Hello Techies! Hope You all are doing Amazing! we're here with the thought of Creating a complete AI Guide/preparation content for the students or the freshers who are willing to learn and wanted to get into the AI industry. Through this AI series, we'll periodically provide you the conceptual articles and new trends in the AI industry. Are you Ready?? AI is growing Faster attach your seatbelts and be ready to Flyyy....🤩to never miss any article please make sure to follow us:)

Let's jump onto it!

Agenda:

1. All About AI World: what is AI and recent inventions by the IT Giant's.

2. Technologies reside inside AI: Machine learning, data science, computer vision, NLP, Deep learning, Reinforcement Learning, and recent trends.

3. Various Job roles and their Skill requirement: Data Engineering, Data Annotation, Data Analyst, Data Scientist, AI/ML engineers, MLOPS Engineer etc.

4. Map-Road: will discuss the content I'm going to cover in my future articles.

Machine Intelligence is the Last invention that humanity will ever need to make.

- Nick Bostrom

In simple words AI is the technology by which God Made humans 😮 Not really;) but by which a human can make another non-living human i.e. By which we can convert a non-living being into a living. Nowadays Most businesses are AI driven i.e. they use Data Science(business aspect of AI) to make critical decisions about customers, products etc. Every Industry is adapting AI/ machine intelligence to make their work faster and more efficient. We can enhance our photoes or filter our photoes using apps like Snapchat, and Instagram. sometimes you get friend's suggestions on Instagram, Facebook, and Linkedin. You would definitely come across the fact of receiving notifications on Flipkart, Amazon, and Meesho about similar purchases you made before, and another fact of getting similar content on Youtube. Interesting right!! All of these things are made possible using AI.

Google Brain is Working extensively on building an alternative to ChatGPT with enhancing their AI assistants and adopting newer transformations in AI.

Microsoft Satya Nadela recently handed in with OpenAI to build the invention like OpenAI's ChatGPT is doing.

Elon Musk after his self-driving car innovation, building Neuralink Musk's neural-interface-technology company. It's developing a device that would be embedded in a person's brain, where it would record brain activity and potentially stimulate it.

Every day Technology is changing and new buzzwords are coming. but it's never too late to start your Journey. technologies that come under the name AI are Machine Learning, Data Science, Deep Learning, Computer Vision, Natural Language Processing, and Reinforcement Learning. hence, by learning a single technique itself you can kickstart your AI journey.

Let's see each Buzzword's one-by-one:

Machine Learning📈:

Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions and to uncover key insights in data mining projects. These insights subsequently drive decision-making within applications and businesses, ideally impacting key growth metrics. ML algorithms depend upon the data. so it's basically a crucial part of data science and plays a very important role to reach the conclusion and helps businesses to grow their customers by predictions from historical data. They will be required to help identify the most relevant business questions and the data to answer them.

ML is all about Fine-tuning, MATHS, and experimentation.

ML algorithms: Linear Regression, OLD, Logistic Regression/Binary Classification, Polynomial Regression, KNN regressor/classifier, Support vector Regressor/classifier, Decision tree regressor/classifier, ensemble techniques - Random Forest, Gradient & XgBoost, K-means clustering, Agglomerative & DBSCAN and many more...

Data Science📊:

Data Science is the Sexiest Job of the 21st Century.

Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data. This analysis helps data scientists to ask and answer questions like what happened, why it happened, what will happen, and what can be done with the results.

Data science is important because it combines tools, methods, and technology to generate meaning from data. Modern organizations are inundated with data; there is a proliferation of devices that can automatically collect and store information. Online systems and payment portals capture more data in the fields of e-commerce, medicine, finance, and every other aspect of human life. We have text, audio, video, and image data available in vast quantities.

It helps industries by discovering unknown transformative patterns using Descriptive, Diagnostic, Predictive, and Prescriptive Analysis, Innovating new products and solutions, Real-time optimization, etc.

Deep Learning🧠:

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.

Computer Vision💻👁:

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.

Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving and whether there is something wrong in an image.

Computer vision trains machines to perform these functions, but it has to do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.

NLP💻🗣:

the branch of artificial intelligence or AI—concerned with giving computers the ability to understand the text and spoken words in much the same way human beings can.

NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.

NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.

Reinforcement Learning🤖:

Reinforcement Learning(RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences.

Though both supervised and reinforcement learning use mapping between input and output, unlike supervised learning where the feedback provided to the agent is the correct set of actions for performing a task, reinforcement learning uses rewards and punishments as signals for positive and negative behavior.

As compared to unsupervised learning, reinforcement learning is different in terms of goals. While the goal in unsupervised learning is to find similarities and differences between data points, in the case of reinforcement learning the goal is to find a suitable action model that would maximize the total cumulative reward of the agent.

That's all about the Technologies in AI.

So, Which technology you liked?? and you wanted to start your journey with??

🤔Still Confused, just wait⏯ further I'll cover the Job roles along with the skills that the industry looks for and if you aspire to get that job then you can grab these skillsets and break the ice in the industry.

Job Roles In AI Industry🤩

Data Engineer:

A data engineer is responsible for collecting, managing, and converting raw data into information that can be interpreted by data scientists and business analysts. Data accessibility is their ultimate goal, which is to enable organizations to utilize data for performance evaluation and optimization.

Responsibility: Work on Data Architecture**,** Collect Data, and Conduct Research.

Skills required: Multi-Cloud computing, Visualization, NoSQL, Data Pipelines, Hyper Automation, Programming, DevOps, SQL_, Scripting, Hadoop, Apache Spark, Databases Skills, Some Tableau & ML algo knowledge.

Data Analyst:

A data analyst gathers, cleans, and studies data sets to help solve problems. A data analyst collects, cleans, and interprets data sets in order to answer a question or solve a problem. They work in many industries, including business, finance, criminal justice, science, medicine, and government.

What kind of customers should a business target in its next ad campaign? What age group is most vulnerable to a particular disease? What patterns in behavior are connected to financial fraud? they help to solve such business questions.

Responsibilities: Identify the data you want to analyze, Collect the data, Clean the data in preparation for analysis, Analyze the data, Interpret the results of the analysis.

Skills required: Business Acumen, Data Analysis, A/B testing, Data visualization, Tableau, PowerBi, Excel, SQL, Databases etc.

Data Scientist:

A data scientist uses data to understand and explain the phenomena around them, and help organizations make better decisions.Working as a data scientist can be intellectually challenging, analytically satisfying, and put you at the forefront of new advances in technology. Data scientists have become more common and in demand, as big data continues to be increasingly important to the way organizations make decisions.

Responsibilities: Find patterns and trends in datasets to uncover insights, Create algorithms and data models to forecast outcomes, Use machine learning techniques to improve the quality of data or product offerings, Communicate recommendations to other teams and senior staff, Deploy data tools such as Python, R, SAS, or SQL in data analysis, Stay on top of innovations in the data science field.

Skills required: Statistical analysis and computing, Machine Learning, Deep Learning, Processing large data sets, Data Visualization(Tableau, Powerbi), Data Wrangling, Mathematics, Software Engineering skills, Strong Programming, Statistics, and Big Data(Hadoop & spark).

AI/ML Engineer:

An AI engineer builds AI models using machine learning algorithms and deep learning neural networks to draw business insights, which can be used to make business decisions that affect the entire organization. These engineers also create weak or strong AIs, depending on what goals they want to achieve.

Responsibilities: Building AI models with the help of machine learning algorithms to acquire valuable business insights, Performing statistical analysis and using interpretation techniques to streamline organizational processes, Developing, programming, and training networks to create AI models that solve complex tasks, Creating and maintaining AI infrastructure.

Skills required: Strong Programming languages like Python, Java, Scala, and TypeScript, Data Science, Statistics and probability, Data Engineering, Exploratory data analysis, Computer Vision, and Natural Language Processing.

Decision Scientists:

Decision scientists require a combination of certain indispensable skills, which they use in collaboration to extract value from data and solve problems. These skills include the ability to use advanced knowledge of mathematics to understand patterns and trends. Then, statistical science helps to perform technical analysis of data based on patterns and trends.

Machine learning helps to assess possibilities galore and make predictions without human intervention. Further, decision scientists require business acumen, so they can perform an astute analysis of critical challenges and make important decisions to tackle them.

Responsibilities: They play a very important role in using the data extracted from small pockets that exist in silos and assembling the chunks together, using the knowledge of business dynamics coupled with intuition and far-sightedness to create the big picture. In short, decision scientists are artists who combine the various sciences of math, technology, and business for doing their job

Skills required: Business Acumen, ML, Data Science, Business Analytical skills, Data Analysis etc.

MLOPS Engineer:

MLOps Engineers are the people who build, maintain, and optimize machine learning solutions. They are the ones who ensure that your algorithms are performing as expected.

They are also responsible for building new models and improving existing ones.They have a wide range of skills, including knowledge of data science, software engineering, and domain expertise in the industry in which they work. They also need to be able to understand business problems and come up with solutions to them using machine learning techniques.

Responsibilities: develop and maintain a platform that automates creating, training, deploying, and updating machine learning models.

Skills required: Apache Spark, Scala, and Python and will have experience building large-scale data pipelines using Apache Kafka, ML, Data Science, Deep Learning, and Automation.

Research Scientists:

A Research Scientist is someone who designs and creates artificial intelligence. They work in computer science and often specialize in machine learning or computer vision. The work of an AI scientist requires them to be well-versed in math, statistics, and programming. Mostly companies like Amazon, and Google have the roles of Research scientists.

Responsibilities: develop new techniques to leverage the field and responsible to evolve the AI field.

Skills required: Writing for research papers and grants, develop new algorithms, Innovation in ML, Deep Learning, Computer vision, NLP, RL, Experience with data science techniques such as feature engineering, model selection, model validation, and hyperparameter tuning, building large-scale distributed systems for training models (e.g., distributed TensorFlow), Python/Java/Scala, Experience in analyzing data from multiple sources (e.g., web logs, click streams, survey responses)

You would be more clear right now about which to go with right!!

Hope you get that🤞. Now how to get started?? plenty of resources are available on the Internet so which to choose and how to start with🤔.

Don't worry, We are here to help you. In this series, the topics/concepts we're going to cover are as follows.

Agenda

Topics I'll cover in this AI series are:

• Real-world Use Cases of the companies like Amazon, Google, Youtube, Netflix, Walmart, Uber, Pinterest, and others. From this, You'll get to know how actual problems can be solved using mentioned technologies that I discussed. It's better to know why we're doing, before actual doing right.

• Complete guide on Data Science Lifecycle i.e. I'll cover Statistics, Probability, Linear Algebra, Data Wrangling, ML/DL algorithms, NLP with some practical guide. also some breakthrough's in Responsible/Explainable AI, AutoML, diffusion models, and much more...

• Recent Advancements/Innovations in AI.

If you wan't any relevant content, feel free to comment. we're happy to receive your suggestions and always open for new Content ideas.

Last but not the list, Spend Enough time learning the concepts and go in-depth unless you reach the nucleus of the concept. because

A year spent in artificial intelligence is enough to make one believe in God.

-Alan Perlis

Hurray!! Ending but Real-beginning:) Love and share this Article, you can connect with me on Linkedin and github. Hope to see you in our next Article:)

Stay Tuned and Happy Reading!🥳