README 中多个章节标注为 Coming Soon 或未填写内容，实际内容可能不完整；仓库以教程笔记为主，未提供统一的依赖安装或运行入口
Ultimate-Data-Science-Toolkit---From-Python-Basics-to-GenerativeAI
bansalkanavGPL-3.0更新放缓一套从 Python 基础到生成式 AI 的数据科学实战教程，覆盖数据分析、深度学习、MLflow 与 Prefect 等 MLOps 工具链，适合系统学习并动手实践。
暂无版本972star329fork同步于 2026-11-07 07:30 UTC
git clone https://github.com/bansalkanav/Machine_Learning_and_Deep_Learning.git 硬件要求按 README 说明
README 未说明
从 Python 基础到生成式 AI 的系统化机器学习与深度学习教程仓库，按模块组织代码与笔记
一套从 Python 基础到生成式 AI 的数据科学实战教程，覆盖数据分析、深度学习、MLflow 与 Prefect 等 MLOps 工具链，适合系统学习并动手实践。
核心能力
- 按模块组织 Python 基础、数据分析、统计与机器学习内容
- 覆盖 Numpy 数组运算、广播与统计函数
- 用 Pandas 读写 csv/xlsx 并做数据框操作
- 包含 CRISP-DM 与 TDSP 数据分析流程讲解
- 提供面试题练习如质数、斐波那契、回文等
容易踩的坑
内容由模型根据 README 生成，可能不完整或不准确，以官方文档为准。看原文 README
暂无 release 数据（可能没有发版，或尚未同步）。
用户点评
登录用户的真实使用记录，可按版本和硬件筛选共 0 条
还没有人点评。第一个说出真实体验的人最有价值。
Getting started with Machine Learning and Deep Learning
Star this repo if you find it useful :star:
| 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 |
| Topic Name | What's Covered |
|---|---|
| Working with Text Data and Text Embeddings | What's Covered |
| Transformers, LLMs, and Prompts | What's Covered |
| LLM Model Provider SDK | What's Covered |
| LLM Inference Provider SDK | What's Covered |
| LangChain Essentials | What's Covered |
| RAGs | What's Covered |
| Orchestration with LangGraph | What's Covered |
| Agentic AI | What's Covered |
| Evaluation | What's Covered |
| LangChain Microsoft Foundry SDK | What's Covered |
netdata
netdata·Go
开箱即用的全栈监控平台，秒级采集指标并自动告警，内置机器学习异常检测，小团队也能低成本搭建可观测性。
全栈监控+4分析 / BI
81345+496.7kv2.12.32026-11-10 10:44 UTC
TrendRadar
sansan0·Python
聚合多平台热点与 RSS 订阅，用 AI 做关键词筛选、翻译和简报分析，并通过微信、飞书、钉钉等渠道推送到手机，帮你摆脱信息过载。
舆情监控+4分析 / BI
63563+6525k2026-11-05 00:47 UTC
best-of-ml-python
lukasmasuch
每周更新的机器学习 Python 库排行榜，按类别和评分整理优质库，帮你快速找到 AutoML、深度学习、数据可视化等方向的趁手工具。
机器学习库+4分析 / BI
23860+23.1k2025.10.302026-11-05 14:09 UTC
vanna
vanna-ai·Python
🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using Agentic Retrieval 🔄.
分析 / BI
238062.5k2026-02-02 14:14 UTC