This article “Journey to AI, ML and Data Science” will certainly help in demystifying the world of AI. Whether you’re just beginning your journey or looking to deepen your understanding, this comprehensive article explores the fascinating world of artificial intelligence (AI), machine learning (ML), and data science, shedding light on the unique capabilities of each and how they work together to power the latest advancements in technology.
We’re about to uncover some vital information.
Some interesting points on AI, ML and Data Science
- According to Harvard Business Review, Data Scientist: The Hottest Career of the 21st Century.
- According to LinkedIn, there was a 151,717-person shortfall of data scientists in the United States alone in August 2018.
- The Massachusetts Institute of Technology (MIT) has just unveiled a $1 billion plan to establish a new college for artificial intelligence.
- IISc [Indian Institute of Science, Bengaluru] has introduced a new artificial intelligence MTech degree.
- Blockchain and AI are the foundation of the Fourth Industrial Revolution, according to Forbes.
Tech Giants in AI, ML and Data Science
- Google AI: Division of Google is dedicated solely to artificial intelligence. It was announced at Google I/O 2017 by CEO Sundar Pichai.
- Amazon: Deployed Artificial Intelligence on AWS, a Powerful tool for all developers to add intelligence to your applications.
- Facebook: Racing with Amazon and Google to develop its own artificial intelligence chips, after recognizing that it needs dramatically faster computing to deliver the next breakthrough in AI.
Popular job roles in AI, ML and Data Science
- Business Analyst
- Product Analyst
- Data Analyst
- ML Engineer
- Data Scientist
- AI Scientist
Without a basic introduction to its fundamentals, machine learning can seem overwhelming.
To master machine learning, you don’t have to be an expert programmer or mathematician, but you do need to possess fundamental knowledge in those fields.
Python for AI, ML and Data Science
You should prefer Python programming language for machine learning.
Data science, as you can see, focuses on problem-solving, exploration, and the extraction of useful information from data.
You will need to manage datasets, train machine learning models, display the outcomes, and do a lot more in order to do this successfully.
- Start by learning core programming concepts.
- Next, gain a working knowledge of essential data science libraries.
- Finally, you’ll practice and refine your skills through actual projects.
This approach will allow you to build mastery over time while having more fun.
Python Skill Kit:
- Python Programming
- Python Data structure: Lists, Tuples, Sets, Dictionary, Strings
- Python ML libraries: scikit-learn
- Python Data libraries: Pandas, Numpy, Scipy
- Python Visualization: Matplotlib, Seaborn
- Python Deep Learning: Tensorflow, Keras
- Python NLP: NLTK, gensim, Textblob, etc.
- The language of choice for most machine learning engineers and data scientists.
- Most tools for data have been built in Python or have built API access for easy Python access.
- Python’s syntax is relatively easy to pick up.
“hello, world!” in JAVA:
“hello, world!” in python:
- Python is easy to learn and use. It can perform complex tasks using a few lines of code.
- It is an interpreted language; it means the Python program is executed one line at a time. The advantage of being interpreted language, it makes debugging easy and portable.
- Python can run equally on different platforms such as Windows, Linux, UNIX, and Macintosh, etc.
- Python is freely available to everyone.
- Python supports object-oriented language and concepts of classes and objects come into existence.
- Also, other languages such as C/C++ can be used to compile the code and thus it can be used further in our Python code. It converts the program into byte code, and any platform can use that byte code.
It provides a vast range of libraries for various fields such as machine learning, web development, and also for scripting.
It can be easily integrated with languages like C, C++, JAVA, etc. Python runs code line by line like C, C++ Java. It makes it easy to debug the code.
The code of the other programming language can use in the Python source code. We can use Python source code in another programming language as well. It can embed other languages into our code.
Dynamic Memory Allocation: In Python, we don’t need to specify the data type of the variable. When we assign some value to the variable, it automatically allocates the memory to the variable at run time. Suppose we are assigned an integer value 15 to x, then we don’t need to write int x = 15. Just write x = 15.
Statistics for AI, ML and Data Science
Understanding statistics, especially Bayesian probability, is essential for many machine learning algorithms. Wikipedia defines it as the study of the collection, analysis, interpretation, presentation, and organization of data. Therefore, it shouldn’t be a surprise that data scientists need to know statistics.
Key concepts include probability distributions, statistical significance, hypothesis testing, regression, conditional probability, priors and posteriors, and maximum likelihood. If those terms sound like mumbo jumbo to you, don’t worry. This will all make sense once you roll up your sleeves and start learning. Furthermore, machine learning requires understanding Bayesian thinking.
Bayesian thinking is the process of updating beliefs as additional data is collected, and it’s the engine behind many machine learning models.
Math for AI, ML and Data Science
Original algorithm research requires a foundation in linear algebra and multivariable calculus.
Do you need to have a math Ph.D to become a data scientist?
How much math you’ll do on a daily basis as a data scientist varies a lot depending on your role.
First, every data scientist needs to know some statistics and probability theory.
How much maths?
You won’t have to conduct complex mathematical calculations, especially in entry-level positions, because there are powerful math libraries already included.
There is no need to start from scratch. But, your knowledge of multivariable calculus and basic linear algebra may still be tested in interviews.
R&D-Heavy Machine Learning Positions need much more original ML research and development.
You may need to translate algorithms from academic papers into working code. Or, you might research enhancements based on your business’s unique challenges.
In other words, you’ll be implementing algorithms from scratch much more often. For these positions, mastery of both linear algebra and multivariable calculus is a must. You’ll use linear algebra to represent the network and calculus to optimize it.
Mathematics for AI:
- Linear Algebra
Other necessary tools:
- SQL / Spark / Hive: for extracting data
- AWS / GCP/Azure: for deploying data solutions
- Learn to use collaborative tools such as Github.
Also, get into the habit of writing your code using jupyter notebook.
- Jupyter Notebook is a very popular and flexible tool that lets us put our code, the output of the code, and any kind of visualization or plot, etc. in the same document.
- We use the Jupyter Notebook since it comes pre-installed with most of the important data science libraries you’ll use.
- It comes with an easy, clean interactive interface that allows you to edit your code on the fly.
Frequently Asked Questions
How do I start AI, ML and data science?
You can start by comprehending the fundamentals of statistics, programming, and data analysis to get started with AI, ML, and data science. To assist you in learning these abilities, there are numerous online tutorials, courses, and resources available.
Can we learn AI, ML and data science?
Certainly, with the correct instruction and resources, you can learn AI, ML, and data science. There are numerous training options available, including online courses, boot camps, and conventional degree programs, but these fields necessitate a blend of technical expertise, creativity, and communication skills.
Can I go into AI with data science?
Certainly, someone with a data science background can get into AI. There are many transferable skills and concepts between the two disciplines, and both share many fundamental abilities including data analysis, statistics, and programming.
What should I learn first: AI or ML or data science?
It is advised to start with data science since it offers a solid basis in statistical and analytical abilities, which are crucial in both AI and machine learning. You can learn AI and ML after obtaining a firm understanding of data science.
Can I learn AI without coding?
Absolutely, it is possible to study AI without knowing how to code because there are many tools and platforms that let you create AI models by dragging and dropping components into place or using pre-made templates. Yet, more sophisticated AI applications and customization may benefit from having a foundation in programming.
What is the salary of a data scientist vs an AI professional?
Data scientists and AI experts may earn different salaries depending on their experience, region, and industry. Due to the fact that AI is thought of as a more specialized and advanced discipline, specialists in the field may generally earn a little bit more money than data scientists.
Can a fresher learn AI and ML?
Certainly, given the correct training and resources, a fresher can learn AI and ML. There are many educational options accessible for those with no prior experience, including online courses, boot camps, and conventional degree programs.
Which branch is better: AI or data science?
Data science and artificial intelligence are both expanding professions with lots of room for professional advancement. While both disciplines have promising and profitable career prospects, the decision ultimately comes down to your hobbies and professional objectives.
Does data science with AI require coding?
Coding is, in fact, a crucial part of data science with AI and ML. Building, training, and deploying AI models requires a solid understanding of coding languages like Python and R.
Which is better: AI or cyber security?
Both artificial intelligence and cybersecurity provide a wide range of professional options. Your hobbies and career objectives will ultimately determine which option you choose.
Which is better in CSE: data science or AI ML?
Your hobbies and professional objectives will ultimately determine whether you choose data science or AI/ML. These industries provide a wide range of rewarding job options.
Which is harder: data science or ML?
Both data science and machine learning can be difficult to study because they call for a solid background in math, statistics, and programming. Nonetheless, these talents may be learned and mastered with the proper instruction and tools.
Are AI and ML hard to study?
Due to the fact that they call for a solid background in arithmetic, statistics, and programming, AI and ML can be difficult to study. Nonetheless, these skills may be learned and mastered with the proper instruction and tools.
In conclusion, AI, ML, and data science are rapidly evolving fields with numerous real-world applications and tremendous potential for future development. Making decisions on how to use these technologies for one’s own purposes may be made more intelligently by people and organizations when they are aware of the differences between these domains and their various uses. There are various educational options and training programs available to assist people to develop the technical knowledge, creativity, and communication skills necessary to become a professional in AI, ML, or data science. There will surely be new difficulties and opportunities for professionals and society at large as AI, ML, and data science continue to advance. Individuals and organizations may maintain their competitiveness and maximize the revolutionary potential of AI, ML, and data science by keeping up with the most recent advancements and trends in these domains.
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