AI, Machine learning, Python these are the three keyword which buzz when we start talking about Artificial Intelligence.

Artificial Intelligence is a broad topic, which cannot be explained with one definition and its use case is in wide variety of fields and the backbone for the AI is the data, more the data more accurate would be the outcome. The Data science subject is a subset of AI, as all the data related stuff is solved by AI. For all the data related problems we have well known subject to study that is Statistics. If we still go one step further, we need to study Machine Learning (ML), Deep learning (DL) and Re-enforcement learning (RL) and to Natural Language Processing NLP. If all the above is studied with respect to one domain or engineering field, then it becomes Field of study.

In summary to learn AI we need to know domain data, Mathematics, any programming language.

• Artificial Intelligence (AI)

Artificial Intelligence, or AI, is like creating a brain for computers. It’s all about making machines smart, so they can do tasks that usually need human intelligence. Imagine having a robot friend that can talk, understand, learn, and even think a little bit!. Tesla cars which drive without driver and chat GPT to name few.

• Machine Learning

Machine Learning is a special part of AI. It’s like teaching a computer to learn by itself without being programmed for every single thing. Just like you learn from experience, a machine can learn from data. For example, you can show pictures of dogs and cats to a computer, and it can learn to tell them apart!

• Programming Language-Python

Python is a cool and friendly computer language. It’s like the language computers understand. With Python, we can talk to computers and tell them what to do step by step. It’s like giving instructions to a robot buddy. Python is one of the best ways to teach machines to learn, and that’s why it’s so popular for AI and machine learning!

 

The Subsets of AI

Python is like the magic wand that makes AI and machine learning happen. With Python, we can creat

e programs that teach machines to learn and become smart. We use Python to write code that helps machines understand data, learn from it, and make decisions. It’s like being a teacher for the computer!

So, when you hear about AI and machine learning, just remember that Python is the special language that makes it all work. It’s like having a fantastic adventure with your robot friend and showing them the wonders of the world through data and learning. With Python, we can create amazing things and make the future a more intelligent and exciting place.

I have just jot down the major contents for beginners to start with and then explore still more further study. Learning Python for machine learning is a great choice! Python is a popular and powerful programming language widely used in the machine learning community. Below is a brief explanation with a table of contents to help you get started on your Python journey towards machine learning:

Table of Contents:

1. Python Basics

– Variables and Data Types

– Operators and Expressions

– Control Flow (if-else, loops)

– Functions and Modules

2. Data Structures in Python

– Lists, Tuples, and Sets

– Dictionaries

– List Comprehensions

3. File Handling

– Reading and Writing files

– CSV and JSON file handling

4. NumPy

– Introduction to NumPy

– NumPy arrays and operations

– Broadcasting and Indexing

5. Pandas

– Introduction to Pandas

– DataFrames and Series

– Data manipulation and filtering

6. Data Visualization

– Matplotlib for basic plotting

– Seaborn for statistical data visualization

7. Machine Learning Basics

– Introduction to machine learning concepts

– Supervised, Unsupervised, and Reinforcement learning

8. Scikit-learn

– Introduction to Scikit-learn library

– Data preprocessing and feature scaling

– Building and evaluating machine learning models

9. Linear Regression

– Understanding linear regression

– Implementing simple linear regression in Python

10. Logistic Regression

– Understanding logistic regression

– Implementing logistic regression in Python

11. Decision Trees and Random Forests

– Understanding decision trees and random forests

– Implementing these algorithms in Python

12. Clustering

– K-Means clustering algorithm

– Hierarchical clustering

13. Introduction to Neural Networks

– Perceptrons and activation functions

– Building a simple neural network in Python

14. Deep Learning with TensorFlow or PyTorch

– Introduction to TensorFlow/PyTorch

– Building and training deep learning models

15. Introduction to Natural Language Processing (NLP)

– Text preprocessing

– Building NLP models using libraries like NLTK or spaCy

16. Introduction to Computer Vision

– Image preprocessing

– Building computer vision models using libraries like OpenCV

18. Putting it All Together

– Working on a complete machine learning project

– Understanding model evaluation and hyperparameter tuning

Remember to practice regularly and work on real-world projects to reinforce your learning. Python offers an excellent foundation for machine learning, and as you progress, you can explore more complex algorithms and specialized libraries. Good luck on your Python and machine learning journey!