Starting your career with Python is an excellent choice due to its versatility and broad range of applications. As you advance, you might discover various specializations that align with your interests:
• Data Science: If you’re excited about analyzing data and extracting insights, diving deeper into data science might be your next step. You’ll use Python libraries like Pandas, NumPy, and SciPy to work with data and build predictive models.
• Machine Learning: If you’re fascinated by building intelligent systems that learn from data, specializing in machine learning could be your calling. Python frameworks like TensorFlow, Keras, and scikit-learn will be key tools in your toolkit.
• Web Development: If you enjoy creating web applications, focusing on web development with Python could be a great path. Frameworks like Django and Flask allow you to build robust and scalable web solutions.
• Automation and Scripting: If you’re interested in automating repetitive tasks and creating scripts to improve efficiency, Python is a perfect choice. You'll use libraries like Selenium and BeautifulSoup for web scraping, and automation tools like Celery for task scheduling.
• Data Engineering: If you’re keen on building data pipelines and managing large datasets, specializing in data engineering might be your next move. Python’s integration with tools like Apache Airflow and Apache Spark can be particularly useful.
• DevOps: If you enjoy managing and automating the deployment of applications, focusing on DevOps with Python might be a good fit. Python can be used for scripting and integrating with tools like Docker and Kubernetes.
• Game Development: If you're interested in creating games, you might explore game development with Python using libraries like Pygame, which can be a fun and creative way to apply your programming skills.
Even if you stick with general Python programming, there’s always something new to explore, especially with the constant evolution of libraries and tools.
The key is to continue coding, experimenting with different projects, and staying updated with industry trends. Each step in Python opens up new opportunities to build diverse and impactful applications.
I have curated the best interview resources to crack Python Interviews 👇👇
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• Data Science: If you’re excited about analyzing data and extracting insights, diving deeper into data science might be your next step. You’ll use Python libraries like Pandas, NumPy, and SciPy to work with data and build predictive models.
• Machine Learning: If you’re fascinated by building intelligent systems that learn from data, specializing in machine learning could be your calling. Python frameworks like TensorFlow, Keras, and scikit-learn will be key tools in your toolkit.
• Web Development: If you enjoy creating web applications, focusing on web development with Python could be a great path. Frameworks like Django and Flask allow you to build robust and scalable web solutions.
• Automation and Scripting: If you’re interested in automating repetitive tasks and creating scripts to improve efficiency, Python is a perfect choice. You'll use libraries like Selenium and BeautifulSoup for web scraping, and automation tools like Celery for task scheduling.
• Data Engineering: If you’re keen on building data pipelines and managing large datasets, specializing in data engineering might be your next move. Python’s integration with tools like Apache Airflow and Apache Spark can be particularly useful.
• DevOps: If you enjoy managing and automating the deployment of applications, focusing on DevOps with Python might be a good fit. Python can be used for scripting and integrating with tools like Docker and Kubernetes.
• Game Development: If you're interested in creating games, you might explore game development with Python using libraries like Pygame, which can be a fun and creative way to apply your programming skills.
Even if you stick with general Python programming, there’s always something new to explore, especially with the constant evolution of libraries and tools.
The key is to continue coding, experimenting with different projects, and staying updated with industry trends. Each step in Python opens up new opportunities to build diverse and impactful applications.
I have curated the best interview resources to crack Python Interviews 👇👇
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope you'll like it
Like this post if you need more resources like this 👍❤️
👍13❤11
Pandas is a powerful and versatile library in Python, especially for data science tasks.
Here are some key Pandas methods that are widely used:
Data Loading and Creation
* read_csv(): Reads data from a CSV file into a DataFrame.
* read_excel(): Reads data from an Excel file into a DataFrame.
* DataFrame(): Creates a new DataFrame from a dictionary, list, or NumPy array.
Data Exploration and Selection
* head(): Returns the first few rows of a DataFrame.
* tail(): Returns the last few rows of a DataFrame.
* shape(): Returns the dimensions of a DataFrame (rows, columns).
* info(): Provides summary information about the DataFrame, including data types and missing values.
* describe(): Generates summary statistics for numerical columns.
* loc[]: Selects rows and columns by label.
* iloc[]: Selects rows and columns by integer position.
* filter(): Selects columns by name.
Data Cleaning and Transformation
* dropna(): Removes rows or columns with missing values.
* fillna(): Fills missing values with a specified value or strategy.
* drop_duplicates(): Removes duplicate rows.
* apply(): Applies a function to each element or row/column.
* groupby(): Groups data based on one or more columns and performs aggregate functions.
* pivot_table(): Creates a pivot table for data summarization.
* merge(): Merges DataFrames based on a common column.
Data Visualization
* plot(): Creates various types of plots (line, bar, scatter, etc.).
* hist(): Creates a histogram.
* boxplot(): Creates a box plot.
These are just a few examples of the many powerful methods that Pandas offers. By mastering these methods, you can efficiently load, clean, transform, analyze, and visualize data for your data science projects.
Example:
import pandas as pd
# Load data from a CSV file
df = pd.read_csv('data.csv')
# Select the first 5 rows
print(df.head())
# Group data by a column and calculate the mean
grouped_df = df.groupby('column_name').mean()
# Create a bar plot
grouped_df.plot(kind='bar')
Here are some key Pandas methods that are widely used:
Data Loading and Creation
* read_csv(): Reads data from a CSV file into a DataFrame.
* read_excel(): Reads data from an Excel file into a DataFrame.
* DataFrame(): Creates a new DataFrame from a dictionary, list, or NumPy array.
Data Exploration and Selection
* head(): Returns the first few rows of a DataFrame.
* tail(): Returns the last few rows of a DataFrame.
* shape(): Returns the dimensions of a DataFrame (rows, columns).
* info(): Provides summary information about the DataFrame, including data types and missing values.
* describe(): Generates summary statistics for numerical columns.
* loc[]: Selects rows and columns by label.
* iloc[]: Selects rows and columns by integer position.
* filter(): Selects columns by name.
Data Cleaning and Transformation
* dropna(): Removes rows or columns with missing values.
* fillna(): Fills missing values with a specified value or strategy.
* drop_duplicates(): Removes duplicate rows.
* apply(): Applies a function to each element or row/column.
* groupby(): Groups data based on one or more columns and performs aggregate functions.
* pivot_table(): Creates a pivot table for data summarization.
* merge(): Merges DataFrames based on a common column.
Data Visualization
* plot(): Creates various types of plots (line, bar, scatter, etc.).
* hist(): Creates a histogram.
* boxplot(): Creates a box plot.
These are just a few examples of the many powerful methods that Pandas offers. By mastering these methods, you can efficiently load, clean, transform, analyze, and visualize data for your data science projects.
Example:
import pandas as pd
# Load data from a CSV file
df = pd.read_csv('data.csv')
# Select the first 5 rows
print(df.head())
# Group data by a column and calculate the mean
grouped_df = df.groupby('column_name').mean()
# Create a bar plot
grouped_df.plot(kind='bar')
👍10❤4
10 Ways to Speed Up Your Python Code
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.
Best Programming Resources: https://topmate.io/coding/898340
All the best 👍👍
1. List Comprehensions
numbers = [x**2 for x in range(100000) if x % 2 == 0]
instead of
numbers = []
for x in range(100000):
if x % 2 == 0:
numbers.append(x**2)
2. Use the Built-In Functions
Many of Python’s built-in functions are written in C, which makes them much faster than a pure python solution.
3. Function Calls Are Expensive
Function calls are expensive in Python. While it is often good practice to separate code into functions, there are times where you should be cautious about calling functions from inside of a loop. It is better to iterate inside a function than to iterate and call a function each iteration.
4. Lazy Module Importing
If you want to use the time.sleep() function in your code, you don't necessarily need to import the entire time package. Instead, you can just do from time import sleep and avoid the overhead of loading basically everything.
5. Take Advantage of Numpy
Numpy is a highly optimized library built with C. It is almost always faster to offload complex math to Numpy rather than relying on the Python interpreter.
6. Try Multiprocessing
Multiprocessing can bring large performance increases to a Python script, but it can be difficult to implement properly compared to other methods mentioned in this post.
7. Be Careful with Bulky Libraries
One of the advantages Python has over other programming languages is the rich selection of third-party libraries available to developers. But, what we may not always consider is the size of the library we are using as a dependency, which could actually decrease the performance of your Python code.
8. Avoid Global Variables
Python is slightly faster at retrieving local variables than global ones. It is simply best to avoid global variables when possible.
9. Try Multiple Solutions
Being able to solve a problem in multiple ways is nice. But, there is often a solution that is faster than the rest and sometimes it comes down to just using a different method or data structure.
10. Think About Your Data Structures
Searching a dictionary or set is insanely fast, but lists take time proportional to the length of the list. However, sets and dictionaries do not maintain order. If you care about the order of your data, you can’t make use of dictionaries or sets.
Best Programming Resources: https://topmate.io/coding/898340
All the best 👍👍
👍7
PROGRAMMING LANGUAGES YOU SHOULD LEARN TO BECOME 👩💻🧑💻⚔️[ Web Developer]
PHP, C#, JS, JAVA, Python, Ruby
⚔️[ Game Developer]
Java, C++, Python, JS, Ruby, C, C#
⚔️[ Data Analysis]
R, Matlab, Java, Python
⚔️[ Desktop Developer]
Java, C#, C++, Python
⚔️[ Embedded System Program]
C, Python, C++
⚔️[ Mobile Apps Development]
Kotlin, Dart, Objective-C, Java, Python, JS, Swift, C#
Join this community for FAANG Jobs : https://t.iss.one/faangjob
👍4
Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started:
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Free Resources on WhatsApp: https://whatsapp.com/channel/0029VauCKUI6WaKrgTHrRD0i
1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.
2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.
3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.
4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.
5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.
6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.
7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.
Free Resources on WhatsApp: https://whatsapp.com/channel/0029VauCKUI6WaKrgTHrRD0i
👍4
If you want to learn Python for data analysis, focus on these essentials
Don't aim for this:
NumPy - 100%
Pandas - 0%
Matplotlib - 0%
Seaborn - 0%
OS - 0%
Aim for this:
NumPy - 25%
Pandas - 25%
Matplotlib - 25%
Seaborn - 25%
OS - 25%
You don't need to master everything at once.
Focus on the essentials to build a strong foundation.
#python
Don't aim for this:
NumPy - 100%
Pandas - 0%
Matplotlib - 0%
Seaborn - 0%
OS - 0%
Aim for this:
NumPy - 25%
Pandas - 25%
Matplotlib - 25%
Seaborn - 25%
OS - 25%
You don't need to master everything at once.
Focus on the essentials to build a strong foundation.
#python
👍14👏4❤1
Essential Python Libraries to build your career in Data Science 📊👇
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Seaborn:
- Statistical data visualization built on top of Matplotlib.
5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
7. PyTorch:
- Deep learning library, particularly popular for neural network research.
8. SciPy:
- Library for scientific and technical computing.
9. Statsmodels:
- Statistical modeling and econometrics in Python.
10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).
11. Gensim:
- Topic modeling and document similarity analysis.
12. Keras:
- High-level neural networks API, running on top of TensorFlow.
13. Plotly:
- Interactive graphing library for making interactive plots.
14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
15. OpenCV:
- Library for computer vision tasks.
As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.
Free Notes & Books to learn Data Science: https://t.iss.one/datasciencefree
Python Project Ideas: https://t.iss.one/dsabooks/85
Best Resources to learn Python & Data Science 👇👇
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING👍👍
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Seaborn:
- Statistical data visualization built on top of Matplotlib.
5. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
6. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
7. PyTorch:
- Deep learning library, particularly popular for neural network research.
8. SciPy:
- Library for scientific and technical computing.
9. Statsmodels:
- Statistical modeling and econometrics in Python.
10. NLTK (Natural Language Toolkit):
- Tools for working with human language data (text).
11. Gensim:
- Topic modeling and document similarity analysis.
12. Keras:
- High-level neural networks API, running on top of TensorFlow.
13. Plotly:
- Interactive graphing library for making interactive plots.
14. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
15. OpenCV:
- Library for computer vision tasks.
As a beginner, you can start with Pandas and NumPy for data manipulation and analysis. For data visualization, Matplotlib and Seaborn are great starting points. As you progress, you can explore machine learning with Scikit-learn, TensorFlow, and PyTorch.
Free Notes & Books to learn Data Science: https://t.iss.one/datasciencefree
Python Project Ideas: https://t.iss.one/dsabooks/85
Best Resources to learn Python & Data Science 👇👇
Python Tutorial
Data Science Course by Kaggle
Machine Learning Course by Google
Best Data Science & Machine Learning Resources
Interview Process for Data Science Role at Amazon
Python Interview Resources
Join @free4unow_backup for more free courses
Like for more ❤️
ENJOY LEARNING👍👍
❤2
Python Game Development Roadmap
Stage 1 - Learn Python basics (syntax, OOP).
Stage 2 - Study game physics and logic fundamentals.
Stage 3 - Use Pygame to prototype 2D games.
Stage 4 - Add input systems (controllers, keyboard, mouse).
Stage 5 - Add sound effects with PyGame Mixer.
Stage 6 - Explore OpenGL or Panda3D for 3D games.
Stage 7 - Add visual effects (shaders, lighting).
Stage 8 - Package and distribute games with tools like cx_Freeze or PyInstaller.
🏆 – Python Game Developer
Stage 1 - Learn Python basics (syntax, OOP).
Stage 2 - Study game physics and logic fundamentals.
Stage 3 - Use Pygame to prototype 2D games.
Stage 4 - Add input systems (controllers, keyboard, mouse).
Stage 5 - Add sound effects with PyGame Mixer.
Stage 6 - Explore OpenGL or Panda3D for 3D games.
Stage 7 - Add visual effects (shaders, lighting).
Stage 8 - Package and distribute games with tools like cx_Freeze or PyInstaller.
🏆 – Python Game Developer
𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 (𝗡𝗼 𝗦𝘁𝗿𝗶𝗻𝗴𝘀 𝗔𝘁𝘁𝗮𝗰𝗵𝗲𝗱)
𝗡𝗼 𝗳𝗮𝗻𝗰𝘆 𝗰𝗼𝘂𝗿𝘀𝗲𝘀, 𝗻𝗼 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀, 𝗷𝘂𝘀𝘁 𝗽𝘂𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.
𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘:
1️⃣ Python Programming for Data Science → Harvard’s CS50P
The best intro to Python for absolute beginners:
↬ Covers loops, data structures, and practical exercises.
↬ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://t.iss.one/datasciencefun
2️⃣ Statistics & Probability → Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
↬ Clear, beginner-friendly videos.
↬ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3️⃣ Linear Algebra for Data Science → 3Blue1Brown
↬ Learn about matrices, vectors, and transformations.
↬ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4️⃣ SQL Basics → Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
↬ Writing queries, joins, and filtering data.
↬ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5️⃣ Data Visualization → freeCodeCamp
Learn to create stunning visualizations using Python libraries:
↬ Covers Matplotlib, Seaborn, and Plotly.
↬ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6️⃣ Machine Learning Basics → Google’s Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
↬ Learn supervised and unsupervised learning.
↬ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7️⃣ Deep Learning → Fast.ai’s Free Course
Fast.ai makes deep learning easy and accessible:
↬ Build neural networks with PyTorch.
↬ Learn by coding real projects.
Link: https://course.fast.ai/
8️⃣ Data Science Projects → Kaggle
↬ Compete in challenges to practice your skills.
↬ Great way to build your portfolio.
Link: https://www.kaggle.com/
𝗡𝗼 𝗳𝗮𝗻𝗰𝘆 𝗰𝗼𝘂𝗿𝘀𝗲𝘀, 𝗻𝗼 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀, 𝗷𝘂𝘀𝘁 𝗽𝘂𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.
𝗛𝗲𝗿𝗲’𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘:
1️⃣ Python Programming for Data Science → Harvard’s CS50P
The best intro to Python for absolute beginners:
↬ Covers loops, data structures, and practical exercises.
↬ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://t.iss.one/datasciencefun
2️⃣ Statistics & Probability → Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
↬ Clear, beginner-friendly videos.
↬ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3️⃣ Linear Algebra for Data Science → 3Blue1Brown
↬ Learn about matrices, vectors, and transformations.
↬ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4️⃣ SQL Basics → Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
↬ Writing queries, joins, and filtering data.
↬ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5️⃣ Data Visualization → freeCodeCamp
Learn to create stunning visualizations using Python libraries:
↬ Covers Matplotlib, Seaborn, and Plotly.
↬ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6️⃣ Machine Learning Basics → Google’s Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
↬ Learn supervised and unsupervised learning.
↬ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7️⃣ Deep Learning → Fast.ai’s Free Course
Fast.ai makes deep learning easy and accessible:
↬ Build neural networks with PyTorch.
↬ Learn by coding real projects.
Link: https://course.fast.ai/
8️⃣ Data Science Projects → Kaggle
↬ Compete in challenges to practice your skills.
↬ Great way to build your portfolio.
Link: https://www.kaggle.com/
👍5❤1