Complete Roadmap to learn Generative AI in 2 months ๐๐
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI ๐๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
Weeks 1-2: Foundations
1. Learn Basics of Python: If not familiar, grasp the fundamentals of Python, a widely used language in AI.
2. Understand Linear Algebra and Calculus: Brush up on basic linear algebra and calculus as they form the foundation of machine learning.
Weeks 3-4: Machine Learning Basics
1. Study Machine Learning Fundamentals: Understand concepts like supervised learning, unsupervised learning, and evaluation metrics.
2. Get Familiar with TensorFlow or PyTorch: Choose one deep learning framework and learn its basics.
Weeks 5-6: Deep Learning
1. Neural Networks: Dive into neural networks, understanding architectures, activation functions, and training processes.
2. CNNs and RNNs: Learn Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
Weeks 7-8: Generative Models
1. Understand Generative Models: Study the theory behind generative models, focusing on GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).
2. Hands-On Projects: Implement small generative projects to solidify your understanding. Experimenting with generative models will give you a deeper understanding of how they work. You can use platforms such as Google's Colab or Kaggle to experiment with different types of generative models.
Additional Tips:
- Read Research Papers: Explore seminal papers on GANs and VAEs to gain a deeper insight into their workings.
- Community Engagement: Join AI communities on platforms like Reddit or Stack Overflow to ask questions and learn from others.
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of Generative AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn Generative AI ๐๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Deep Learning Nanodegree Program with Real-world Projects
Join @free4unow_backup for more free courses
ENJOY LEARNING๐๐
โค3
Forwarded from Data Analysis Books | Python | SQL | Excel | Artificial Intelligence | Power BI | Tableau | AI Resources
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IBM, one of the biggest tech companies, is offering 5 FREE courses that can seriously upgrade your resume and skills โ without costing you anything.
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Essential Python Libraries for Data Analytics ๐๐
Python Free Resources: https://t.iss.one/pythondevelopersindia
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. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Python Free Resources: https://t.iss.one/pythondevelopersindia
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. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐2
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Dreaming of Mastering AI? ๐ฏ
Harvard and Stanfordโtwo of the most prestigious universities in the worldโare offering FREE AI courses๐จโ๐ป
No hidden fees, no long applicationsโjust pure, world-class education, accessible to everyone๐ฅ
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Hereโs your golden ticket to the future!โ
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Python Interview Questions for Data/Business Analysts:
Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?
Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.
Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?
Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.
Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.
Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.
Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?
Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?
Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.
Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?
Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?
Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?
Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.
Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
Question 1:
Given a dataset in a CSV file, how would you read it into a Pandas DataFrame? And how would you handle missing values?
Question 2:
Describe the difference between a list, a tuple, and a dictionary in Python. Provide an example for each.
Question 3:
Imagine you are provided with two datasets, 'sales_data' and 'product_data', both in the form of Pandas DataFrames. How would you merge these datasets on a common column named 'ProductID'?
Question 4:
How would you handle duplicate rows in a Pandas DataFrame? Write a Python code snippet to demonstrate.
Question 5:
Describe the difference between '.iloc[] and '.loc[]' in the context of Pandas.
Question 6:
In Python's Matplotlib library, how would you plot a line chart to visualize monthly sales? Assume you have a list of months and a list of corresponding sales numbers.
Question 7:
How would you use Python to connect to a SQL database and fetch data into a Pandas DataFrame?
Question 8:
Explain the concept of list comprehensions in Python. Can you provide an example where it's useful for data analysis?
Question 9:
How would you reshape a long-format DataFrame to a wide format using Pandas? Explain with an example.
Question 10:
What are lambda functions in Python? How are they beneficial in data wrangling tasks?
Question 11:
Describe a scenario where you would use the 'groupby()' method in Pandas. How would you aggregate data after grouping?
Question 12:
You are provided with a Pandas DataFrame that contains a column with date strings. How would you convert this column to a datetime format? Additionally, how would you extract the month and year from these datetime objects?
Question 13:
Explain the purpose of the 'pivot_table' method in Pandas and describe a business scenario where it might be useful.
Question 14:
How would you handle large datasets that don't fit into memory? Are you familiar with Dask or any similar libraries?
Python Interview Q&A: https://topmate.io/coding/898340
Like for more โค๏ธ
ENJOY LEARNING ๐๐
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Some interview questions related to Data science
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
1- what is difference between structured data and unstructured data.
2- what is multicollinearity.and how to remove them
3- which algorithms you use to find the most correlated features in the datasets.
4- define entropy
5- what is the workflow of principal component analysis
6- what are the applications of principal component analysis not with respect to dimensionality reduction
7- what is the Convolutional neural network. Explain me its working
๐2
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Dreaming of becoming a Data Analyst but feel overwhelmed by where to start?๐จโ๐ป
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To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://t.iss.one/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or ๐ and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.โ๏ธโ๏ธ
โค2
Artificial Intelligence (AI) Roadmap
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Artificial Intelligence WhatsApp channel
Hands-on Machine Learning
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Like this post for more roadmaps โค๏ธ
Follow & share the channel link with your friends: t.iss.one/free4unow_backup
ENJOY LEARNING๐๐
|
|-- Fundamentals
| |-- Mathematics
| | |-- Linear Algebra
| | |-- Calculus
| | |-- Probability and Statistics
| |
| |-- Programming
| | |-- Python (Focus on Libraries like NumPy, Pandas)
| | |-- Java or C++ (optional but useful)
| |
| |-- Algorithms and Data Structures
| | |-- Graphs and Trees
| | |-- Dynamic Programming
| | |-- Search Algorithms (e.g., A*, Minimax)
|
|-- Core AI Concepts
| |-- Knowledge Representation
| |-- Search Methods (DFS, BFS)
| |-- Constraint Satisfaction Problems
| |-- Logical Reasoning
|
|-- Machine Learning (ML)
| |-- Supervised Learning (Regression, Classification)
| |-- Unsupervised Learning (Clustering, Dimensionality Reduction)
| |-- Reinforcement Learning (Q-Learning, Policy Gradient Methods)
| |-- Ensemble Methods (Random Forest, Gradient Boosting)
|
|-- Deep Learning (DL)
| |-- Neural Networks
| |-- Convolutional Neural Networks (CNNs)
| |-- Recurrent Neural Networks (RNNs)
| |-- Transformers (BERT, GPT)
| |-- Frameworks (TensorFlow, PyTorch)
|
|-- Natural Language Processing (NLP)
| |-- Text Preprocessing (Tokenization, Lemmatization)
| |-- NLP Models (Word2Vec, BERT)
| |-- Applications (Chatbots, Sentiment Analysis, NER)
|
|-- Computer Vision
| |-- Image Processing
| |-- Object Detection (YOLO, SSD)
| |-- Image Segmentation
| |-- Applications (Facial Recognition, OCR)
|
|-- Ethical AI
| |-- Fairness and Bias
| |-- Privacy and Security
| |-- Explainability (SHAP, LIME)
|
|-- Applications of AI
| |-- Healthcare (Diagnostics, Personalized Medicine)
| |-- Finance (Fraud Detection, Algorithmic Trading)
| |-- Retail (Recommendation Systems, Inventory Management)
| |-- Autonomous Vehicles (Perception, Control Systems)
|
|-- AI Deployment
| |-- Model Serving (Flask, FastAPI)
| |-- Cloud Platforms (AWS SageMaker, Google AI)
| |-- Edge AI (TensorFlow Lite, ONNX)
|
|-- Advanced Topics
| |-- Multi-Agent Systems
| |-- Generative Models (GANs, VAEs)
| |-- Knowledge Graphs
| |-- AI in Quantum Computing
Best Resources to learn ML & AI ๐
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Machine Learning with Python Free Course
Machine Learning Free Book
Artificial Intelligence WhatsApp channel
Hands-on Machine Learning
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Like this post for more roadmaps โค๏ธ
Follow & share the channel link with your friends: t.iss.one/free4unow_backup
ENJOY LEARNING๐๐
๐2
๐ง๐๐ฆ ๐๐ฅ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ข๐ป ๐๐ฎ๐๐ฎ ๐ ๐ฎ๐ป๐ฎ๐ด๐ฒ๐บ๐ฒ๐ป๐ - ๐๐ป๐ฟ๐ผ๐น๐น ๐๐ผ๐ฟ ๐๐ฅ๐๐๐
Want to know how top companies handle massive amounts of data without losing track? ๐
TCS is offering a FREE beginner-friendly course on Master Data Management, and yesโit comes with a certificate! ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jGFBw0
Just click and start learning!โ ๏ธ
Want to know how top companies handle massive amounts of data without losing track? ๐
TCS is offering a FREE beginner-friendly course on Master Data Management, and yesโit comes with a certificate! ๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4jGFBw0
Just click and start learning!โ ๏ธ
๐2
๐ฑ ๐๐ฟ๐ฒ๐ฒ ๐ช๐ฒ๐ฏ๐๐ถ๐๐ฒ๐ ๐๐ผ ๐๐ฒ๐ฎ๐ฟ๐ป ๐ฃ๐๐๐ต๐ผ๐ป ๐ณ๐ฟ๐ผ๐บ ๐ฆ๐ฐ๐ฟ๐ฎ๐๐ฐ๐ต ๐ถ๐ป ๐ฎ๐ฌ๐ฎ๐ฑ (๐ก๐ผ ๐๐ป๐๐ฒ๐๐๐บ๐ฒ๐ป๐ ๐ก๐ฒ๐ฒ๐ฑ๐ฒ๐ฑ!)๐
If youโre serious about starting your tech journey, Python is one of the best languages to master๐จโ๐ป๐จโ๐
Iโve found 5 hidden gems that offer beginner tutorials, advanced exercises, and even real-world projects โ absolutely FREE๐ฅ
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Start today, and youโll thank yourself tomorrow.โ ๏ธ
If youโre serious about starting your tech journey, Python is one of the best languages to master๐จโ๐ป๐จโ๐
Iโve found 5 hidden gems that offer beginner tutorials, advanced exercises, and even real-world projects โ absolutely FREE๐ฅ
๐๐ข๐ง๐ค๐:-
https://pdlink.in/4lOVqmb
Start today, and youโll thank yourself tomorrow.โ ๏ธ
๐1
Forwarded from Artificial Intelligence
๐๐ผ๐ผ๐ด๐น๐ฒ ๐๐ฅ๐๐ ๐๐ ๐๐ฒ๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐๐ผ๐๐ฟ๐๐ฒ๐๐
Ever wondered how machines describe images in words?๐ป
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Ever wondered how machines describe images in words?๐ป
Want to get hands-on with cutting-edge AI and computer vision โ for FREE?๐
๐๐ข๐ง๐ค๐:-
https://pdlink.in/42FaT0Y
๐ฏ Start Learning AI for FREE
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1. Negotiating Salary:
Learn how to get the pay you deserve by mastering the art of negotiation.
https://pll.harvard.edu/course/negotiating-salary
Share this telegram channel with your friends: https://t.iss.one/udacityfreecourse
2. Entrepreneurship:
Learn how to build a successful business.
https://pll.harvard.edu/course/technology-entrepreneurship-lab-market
3. Intro to AI:
A beginner's guide to artificial intelligence and its applications in the real world.
https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python
4. Managing Happiness:
Did you know you can learn how to be happier?
Learn how!
https://pll.harvard.edu/course/managing-happiness
5. Mobile App Development:
Learn how to create your mobile app and reach a wider audience.
https://cs50.harvard.edu/mobile/2018/
6. Entrepreneurship in Emerging Economies:
Learn how to start a successful business in countries where the economy is growing fast.
https://pll.harvard.edu/course/entrepreneurship-in-emerging-economies
7. Web Programming:
Learn how to build your website.
https://pll.harvard.edu/course/cs50s-web-programming-python-and-javascript
Share this telegram channel with your friends: https://t.iss.one/udacityfreecourse
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