  • Chart Theory
  • Vectors.
  • Probability and statistics.

In addition, there are a few tools you might want to master that will improve your data handling.

  • Database fundamentals
  • SQL and SQL Joins.
  • databases that are relational and non-relational.
  • NoSQL data sources
  • Tables of data (Excel)
  • Data Frames and Data Series.
  • Data Formats (JSON, CSV, XML).
  • Regular Expressions.
  • Extract, transform, and load data.

Data preparation

Now that you can edit data, it's essential to study various methods that will enable you to turn unstructured data into structured data so that you can use Machine Learning algorithms to extract insights from it. These techniques include:

  • Analysis using Principal Components.
  • Reduction of dimensions.
  • Normalization.
  • Cleaning up the data, dealing with missing values, etc.
  • impartial estimators.
  • Extraction of features.
  • Sampling and denoising

These techniques will help you to organize your data to perform further analysis. From here, you have 3 directions to go to - Machine Learning, Data Scientist, and Data Engineer.

Engineer in Machine Learning

Machine learning is the use of algorithms that have been trained on data to uncover specific patterns and features to aid in the prediction and decision-making process based on new data. The quality of the output increases with the algorithm's accuracy. The choice of the best algorithm for any problem statement is therefore crucial. Following is a general outline for learning machine learning:

  • Become familiar with ideas like different input parameters and variables (categorical, ordinal, and numerical).
  • Concepts like gradient descents and cost functions.
  • Data sets used for training, testing, and validation, as well as over- and underfitting.
  • Recall, bias, precision, and variance.
  • After that, you can continue and master the various categories of machine learning algorithms that will aid you in solving particular issues.

The major groups are:

  • Object classification and regression problems are both handled by algorithms for supervised learning.
  • Algorithms for Unsupervised Learning in Clusters.
  • Boosting, bagging, and stacking in ensemble learning.
  • Reward-based learning algorithms are used in reinforcement learning.
  • Finding the algorithms that are most appropriate for a certain type of task is challenging; yet, learning these algorithms and the understanding behind them is not tough. Through diligent practice, you can brush up on your abilities.

Analyst of data

You can pursue a different professional path in the area of AI. To uncover the relevant information concealed inside the data, it entails experimenting with data and mathematical models. Statistics and data visualization are the two core topics of a data science course.

The areas of statistics that you should be knowledgeable about are as follows:

  • Probabilistic thinking.
  • Distributions that are continuous and discrete.
  • Testing hypotheses.
  • Statistical summaries and certain significant laws, including (LLN, CLT).
  • estimates like MLE and KDE, etc.
  • Intervals of Confidence.

You can utilize resources like - to create visualizations.

  • Python: seaborn, plotnine, Matplotlib, etc.
  • Internet – Vega-lite, D3.js, etc.
  • Dashboards, such as Tableau and Dash.

Engineer for Data

Data engineering is all about investigating the various facets of data. Relevant elements include:

  • Data Formats Summary
  • Data exploration.
  • Source and acquisition of data.
  • Integration of data
  • Fusion of data.
  • Enhancement and transformation.
  • OpenRefine and a survey.
  • Data warehousing and data lakes.

You can select advanced subjects like Deep Learning Engineer and Big Data Engineer in addition to these job options. However, these career pathways call for extensive training and expertise in prerequisite fields like data engineering and machine learning. This is the greatest and simplest starting point for your artificial intelligence adventure.


A passionate writer, blogger, language trainer, co-author of the book 'Irenic' and an enthusiastic learner. Interest includes travelling places and exploring.

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