Ultimate-Data-Science-Toolkit---From-Python-Basics-to-GenerativeAI
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Ultimate Data Science Toolkit is a comprehensive repository covering Python basics to Generative AI. It includes modules on Python programming, data analysis, statistics, machine learning, MLOps, case studies, and deep learning. The repository provides detailed tutorials on various topics such as Python data structures, control statements, functions, modules, object-oriented programming, exception handling, file handling, web API, databases, list comprehension, lambda functions, Pandas, Numpy, data visualization, statistical analysis, supervised and unsupervised machine learning algorithms, model serialization, ML pipeline orchestration, case studies, and deep learning concepts like neural networks and autoencoders.
README:
| Topic Name | What's Covered |
|---|---|
| Intro to Python | Applications and Features of Python, Hello World Program, Identifiers and Rules to define identifiers, Data Types (numeric, boolean, strings, list, tuple, set and dict), Comments, Input and Output, Operators - Arithmatic, Reltaional, Equality, Logical, Bitwise, Assignment, Ternary, Identity and Membership |
| Data Structures in Python (Strings, List, Tuple, Set, Dictionary) | Strings - Creating a string, Indexing, Slicing, Split, Join, etc, List - Initialization, Indexing, Slicing, Sorting, Appending, etc, Tuple - Initialization, Indexing, Slicing, Count, Index, etc, Set - Initialization, Unordered Sequence, Set Opertaions, etc, Dictionary - Initialization, Updating, Keys, Values, Items, etc |
| Control Statements (Conditionals and Loops) | Conditional Statements - Introducing Indentation, if statement, if...else statement, if..elif...else statement, Nested if else statement, Loops - while loops, while...else loop, Membership operator, for loop, for...else loop, Nested Loops, Break and Continue Statement, Why else? |
| Functions and Modules | Functions - Introduction to Python Functions, Function Definition and Calling, Functions with Arguments/Parameters, Return Statement, Scope of a Variable, Global Variables, Modules - Introduction to Modules, Importing a Module, Aliasing, from...import statement, import everything, Some important modules - math, platform, random, webbrowser, etc |
| Object Oriented Programming | Classes and Objects - Creating a class, Instantiating an Object, Constructor, Class Members - Variables and Mentods, Types of Variables - Instance, Static and Local Variables, Types of Methods - Instance, Class and Static Methods, Access Modifiers - Public, Private and Protected, Pillars of Object Oriented Programming - Inheritance, Polymorphism, Abstraction and Encapsulation, Setters and Getters, Inheritance vs Association |
| Exception Handling | Errors vs Exception, Syntax and Indentation Errors, try...except block, Control Flow in try...except block, try with multiple except, finally block, try...except...else, Nested try...except...finally, User Defined Exception |
| File Handling | Introduction to File Handling, Opening and Closing a File, File Object Properties, Read Data from Text Files, Write Data to Text Files, with statement, Renaming and Deleting Files |
| Web API | Application Programming Interface, Indian Space Station API, API Request, Status Code, Query Parameters, Getting JSON from an API Request, Working with JSON - dump and load, Working with Twitter API |
| Databases | Introduction to Databases, SQLite3 - Connecting Python with SQLite3, Performing CRUD Opertations, MySQL - Connecting Python with MySQL, Performing CRUD Opertations, MongoDB - Connecting Python with MongoDB, Performing CRUD Opertations, Object Relation Mapping - SQLAlchemy ORM, CRUD operations and Complex DB operations |
| List Comprehension, Lambda, Filter, Map, Reduce | List Comprehension, Anonymous Functions, Filter, Map, Reduce, Function Aliasing |
| Problem Solving for Interviews | Swapping two numbers, Factorial of a number, Prime Number, Fibbonnacci Sequence, Armstrong Number, Palindrome Number, etc |
| Topic Name | What's Covered |
|---|---|
| Data Analytics Framework | Data Collection, Business Understanding, Exploratory Data Analysis, Data Preparation, Model Building, Model Evaluation, Deployment, Understanding Cross Industry Standard Process for Data Mining (CRISP-DM) and Microsoft's Team Data Science Process (TDSP) |
| Numpy | Array Oriented Numerical Computations using Numpy, Creating a Numpy Array, Basic Operations on Numpy Array - Check Dimensions, Shape, Datatypes and ItemSize, Why Numpy, Various ways to create Numpy Array, Numpy arange() function, Numpy Random Module - rand(), randn(), randint(), uniform(), etc, Indexing and Slicing in Numpy Arrays, Applying Mathematical Operations on Numpy Array - add(), subtract(), multiply(), divide(), dot(), matmul(), sum(), log(), exp(), etc, Statistical Operations on Numpy Array - min(), max(), mean(), median(), var(), std(), corrcoef(), etc, Reshaping a Numpy Array, Miscellaneous Topics - Linspace, Sorting, Stacking, Concatenation, Append, Where and Numpy Broadcasting |
| Pandas for Beginners | Pandas Data Structures - Series, Dataframe and Panel, Creating a Series, Data Access, Creating a Dataframe using Tuples and Dictionaries, DataFrame Attributes - columns, shape, dtypes, axes, values, etc, DataFrame Methods - head(), tail(), info(), describe(), Working with .csv and .xlsx - read_csv() and read_excel(), DataFrame to .csv and .xlsx - to_csv() and to_excel() |
| Advance Pandas Operations | What's Covered |
| Case Study - Pandas Manipulation | What's Covered |
| Missing Value Treatment | What's Covered |
| Visuallization Basics - Matplotlib and Seaborn | What's Covered |
| Case Study - Covid_19_TimeSeries | What's Covered |
| Plotly and Express | What's Covered |
| Outliers - Coming Soon | What's Covered |
| Topic Name | What's Covered |
|---|---|
| Normal Distribution | What's Covered |
| Central Limit Theorem | What's Covered |
| Hypothesis Testing | What's Covered |
| Chi Square Testing | What's Covered |
| Performing Statistical Test | What's Covered |
- Data Preparation and Modelling with SKLearn
- Working with Text Data
- Working with Image Data
-
Supervised ML Algorithms
- K - Nearest Neighbours
- Linear Regression
- Logistic Regression
- Gradient Descent
- Decision Trees
- Support Vector Machines
- Models with Feature Engineering
- Hyperparameter Tuning
- Ensembles -
Unsupervised ML Algorithms
- Clustering
- Principal Component Analysis
| Topic Name | What's Covered |
|---|---|
| Model Serialization and Deserialization | What's Covered |
| Application Integration | What's Covered |
| MLFlow - Experiment Tracking and Model Management | What's Covered |
| Prefect - Orchestrate ML Pipeline | What's Covered |
| Topic Name | What's Covered |
|---|---|
| Car Price Prediction (Regression) | What's Covered |
| Airline Sentiment Analysis (NLP - Classification) | What's Covered |
| Adult Income Prediction (Classification) | What's Covered |
| Web App Development + Serialization and Deserialization | What's Covered |
| AWS Deployment | What's Covered |
| Streamlit Heroku Deployment | What's Covered |
| Customer Segmentation | What's Covered |
| Web Scrapping | What's Covered |
| Topic Name | What's Covered |
|---|---|
| Introduction to Deep Learning | What's Covered |
| Training a Deep Neural Network + TensorFlow.Keras | What's Covered |
| Convolutional Neural Network + TensorFlow.Keras | What's Covered |
| Auto Encoders for Image Compression | What's Covered |
| Recurrent Neural Network (Coming Soon) | What's Covered |
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