AI Roadmap: How to go from Beginner to Intermediate

Deepak Tewatia
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Introduction

This article talks about how a beginner can start and reach the level of a pro.

Python Basics

Python is an interpreted, high-level, general-purpose programming language. It was created by Guido van Rossum and first released in 1991. he started Python as a hobby project to keep him occupied in the week around Christmas. Got its name from the name of British comedy troupe Monty Python. It is used in.

Software Development
Web Development
System Scripting
Mathematics

Basic Data Types
- Numerical Types
- Natively Implemented Arithmetic Operations
- Lists
- Sets
- Tuples
- String
- Dictionaries
Working with the Assignment Operator
Operator Overloading
Control Flow
- If
- Elif
- Else
- If-Elif-Else
- For / Range
- While / Break / Continue
Advanced Iteration
Functions
- Function Definition
- Return Statement
- Parameters
Functions
- Creation
- Manipulation
- Built-In Functions
String
- Creation
- Manipulation
- Built-In Functions
List Manipulation
- Creation
- Manipulation
- Built-In Functions
Tuple Manipulation
- Creation
- Manipulation
- Built-In Functions
Dictionary Manipulations
- Creation
- Manipulation
- Built-In Functions

Numpy

Numeric, the ancestor of NumPy, was developed by Jim Hugunin. Another package Numarray was also developed, having some additional functionalities. In 2005, Travis Oliphant created the NumPy package by incorporating Numarray's features into the Numeric package. There are many contributors to this open-source project.

Numpy or Numerical Python is a Python library that provides the following.

a powerful N-dimensional array object
sophisticated (broadcasting) functions
tools for integrating C/C++ and Fortran code
useful linear algebra, Fourier Transform, and random number capabilities.

It can also provide an efficient multi-dimensional container of generic data. Arbitrary data types can be defined. The official website is https://www.numpy.org

Introduction to NumPy
NumPy Arrays
Array Operations
NumPy Indexing and Slicing
NumPy Shape Manipulation
NumPy Functions and Methods

Pandas

Pandas is a software library written in Python for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. The name is derived from the term "panel data", an econometrics term for data sets that include observations over multiple periods for the same individuals

The original author is Wes McKinney. Pandas was first released on 11 January 2008. The official website is https://www.pandas.pydata.org

Introduction to Pandas
Series and DataFrames
Data Indexing and Selection
Data Cleaning and Preprocessing
Data Analysis and Manipulation
Data Visualization with Pandas

Sckit-Learn

Scikit-learn (formerly scikits.learn) is a free software machine-learning library for Python programming.

It features various classification, regression, and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

The scikit-learn project started as scikits.learn a Google Summer of Code project by David Cournapeau. Its name stems from the notion that it is a "SciKit" (SciPy Toolkit), a separately-developed and distributed third-party extension to SciPy.

It was first released in June 2012. The official website is https://www.scikit-learn.org.

What is Scikit-Learn?
Installing Scikit-Learn
Classification
Loading Datasets
Breaking datasets into training, test and validation set
Learning and Predicting
Performance Analysis

Matplotlib

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural "pylab" interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of Matplotlib.

Matplotlib was originally written by John D. Hunter, has an active development community, and is distributed under a BSD-style license. It was first released in 2003. The official website is https://www.matplotlib.org.

What is MatplotLib?
What is MatplotLib.PyPlot and MatplotLib Inline?
Installing MatplotLib
Anatomy of a figure
Backend in Matplotlib
Matplotlib.pyplot functions
2D plots
3D plots

Scipy

SciPy is a powerful Python library used for scientific and technical computing, offering a range of modules for optimization, integration, interpolation, linear algebra, statistics, and more. It builds on NumPy and provides advanced functionality for handling multidimensional arrays and performing complex mathematical operations. With its user-friendly interface and extensive capabilities, SciPy is widely utilized in data science, machine learning, and scientific research to solve real-world problems efficiently.

The SciPy library was created by Travis Oliphant in 2001, and its official website is https://scipy.org.

Introduction to SciPy
Integration and ODEs
Linear Algebra
Optimization
Interpolation

Model Training

Model training is the process of teaching a machine learning model to recognize patterns and make decisions based on data. During this process, a dataset is used to adjust the model's parameters, improving its accuracy and generalization capabilities. This involves iterative optimization techniques such as gradient descent to minimize errors and ensure the model performs well on unseen data. Model training is a cornerstone of artificial intelligence, enabling applications in areas like natural language processing, computer vision, and predictive analytics. The concept of training models can be traced back to the invention of the perceptron by Frank Rosenblatt in 1958, which laid the foundation for modern neural networks.

Selection
Training
Overtraining/Undertraining
Learning Rate
Confusion Matrix
Model evaluation
Model Bias

PyTorch

PyTorch is an open-source deep learning framework that provides a flexible and dynamic platform for building machine learning and deep learning models. Known for its ease of use and efficient execution, PyTorch supports dynamic computational graphs, allowing users to modify the graph during runtime, which is particularly useful for research and experimentation. It offers robust support for GPU acceleration, making it ideal for large-scale data processing and training complex neural networks. PyTorch is widely adopted in both academia and industry for tasks like natural language processing, computer vision, and reinforcement learning.

It was developed by Facebook's AI Research (FAIR) team in 2016. The official website for PyTorch is pytorch.org.

Getting Started with PyTorch.
- Installing PyTorch
- Basic Operations with Tensors
- Building Your First Neural Network
Creating a Simple Neural Network.
- Training a Model
- Image Classification with PyTorch
Building an Image Classifier: Training and Using the Model

HuggingFace & Gradio

Hugging Face is an AI research company known for its focus on natural language processing (NLP) and its open-source platform that provides a vast collection of pre-trained machine learning models. It has become a go-to hub for NLP practitioners and researchers. Hugging Face's platform enables easy access to models and datasets, making it a powerful tool for developers working with language-based AI applications. Gradio, created by the same team, is a Python library that allows users to quickly create interactive UIs for machine learning models. Gradio simplifies the process of building demos for AI models, helping users showcase their work without needing to develop complex front-end systems.

Hugging Face was founded by Clément Delangue, Julien Chaumond, and Thomas Wolf in 2016, and its official website is https://huggingface.co