How to get job as python fresher?

1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.

2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.

3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.

@crackingthecodinginterview

4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.

5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.

Python Interview Q&A: https://topmate.io/coding/898340

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📌 Python Cheatsheet: Master the Foundations & Beyond
Start learning Python →

⬇️ Core Python Building Blocks

Basic Commands
→ print() – Display output
→ input() – Get user input
→ len() – Get length of a data structure
→ type() – Get variable type
→ range() – Generate a sequence
→ help() – Get documentation

Data Types
→ int, float, bool, str – Numbers & text
→ list, tuple, dict, set – Data collections

Control Structures
→ if / elif / else – Conditional logic
→ for, while – Loops
→ break, continue, pass – Loop control

⬇️ Advanced Concepts

Functions & Classes
→ def, return, lambda – Define functions
→ class, init, self – Object-oriented programming

Modules
→ import, from ... import – Reuse code

⬇️ Special Tools

Exception Handling
→ try, except, finally, raise – Handle errors

File Handling
→ open(), read(), write(), close() – Manage files

Decorators & Generators
→ @decorator, yield – Extend or pause functions

List Comprehension
→ [x for x in list if condition] – Create lists efficiently


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Are you looking to become a machine learning engineer? 🤖
The algorithm brought you to the right place! 🚀

I created a free and comprehensive roadmap. Let’s go through this thread and explore what you need to know to become an expert machine learning engineer:

📚 Math & Statistics
Just like most other data roles, machine learning engineering starts with strong foundations from math, especially in linear algebra, probability, and statistics. Here’s what you need to focus on:

- Basic probability concepts 🎲
- Inferential statistics 📊
- Regression analysis 📈
- Experimental design & A/B testing 🔍
- Bayesian statistics 🔢
- Calculus 🧮
- Linear algebra 🔠

🐍 Python
You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.

- Variables, data types, and basic operations ✏️
- Control flow statements (e.g., if-else, loops) 🔄
- Functions and modules 🔧
- Error handling and exceptions ❌
- Basic data structures (e.g., lists, dictionaries, tuples) 🗂️
- Object-oriented programming concepts 🧱
- Basic work with APIs 🌐
- Detailed data structures and algorithmic thinking 🧠

🧪 Machine Learning Prerequisites
- Exploratory Data Analysis (EDA) with NumPy and Pandas 🔍
- Data visualization techniques to visualize variables 📉
- Feature extraction & engineering 🛠️
- Encoding data (different types) 🔐

⚙️ Machine Learning Fundamentals
Use the scikit-learn library along with other Python libraries for:

- Supervised Learning: Linear Regression, K-Nearest Neighbors, Decision Trees 📊
- Unsupervised Learning: K-Means Clustering, Principal Component Analysis, Hierarchical Clustering 🧠
- Reinforcement Learning: Q-Learning, Deep Q Network, Policy Gradients 🕹️

Solve two types of problems:
- Regression 📈
- Classification 🧩

🧠 Neural Networks
Neural networks are like computer brains that learn from examples 🧠, made up of layers of "neurons" that handle data. They learn without explicit instructions.

Types of Neural Networks:
- Feedforward Neural Networks: Simplest form, with straight connections and no loops 🔄
- Convolutional Neural Networks (CNNs): Great for images, learning visual patterns 🖼️
- Recurrent Neural Networks (RNNs): Good for sequences like text or time series 📚

In Python, use TensorFlow and Keras, as well as PyTorch for more complex neural network systems.

🕸️ Deep Learning
Deep learning is a subset of machine learning that can learn unsupervised from data that is unstructured or unlabeled.

- CNNs 🖼️
- RNNs 📝
- LSTMs ⏳

🚀 Machine Learning Project Deployment

Machine learning engineers should dive into MLOps and project deployment.

Here are the must-have skills:

- Version Control for Data and Models 🗃️
- Automated Testing and Continuous Integration (CI) 🔄
- Continuous Delivery and Deployment (CD) 🚚
- Monitoring and Logging 🖥️
- Experiment Tracking and Management 🧪
- Feature Stores 🗂️
- Data Pipeline and Workflow Orchestration 🛠️
- Infrastructure as Code (IaC) 🏗️
- Model Serving and APIs 🌐

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

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