How can we learn Artificial Intelligence (AI)

From the primitive era of mankind to the most advanced homo-sapiens in terms of technology, there has been a remarkable progression. The incredible shift from acquiring fire from specific rocks to accessing knowledge instantly is always captivating. Until the creation of computers, all tasks were carried out by humans. As a result of technological advancements, everything in the world has changed. The introduction of computers and automated agents began to replace human efforts in certain activities. However, we now live in a world where more than seventy-five (75%) of human tasks are performed by artificially created machines or technical agents. These machines have diminished human labor and improved efficiency by reducing the time required to complete a task. This is how the creation of these machines began. Nevertheless, due to the significant increase in technology, we are now working on machines that have the potential to replace humans in task completion completely. This significant technological advancement has paved the way for the field known as "Artificial Intelligence."

Artificial Intelligence (AI) has emerged as a game-changing power, completely transforming different sectors and opening up avenues for groundbreaking advancements. As of now, when the present scenario of artificial intelligence is taken into consideration, it is still in the stage of initial position with respect to its classification. Basically, Artificial Intelligence is the study and analysis of working of the computational agents and designing these agents that can work without the help of humans. Artificial Intelligence is classified into 3 parts based upon its abilities. They are:

• Weak Artificial Intelligence or Weak AI (Narrow Artificial Intelligence or Narrow AI)
• General Artificial Intelligence or General AI
• Super Artificial Intelligence or Super AI

Presently, the artificial intelligence is in the weak artificial intelligence stage that is it can perform only one specific task and cannot move beyond its boundaries.

The need for proficient AI experts is constantly increasing, presenting an ideal moment to begin a quest to acquire and become proficient in AI. This extensive article offers a detailed examination of eight crucial stages to gain expertise and thrive in the realm of Artificial Intelligence.

Various ways to learn artificial intelligence

The points mentioned below are the eight (8) most powerful points to be followed in order to learn and excel in artificial intelligence.

i. Grasp the Essential Knowledge

ii. Excel in Artificial Intelligence Concepts

iii. Master Data Management

iv. Engage in AI Projects

v. Acquire Proficiency in AI Tools

vi. Enroll in AI Courses

vii. Apply for an Internship

viii. Secure Employment

Let us now go deeper into each point and have a detailed view of how to achieve each weapon in order to excel in the stream of artificial intelligence.

(i) Grasp the Essential Knowledge:

Grasping the Essential Knowledge is the initial step in establishing a sturdy base in artificial intelligence (AI). Obtaining the most vital fundamental basement of basics of mathematics and computer science is crucial to understanding the in-depth principles of AI algorithms and models. Mathematics serves as the vocabulary of AI, providing the necessary tools and ideas for developing and examining AI algorithms.

Linear algebra serves as the cornerstone for many AI approaches, allowing for data manipulation and modification via matrices and vectors. Grasp methods such as principle component analysis (PCA) or singular value decomposition (SVD) require a solid grasp of topics such as matrix operations, eigenvectors, and eigenvalues.

Probability theory is another fundamental mathematical concept in AI. It allows us to model uncertainty and make informed decisions based on probabilities. Probability distributions, conditional probabilities, and statistical inference are essential in understanding machine learning algorithms like Naive Bayes or Gaussian processes.

Calculus plays a crucial role in optimization, which is a critical element of many AI algorithms. Concepts such as derivatives, gradients, and optimization techniques like gradient descent are fundamental in training machine learning models.

Besides mathematics, a solid understanding of computer science fundamentals is necessary for AI. Algorithms and data structures provide the framework for efficiently organizing and processing data. Understanding concepts like sorting algorithms, search algorithms, linked lists, trees, and graphs enables effective problem-solving and algorithm design in AI applications. Python is extensively utilized in the AI world because of its simplicity, adaptability, and rich ecosystem of AI tools and frameworks. Python is a programming language with exceptionally in-built valuable libraries. Libraries such as NumPy, pandas, and scikit-learn can be extensively used to implement AI algorithms, process data, and develop AI models. Hence, having knowledge about these libraries is very important.

(ii) Excel in Artificial Intelligence Concepts:

With a strong knowledge base, delve into the fundamental concepts of artificial intelligence. Machine Learning is base of artificial intelligence. Basically, it is the process of empowering a machine in order to perform specific tasks with the least human intervention required. These intelligent machines acquire huge amounts of data, analyze the data and draw conclusions from them. They are capable of making their own decisions. Hence, machine learning is considered the most significant and impressive technology that highly contributes to increasing the efficiency of the task. And hence, the need to learn the three most important types of machine learning is so critical and they are supervised learning, unsupervised learning, and reinforcement learning. Grasp and acquire knowledge on how to generate, train, and evaluate machine learning models to address practical problems.

After the exploration of machine learning strategies, enter into the most exciting world of deep learning as well. The concept of deep learning is so fascinating. Basically, deep learning is a sub-branch of machine learning which uses the concept of artificial neural networks that are designed by observing the human neural network system. The techniques in deep learning work upon massive complex data to analyze and extract complex patterns from it. We have methods like CNN, i.e., Convolutional neural network, used for image analysis. And RNN is a recurrent neural network used for sequential data analysis. Hence, knowing these concepts is essential. Gain practical expertise by developing and training deep learning models with tools such as TensorFlow or PyTorch.

(iii) Master Data Management:

Data is the energy that drives AI systems. Becoming proficient in data management is crucial to guarantee the accessibility, excellence, and reliability of information utilized in AI applications. Familiarize yourself with data pretreatment methods, such as data scrubbing, conversion, and attribute extraction. Acquire proficiency in managing absent data, anomalies, and discrepancies in datasets.

Develop abilities in data examination and representation to gain insights and recognize patterns within the information. Obtain an understanding of databases and dispersed file systems to proficiently stockpile, handle, and retrieve large-scale datasets. Comprehend data confidentiality and moral considerations to guarantee responsible and impartial use of information in AI applications.

(iv) Engage in AI Projects:

To strengthen your education and acquire hands-on practice, involve yourself in AI undertakings. Begin with minor projects and gradually take on more intricate difficulties. Websites such as Kaggle offer an extensive assortment of data sets and contests to engage in, granting you the opportunity to put your abilities into action and gain knowledge from the AI society. Collaborate with other passionate individuals, participate in AI hackathons, and contribute to open-source AI projects to improve your aptitude in problem-solving.

(v) Acquire Proficiency in AI Tools:

Now that we have acquired a basic idea of artificial intelligence and its basic fundamentals and now the turn comes to learn about AI tools and frameworks. It helps developers to build advanced AI applications efficiently. Gaining complete knowledge about TensorFlow, PyTorch, and scikit-learn is very crucial to move forward in artificial intelligence. These libraries provide in-built algorithms, neural network architectures, and utilities to streamline AI development. Data visualization plays a crucial role in artificial intelligence, which is possible with the help of Matplotlib and Tableau. It helps in effectively communicating with insights derived from AI models. There are multiple sources available online to acquire knowledge about the most useful cloud-based platforms that help in building AI applications. Some of the best cloud-based AI platforms are Amazon Web Services shortly, AWS, and Google Cloud Platform. These platforms offer AI services, such as pre-trained models, scalable infrastructure, and data management tools, enabling you to experience AI's capabilities in a scalable and cost-effective manner.

(vi)Enroll in AI Courses

After all, formal education is much more needed to get a complete picture of artificial intelligence, and it plays a crucial role in obtaining extensive knowledge of AI. Register yourself in AI classes provided by reputable organizations or via online platforms such as Coursera, edX, or Udacity. These training courses provide well-organized learning paths, detailed materials, and practical hands-on projects to strengthen your learning. Try to explore on most advanced topics in artificial intelligence, such as advanced machine learning algorithms, deep learning neural network concepts, natural language processing, computer vision, and reinforcement learning. Attend the classes regularly, maintain consistency, complete assignments on time, be attentive and engage in online debates, and cooperate with other learners to enrich your learning journey. Moreover, pursue specialized certifications to demonstrate your proficiency in specific AI fields.

(vii) Apply for an Apprenticeship:

Here comes the most crucial step in the learning path "Internships." Applying for an AI internship is the most crucial step in the journey to mastering artificial intelligence. You will have an opportunity to keep all your learnings till now into the practical to resolve real-world problems. These internship programs connect you between academic knowledge and real-world application. This offers a powerful opportunity to gain the most important practical experience with the help and guidance of experienced AI professionals.

One of the essential advantages of an AI internship is the opportunity to work on real-world projects wherein you will get a complete picture to apply your academic intelligence to solve problems in the real world. As of now, we have worked on academic exercises in which we will be working on example datasets. Still, internship projects involve actual data, industry-specific challenges, and high-stakes results. Interns are presented with intricate problem-solving situations, necessitating them to employ their theoretical expertise in real-life circumstances. This practical involvement cultivates a more profound comprehension of how AI is utilized to tackle genuine issues and enhances critical thinking and analytical abilities.

Furthermore, internships provide access to seasoned mentors. Experienced AI professionals guide and support interns throughout their journey, offering valuable insights, feedback, and advice. Mentorship enables interns to navigate challenges effectively, learn best practices, and benefit from the wisdom of those who have already made significant contributions to the field. Such guidance also helps in understanding the intricacies of AI projects and aligning them with industry standards.

Collaboration is another crucial aspect of AI internships. Working with interdisciplinary teams exposes interns to diverse perspectives and promotes teamwork. Collaborating with individuals from different backgrounds, such as data scientists, engineers, and subject matter experts, offers a comprehensive view of AI project development. Team interactions cultivate communication skills, empathy, and adaptability—qualities essential for succeeding in any professional setting.

The internship journey is an ongoing practical educational adventure that is very similar to a real job. We know that artificial intelligence is a constantly evolving field hence staying updated with the latest advancements and trends in the technology is so crucial.

Interns will also have the opportunity to take part in workshops, seminars, and industry events conducted by the company during their internship, which significantly enhances their understanding of cutting-edge AI technologies. Adopting continuous learning not only ensures the relevance of acquired skills but also opens doors for further growth and career development.

Alongside technical skills, an AI internship also focuses on the development of interpersonal skills. Interns learn effective time management, meeting project deadlines, and effectively communicating complex technical concepts to stakeholders with varying levels of technical knowledge. These skills are vital for success in any professional setting and are highly valued by employers. In conclusion, an AI internship is a transformative experience for aspiring AI professionals. An internship is an excellent combination of hands-on experience, mentorship, collaboration, and continuous learning. Interns gain practical skills and insights that cannot be fully obtained through academic study alone with the help of real-world projects and exposure to industry best practices.

(viii) Secure Employment:

Once you have obtained a solid foundation and hands-on experience in artificial intelligence, it's time to commence your professional journey. Enhance your curriculum vitae to emphasize your artificial intelligence projects, pertinent coursework, certifications, and apprenticeship experience. Connect with artificial intelligence experts through platforms like LinkedIn, attend industry conferences, and participate in artificial intelligence competitions to broaden your professional network. Conduct research and apply for job opportunities in the field of artificial intelligence that align with your interests and skill set. Prepare for interviews by reviewing fundamental concepts, solving practice problems, and showcasing your project portfolio. Stay informed about the latest trends and advancements in artificial intelligence to showcase your enthusiasm and dedication to the field during the hiring process.

Conclusion:

Acquiring knowledge and becoming skilled in artificial intelligence is an ever-changing and fulfilling wish. By adhering to the eight fundamental stages depicted in this module, you can establish a solid groundwork in AI principles, obtain hands-on expertise through assignments and mentorships, and attain mastery of AI platforms.