Introduction

Artificial intelligence (AI) is an interesting field that is growing. Although we are yet to have robot servants, artificial intelligence is part of our lives. AI is a multifaceted field that is applicable in every industry, thus making AI jobs to be in high demand. It is widely applied in fraud prevention, risk modeling, product enhancement and service operation optimization.

What is Artificial Intelligence?

Artificial intelligence is the creation of programs in computers that can mimic associated tasks with human intelligence. The technology offer solution to problems through large data sets and computer programming. The artificial intelligence field includes natural language processing, deep learning, and machine learning that enable computers to gain knowledge from experience and conduct human-like tasks more efficiently than human beings. The AI is categorized as weak or narrow AI. It is a case where computers perform particular tasks through the recognition of large data set patterns. For example chess bots. Strong AI is a type of artificial intelligence related to science fiction robots.

Why Learn AI?

Artificial Intelligence is at the forefront of obtaining solutions to problems in society like climate change, pollution, and disease. In the economy, it is also a growing sector. Gaining knowledge on AI, prepares an individual for rewarding but challenging tasks that pay well.

What Does an AI Engineer Do?

The duties of an AI engineer vary depending on their industries. However, AI engineers create AI applications and systems to enhance efficiency, improve performance and make better decisions.

The requirements for AI engineering include;

• Achieving objectives through AI methods

• Solving problems with machine learning, probability analysis, and logic.

• Steering and monitoring development projects through system analysis

• Understanding and applying best practices in robotics, data mining, data processing, and speech recognition.

Steps for Learning AI Effectively

1. Understanding Prerequisites

2. Ace AI Theory

3. Mastering Data Processing

4. Working on AI Projects

5. Learning and Working With AI Tools

6. Opting for AI Courses

7. Applying for an Internship

8. Getting a Job

   One of the greatest challenges with learning artificial intelligence is having no knowledge of where to begin. It is a large field that has several components. Most of the AI concepts depend on formal logic and advanced math. To assist in overcoming the challenges, the AI field has been broken down into manageable step-by-step guidance.

Understanding Prerequisites

It is important to have solid foundation in the areas below before beginning to learn AI.

• Computer science basics

• Algorithms

• Probability and Statistics

• Mathematics

• Programming

• Data Structures

Ace AI Theory

Once you’ve learned the prerequisites, you’re ready to dive into AI theory. Regardless of whether you learn AI through an in-person class, with a self-paced online course, or in piecemeal fashion with YouTube videos, you’ll need to cover the same basic theoretical concepts. Here are some of the most important tenets that you’ll need to learn:

Problem-Solving

The purpose of AI is to solve a problem, which involves a number of techniques, including algorithms and heuristics. An AI system includes an agent and its environment. In AI, an agent is the program that makes decisions. A problem-solving agent in AI is focused on achieving its goal. Once the goal is formulated, a process for solving the problem is created through problem formulation. This involves several components, including:

• The initial state of the agent

• The possible actions the agent can take

• A transaction model that describes each action

• A goal test to determine if the goal has been achieved

• The cost of each action path

Reasoning

Reasoning is the process of drawing conclusions or making predictions based on your existing knowledge. Because machines aren’t capable of thinking, they have to be programmed to do this kind of reasoning with algorithms.

Deductive Reasoning

This type of reasoning uses existing data to determine if the premise of an argument is valid. It’s a kind of reasoning that applies general principles to a specific case.

Inductive Reasoning

Inductive reasoning produces a general conclusion from specific observations. In inductive reasoning, a conclusion can be false even if all of the observations are true.

Abductive Reasoning

Abductive reasoning is the process of drawing a conclusion that most likely fits the observations. This type of reasoning is used by doctors to make medical diagnoses.

Common-Sense Reasoning

Common-sense reasoning is an informal type of reasoning that relies on experience. Common-sense reasoning is most widely used in the AI field of natural language processing to help computers communicate more effectively with humans.

Monotonic Reasoning

In monotonic reasoning, once a conclusion is reached, it will never change, even if additional facts are added. Any theorem that proves an example is using monotonic reasoning. In AI, monotonic reasoning can be used for applications such as content filtering.

Non-monotonic Reasoning

In non-monotonic reasoning, the conclusion may be invalidated if new information is added. Incomplete and uncertain models use non-monotonic reasoning. This type of reasoning is useful in AI applications such as robotic navigation systems.

Data Manipulation

AI relies on data sets to learn and to make predictions, so you’ll need to be skilled at structuring data into a useful format. You will need to create programs that identify connections among data sets. SQL is the programming language used to manage databases.

Natural Language Understanding

Natural language understanding is a subset of natural language processing that uses programming to understand human speech. It allows computers to understand human speech without the formal syntax of computer languages. Natural language understanding uses algorithms to analyze human speech and format it as a structured data model based on sentiment, named entities, and numeric entities. Voice-enabled assistants and chatbots both use natural language processing.

Computer Vision

Computer vision is the process of training computers to observe and understand visual input. It allows computers to extract information from images, videos, and other visual inputs. The program can then use that information to take action or make recommendations. Computers can analyze visual information much faster than humans, analyzing thousands of images per minute.

Automated Programming

Automated programming is a type of computer program that generates the code for another program based on a set of specifications. One example of this is DeepMind’s AlphaCode, which writes computer programs well enough to rank in the 54th percentile of human programmers when tested in coding challenges.

AlphaCode was given a set of challenges used in coding competitions such as transforming a random string of letters into another random string of the same letters using limited inputs. AlphaCode approached this challenge by generating a huge number of possible answers. It then ran the code, tested the output, and tested the answer to select the best option.

Master Data Processing

Data processing is such a significant aspect of AI that it’s a field unto itself. Big data permeates all aspects of modern life. Almost all businesses incorporate data-driven decision-making into their strategies. This is possible through machine learning, which relies on processing massive data sets. If you’re interested in the big data element of AI, you might enjoy one of the following careers.

Data Preprocessing

Data preprocessing involves transforming raw data into an understandable format and ensuring its quality. The quality of data depends on its: accuracy, consistency, completeness, timeliness and trustworthiness.

Machine Learning E\ngineering

A machine learning engineer builds AI systems that automate predictive models based on machine learning. Their systems use huge data sets to generate and develop algorithms that learn from results and refine the process of performing future operations for more accurate results.

What Is Machine Learning?

Machine learning is a branch of AI in which computers are taught to learn and improve on their processes with minimal human intervention. Machine learning programs can even detect more complex and subtle patterns than humans can. This happens through the use of data sets and pattern recognition. There are two main types of machine learning: supervised learning and unsupervised learning.

In supervised learning, you can collect or produce data from a previous output of machine learning. You give the computer a training set of labeled data points.

In unsupervised learning, the algorithm tries to discern the inherent structure of the data without a training set. This can help you find many unknown patterns in your data.

How To Learn Machine Learning

Machine learning is a specialized field of AI, so you’ll still need to understand the prerequisites and general AI theory. In addition, here are some steps that you can take to specialize in machine learning:

• Learn Python

• Learn data science tools such as Jupyter and Anaconda

• Learn data analysis tools like Pandas, NumPy, and Matplotlib

• Use the Python library SciKit-Learn to find patterns in your data

• Learn to build deep learning neural networks

• Work on your own projects

How Is Machine Learning Related to AI?

Machine learning is a branch of AI. Machine learning is one way to implement AI principles, giving computers the ability to learn on their own without being explicitly programmed.

Data Scientist

Data science is closely related to machine learning engineering, but they aren’t the same. Data science is a broad field aimed at extracting insights from data. Machine learning is one tool data scientists use.

As far as education goes, data scientists often have advanced degrees in a variety of subject areas, while machine learning engineers usually come from the field of software engineering.

What Is Data Science?

Data science is the process of using scientific methods, processes, algorithms, and systems to extract meaning and insights from unstructured data.

What Does a Data Scientist Do?

Data scientists use machine learning or deep learning models to develop solutions for business problems. Unlike machine learning engineers, data scientists often use existing machine learning tools to process data, although they may have to develop novel applications if needed. After identifying business problems that can be solved with machine learning, a data scientist will then develop custom algorithms and models to solve those problems.

How To Learn Data Science

As with machine learning, mastering the prerequisites and fundamentals of AI is necessary for learning data science. Because it’s a branch of AI, many of the same principles apply. Once you’ve mastered the basics, you can continue your learning journey by:

• Mastering data cleaning, which will be a huge part of your job

• Using existing data sets to work on your own projects

• Gain experience and contribute to valuable work through data science volunteering

How Is Data Science Related to AI?

Data scientists use AI to do their jobs, so there’s a lot of overlap between data science, machine learning, and artificial intelligence. The biggest difference among the three is that data science uses AI and machine learning to produce insights. Data science relies on humans to gain insights and make conclusions from the results produced by machine learning.

Data Engineer

It can be difficult to distinguish between a data scientist and a data engineer, particularly if you look at job postings. Data scientists are often expected to also fill the role of a data engineer. However, these two roles are distinct.

What Is Data Engineering?

Data engineering is the process of designing and building pipelines for transforming data into a usable format. These pipelines take data from different sources and combine them into a single source for further analysis.

What Does a Data Engineer Do?

Data engineers build and maintain data infrastructure that serves as the foundation for all other data functions. They use databases, servers, and large-scale processing systems to transform unstructured data into usable formats. They do this through a process called ETL (extract, transform, load) using tools such as SQL, Cassandra, and BigTable.

How To Learn Data Engineering

Data engineers need to be well-versed in the following skills:

• Data warehousing

• ETL tools such as Xplenty or Hevo

• Machine learning

• Database systems such as SQL

• Programming languages such as Python and Julia

• Algorithms and data structures

• Distributed systems

How Is Data Engineering Related to AI?

Data engineering provides the raw materials for data-related AI tasks. Machine learning and AI require such massive amounts of data that it wouldn’t be possible to scale them without data engineering. The exponential growth in data that’s created on a daily basis feeds AI, but the majority of it is unstructured. Data engineering transforms unstructured data into usable formats for AI developers.

Work on AI Projects

The best way to develop an understanding of AI algorithms is to build them from scratch. Start with projects that require simple algorithms and then take on harder projects, gradually increasing the skill level required. When you’re trying to master AI, theory alone isn’t enough. A practical, hands-on approach will cement your learning and boost your skills.

How To Choose Projects

There are several ways to choose AI projects. Because AI is applicable to every industry, the options can seem overwhelming. Start by choosing projects based on your interests, fundamental projects, and projects that add value to your community.

Choose a Project Based on Your Interests

Pick a project that combines learning AI with your other hobbies and interests. If you’re an avid gamer, design a game you can play against. Chess is a classic option.

Work on Fundamental Projects

There are some traditional projects that are routinely recommended for beginners. These projects are fun and teach some foundational skills. Although there is controversy over what’s considered foundational in AI, there’s no doubt that learning to train one model on a huge amount of data and then adapting it to different applications is a fundamental skill in AI. Even if you eventually decide this model isn’t foundational, it still has many practical uses.

One common fundamental project recommended for beginners is using Enron’s email database to analyze social networks or detect anomalies. The Enron debacle in 2001 was one of the most massive fraud scandals in recent history. The investigation resulted in a database of more than half a million emails that are publicly archived.

Build Professionally and Personally Valuable Projects

One way to make your portfolio stand out is to include projects that are important to you personally and add value to the community. Choose an issue that is significant to you and design an algorithm to address a problem related to it, like using social media posts to predict depression.

Ideas To Get You Started

If you can’t think of any projects or you’re just looking for inspiration, here are some ideas:

• Fake news detection

• Translation

• Stock price prediction

• Facial recognition

• Human activity recognition

• Sales price forecasting

Learn and Work With AI Tools

There are many AI tools you can choose from, but these are some of the most popular frameworks and tools currently in use.

SciKit-Learn

SciKit-Learn is one of the most popular tools in ML libraries. It’s used with unsupervised and administered calculations. SciKit is a great tool to use for fledglings.

TensorFlow

TensorFlow can be used for a variety of machine learning tasks, but it’s especially useful for the training and inference of deep neural networks.

PyTorch

PyTorch was developed by Facebook. It’s used primarily for applications such as natural language processing and computer vision.

Opt for AI Courses

Although you could take a piecemeal approach to learning AI, choosing a formal course will accelerate the process and provide some structure. A class can provide accountability, feedback, and resources if you run into questions or problems.

Depending on how you learn best, you can choose a self-paced MOOC, a formal graduate degree program, or a bootcamp.

Apply for an Internship

Once you’ve finished your classes and built a portfolio, applying for an internship is a great way to get some real-world experience to make your job search easier. To increase your chances of landing an internship, you can:

• Tell people in your professional and personal networks that you’re looking for an internship

• Attend local meetups and AI hackathons

• Keep your professional networking accounts updated

• Take advantage of career resources from your coursework

• Prepare for your technical interview