7 Baby steps to learn Python:
1. Learn the basics: Start with the fundamentals of Python programming language, such as data types, variables, operators, control structures, and functions.
2. Write simple programs: Start writing simple programs to practice what you have learned. Start with small programs that solve basic problems, such as calculating the factorial of a number, checking whether a number is prime or not, or finding the sum of a sequence of numbers.
3. Work on small projects: Start working on small projects that interest you. These can be simple projects, such as creating a calculator, building a basic game, or automating a task. By working on small projects, you can develop your programming skills and gain confidence.
4. Learn from other people's code: Look at other people's code and try to understand how it works. You can find many open-source projects on platforms like GitHub. Analyze the code, see how it's structured, and try to figure out how the program works.
5. Read Python documentation: Python has extensive documentation, which is very helpful for beginners. Read the documentation to learn more about Python libraries, modules, and functions.
6. Participate in online communities: Participate in online communities like StackOverflow, Reddit, or Python forums. These communities have experienced programmers who can help you with your doubts and questions.
7. Keep practicing: Practice is the key to becoming a good programmer. Keep working on projects, practicing coding problems, and experimenting with different techniques. The more you practice, the better you'll get.
Best Resource to learn Python
Freecodecamp Python ML Course with FREE Certificate
Python for Data Analysis
Python course for beginners by Microsoft
Scientific Computing with Python
Python course by Google
Python Free Resources
Please give us credits while sharing: -> https://t.iss.one/free4unow_backup
ENJOY LEARNING 👍👍
1. Learn the basics: Start with the fundamentals of Python programming language, such as data types, variables, operators, control structures, and functions.
2. Write simple programs: Start writing simple programs to practice what you have learned. Start with small programs that solve basic problems, such as calculating the factorial of a number, checking whether a number is prime or not, or finding the sum of a sequence of numbers.
3. Work on small projects: Start working on small projects that interest you. These can be simple projects, such as creating a calculator, building a basic game, or automating a task. By working on small projects, you can develop your programming skills and gain confidence.
4. Learn from other people's code: Look at other people's code and try to understand how it works. You can find many open-source projects on platforms like GitHub. Analyze the code, see how it's structured, and try to figure out how the program works.
5. Read Python documentation: Python has extensive documentation, which is very helpful for beginners. Read the documentation to learn more about Python libraries, modules, and functions.
6. Participate in online communities: Participate in online communities like StackOverflow, Reddit, or Python forums. These communities have experienced programmers who can help you with your doubts and questions.
7. Keep practicing: Practice is the key to becoming a good programmer. Keep working on projects, practicing coding problems, and experimenting with different techniques. The more you practice, the better you'll get.
Best Resource to learn Python
Freecodecamp Python ML Course with FREE Certificate
Python for Data Analysis
Python course for beginners by Microsoft
Scientific Computing with Python
Python course by Google
Python Free Resources
Please give us credits while sharing: -> https://t.iss.one/free4unow_backup
ENJOY LEARNING 👍👍
👍7
Here are some project ideas for a data science and machine learning project focused on generating AI:
1. Natural Language Generation (NLG) Model: Build a model that generates human-like text based on input data. This could be used for creating product descriptions, news articles, or personalized recommendations.
2. Code Generation Model: Develop a model that generates code snippets based on a given task or problem statement. This could help automate software development tasks or assist programmers in writing code more efficiently.
3. Image Captioning Model: Create a model that generates captions for images, describing the content of the image in natural language. This could be useful for visually impaired individuals or for enhancing image search capabilities.
4. Music Generation Model: Build a model that generates music compositions based on input data, such as existing songs or musical patterns. This could be used for creating background music for videos or games.
5. Video Synthesis Model: Develop a model that generates realistic video sequences based on input data, such as a series of images or a textual description. This could be used for generating synthetic training data for computer vision models.
6. Chatbot Generation Model: Create a model that generates conversational agents or chatbots based on input data, such as dialogue datasets or user interactions. This could be used for customer service automation or virtual assistants.
7. Art Generation Model: Build a model that generates artistic images or paintings based on input data, such as art styles, color palettes, or themes. This could be used for creating unique digital artwork or personalized designs.
8. Story Generation Model: Develop a model that generates fictional stories or narratives based on input data, such as plot outlines, character descriptions, or genre preferences. This could be used for creative writing prompts or interactive storytelling applications.
9. Recipe Generation Model: Create a model that generates new recipes based on input data, such as ingredient lists, dietary restrictions, or cuisine preferences. This could be used for meal planning or culinary inspiration.
10. Financial Report Generation Model: Build a model that generates financial reports or summaries based on input data, such as company financial statements, market trends, or investment portfolios. This could be used for automated financial analysis or decision-making support.
Any project which sounds interesting to you?
1. Natural Language Generation (NLG) Model: Build a model that generates human-like text based on input data. This could be used for creating product descriptions, news articles, or personalized recommendations.
2. Code Generation Model: Develop a model that generates code snippets based on a given task or problem statement. This could help automate software development tasks or assist programmers in writing code more efficiently.
3. Image Captioning Model: Create a model that generates captions for images, describing the content of the image in natural language. This could be useful for visually impaired individuals or for enhancing image search capabilities.
4. Music Generation Model: Build a model that generates music compositions based on input data, such as existing songs or musical patterns. This could be used for creating background music for videos or games.
5. Video Synthesis Model: Develop a model that generates realistic video sequences based on input data, such as a series of images or a textual description. This could be used for generating synthetic training data for computer vision models.
6. Chatbot Generation Model: Create a model that generates conversational agents or chatbots based on input data, such as dialogue datasets or user interactions. This could be used for customer service automation or virtual assistants.
7. Art Generation Model: Build a model that generates artistic images or paintings based on input data, such as art styles, color palettes, or themes. This could be used for creating unique digital artwork or personalized designs.
8. Story Generation Model: Develop a model that generates fictional stories or narratives based on input data, such as plot outlines, character descriptions, or genre preferences. This could be used for creative writing prompts or interactive storytelling applications.
9. Recipe Generation Model: Create a model that generates new recipes based on input data, such as ingredient lists, dietary restrictions, or cuisine preferences. This could be used for meal planning or culinary inspiration.
10. Financial Report Generation Model: Build a model that generates financial reports or summaries based on input data, such as company financial statements, market trends, or investment portfolios. This could be used for automated financial analysis or decision-making support.
Any project which sounds interesting to you?
👍7
📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
👍7
Automate_the_Boring_Stuff_with_Python,_2nd_Edition_Practical_Programming.pdf
13.7 MB
One of the best books to learn python
👍7