bansalkanav•Mar 27, 2025
Ultimate-Data-Science-Toolkit---From-Python-Basics-to-GenerativeAI
Getting started with Machine Learning and Deep Learning
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Module 1 - Python Programming
| 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 |
Module 2 - Python for Data Analysis
| 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 - readcsv() and readexcel(), DataFrame to .csv and .xlsx - tocsv() and toexcel() |
| 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 - Covid19TimeSeries | What's Covered |
| Plotly and Express | What's Covered |
| Outliers - Coming Soon | What's Covered |
Module 3 - Statistics for Data Analysis
| 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 |
Module 4 - Machine Learning
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
Module 5 - MLOPs
| 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 |
Module 6 - Case Studies
| 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 |
Module 7 - Deep Learning
| 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 |
![[P] [R] sANNd: A New Neural Network Framework Using Trainable Iterators](https://jfbmhhfxbbrxcmwilqxt.supabase.co/storage/v1/object/public/resource-images/MachineLearning_No-code_AI_20250328_190640_processed_image.jpg)
![[P] Utilizing graph attention-based neural networks and generative AI to build a tool to automate debugging and refactoring Python code](https://jfbmhhfxbbrxcmwilqxt.supabase.co/storage/v1/object/public/resource-images/MachineLearning_AI_automation_tools_20250328_191208_processed_image.jpg)
