From Zero to Machine Learning Pro: A Webinar Wrap-Up

The field of Artificial Intelligence (AI) is rapidly expanding, with new innovations and advancements making headlines on a regular basis.

Despite this, a career in AI has been a solid choice for some time now, as the technology is increasingly being adopted across industries and creating a need for skilled professionals to fill the resulting job opportunities.

However, it’s also important to note that AI is projected to displace over 1.7 million jobs, but it is also expected to create about half a million new jobs worldwide.

With AI being utilized in various industries such as entertainment, transportation, and more, the demand for qualified and skilled professionals in the field is high.

Further About Machine Learning

Machine learning is a subset of artificial intelligence that allows computers to automatically learn and improve from experience without being explicitly programmed.

It involves the use of algorithms and statistical models to analyze and understand data, and make predictions or decisions.

Machine learning can be used for a variety of tasks, such as image and speech recognition, natural language processing, and predictive analytics.

There are different types of machine learning, including supervised, unsupervised, and reinforcement learning.

Supervised learning involves training a model on a labeled dataset, while unsupervised learning involves training a model on an unlabeled dataset. Reinforcement learning involves training a model through trial and error.

The popularity and applications of machine learning continue to grow as more data becomes available and processing power increases.

AI and Machine Learning

Artificial Intelligence (AI) refers to the ability of machines to mimic human intelligence and perform tasks that typically require human-like thinking, such as decision-making, problem-solving, and learning.

Machine learning, which is a subset of AI, is the process of teaching machines to learn from data and improve their performance over time without being explicitly programmed.

Through machine learning, computers can be trained to recognize patterns, make predictions, and perform tasks that would typically require human intervention.

This technology allows machines to improve their performance and capabilities, leading to numerous benefits for both individuals and organizations.

Consumers can use AI in various forms such as navigation and personal assistant apps, while businesses can use AI to improve their operations and decision-making processes.

Three Main Stages of AI

AI is constantly evolving, making a career in this field an exciting and dynamic opportunity. According to Van Loon, there are three stages of AI and machine learning development:

  1. Machine learning – This refers to intelligent systems using algorithms to learn from experience.
  2. Machine intelligence – This is the current state of AI technology, where machines learn from experience based on pre-programmed algorithms. It is a more advanced form of machine learning, with enhanced cognitive abilities.
  3. Machine consciousness – This is the stage where systems can self-learn from experience without any external input. An example of this is Siri, which can understand and respond to user commands without the need for data inputs.

Career As AI Engineer

Machine Learning Engineer works in a Software Application and must understand how to create an algorithm with Artificial Intelligence technology.

Basically, Machine Learning is a method designed to teach AI technology to be able to study data so that, in the future, it no longer relies on explicit programming.

Machine Learning Engineer is a relatively new profession. To become a Machine Learning Engineer, you must learn and master various programming languages to quite complex statistics.

Becoming a Machine Learning Engineer requires experience, experience and other soft skills. This position is very open for those currently working in the Technology & IT field due to the broad future prospects and quite promising career paths.

Not a few workers from other technology fields are currently starting to switch careers to become Machine Learning Engineers.

One of the career paths that you can go through before becoming a Machine Learning Engineer is to become a Data Scientist or those who work in Data Science because there is a significant correlation with Machine Learning Engineers.

Duties and Responsibilities

The first thing you should know about this profession is what tasks are carried out in their daily work. Here are the functions of a Machine Learning Engineer:

1. Design a Machine Learning Program

A Machine Learning Engineer’s main task is to create system algorithms. A Machine Learning Engineer uses machine learning platforms daily, such as IBM, Microsoft, Google, Amazon, and others. In designing programs, you must understand data structures and architectures in theory and practice.

One example of the work of a Machine Learning Engineer that we can find every day is in advertisements displayed on Instagram, Facebook, or other media.

Ads that appear result from algorithms that show topics or things you like. This profession requires you to be able to create programs that can control a robot or system.

2. Perform Statistical Analysis

A Machine Learning Engineer must be able to read and analyze statistical data.

Because the basis of this profession is the field of Data Science, you need to understand the basics of this field. Machine Learning is a mathematical relationship between input and output data combinations.

You need to provide a good data set so that Machine Learning can learn from the data and build on it.

3. Train the Machine Learning System

The next task for a Machine Learning Engineer is to train the system created in Machine Learning. This, of course, takes time and has to go through a lot of trial & error. This process determines whether the program created has the desired output.

From the data set provided to Machine Learning, you need to do testing by giving it an input/output combination.

Then Machine Learning will compute the relationship between the input and output, and Machine Learning will automatically predict or determine the work.

As long as the computer system has sufficient data and computing power to process the data, Machine Learning can relate complex data mathematically to computer systems.

4. Understand the concept of Big Data

The next thing that becomes the task of the Machine Learning Engineer is that they must be familiar with and understand Big Data. An important job in this work is transforming these data into a model for a desired output.

Not only that, this profession is required to be able to analyze big data in a complex manner to extract insights and be able to determine which techniques are appropriate.

This profession requires that you understand how to use data modelling and evaluation strategies to find patterns and predict certain things.

Another requirement in this profession is having to master mathematics in terms of calculations and determining which algorithms are appropriate and suitable for research.

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