Android_Programming_The_Big_Nerd_Ranch_Guide.epub
8.8 MB
Android Programming
Kristin Marsicano, 2022
Kristin Marsicano, 2022
Practical Deep Reinforcement Learning.pdf
8.4 MB
Practical Deep Reinforcement Learning with Python
Ivan Gridin, 2022
Ivan Gridin, 2022
Applied Machine Learning.pdf
4.7 MB
Applied Machine Learning Explainability Techniques
Aditya Bhattacharya, 2022
Aditya Bhattacharya, 2022
Machine Learning and Data Science .pdf
15.5 MB
Machine Learning and Data Science
Prateek Agrawal, 2022
Prateek Agrawal, 2022
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7 popular programming languages and their benefits:
1. Python:
- Benefits: Python is known for its simplicity and readability, making it a great choice for beginners. It has a vast ecosystem of libraries and frameworks for various applications such as web development, data science, machine learning, and automation. Python's versatility and ease of use make it a popular choice for a wide range of projects.
2. JavaScript:
- Benefits: JavaScript is the language of the web, used for building interactive and dynamic websites. It is supported by all major browsers and has a large community of developers. JavaScript can also be used for server-side development (Node.js) and mobile app development (React Native). Its flexibility and wide range of applications make it a valuable language to learn.
3. Java:
- Benefits: Java is a robust, platform-independent language commonly used for building enterprise-level applications, mobile apps (Android), and large-scale systems. It has strong support for object-oriented programming principles and a rich ecosystem of libraries and tools. Java's stability, performance, and scalability make it a popular choice for building mission-critical applications.
4. C++:
- Benefits: C++ is a powerful and efficient language often used for system programming, game development, and high-performance applications. It provides low-level control over hardware and memory management while offering high-level abstractions for complex tasks. C++'s performance, versatility, and ability to work closely with hardware make it a preferred choice for performance-critical applications.
5. C#:
- Benefits: C# is a versatile language developed by Microsoft and commonly used for building Windows applications, web applications (with ASP.NET), and games (with Unity). It offers a modern syntax, strong type safety, and seamless integration with the .NET framework. C#'s ease of use, robustness, and support for various platforms make it a popular choice for developing a wide range of applications.
6. R:
- Benefits: R is a language specifically designed for statistical computing and data analysis. It has a rich set of built-in functions and packages for data manipulation, visualization, and machine learning. R's focus on data science, statistical modeling, and visualization makes it an ideal choice for researchers, analysts, and data scientists working with large datasets.
7. Swift:
- Benefits: Swift is Apple's modern programming language for developing iOS, macOS, watchOS, and tvOS applications. It offers safety features to prevent common programming errors, high performance, and interoperability with Objective-C. Swift's clean syntax, powerful features, and seamless integration with Apple's platforms make it a preferred choice for building native applications in the Apple ecosystem.
These are just a few of the many programming languages available today, each with its unique strengths and use cases.
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1. Python:
- Benefits: Python is known for its simplicity and readability, making it a great choice for beginners. It has a vast ecosystem of libraries and frameworks for various applications such as web development, data science, machine learning, and automation. Python's versatility and ease of use make it a popular choice for a wide range of projects.
2. JavaScript:
- Benefits: JavaScript is the language of the web, used for building interactive and dynamic websites. It is supported by all major browsers and has a large community of developers. JavaScript can also be used for server-side development (Node.js) and mobile app development (React Native). Its flexibility and wide range of applications make it a valuable language to learn.
3. Java:
- Benefits: Java is a robust, platform-independent language commonly used for building enterprise-level applications, mobile apps (Android), and large-scale systems. It has strong support for object-oriented programming principles and a rich ecosystem of libraries and tools. Java's stability, performance, and scalability make it a popular choice for building mission-critical applications.
4. C++:
- Benefits: C++ is a powerful and efficient language often used for system programming, game development, and high-performance applications. It provides low-level control over hardware and memory management while offering high-level abstractions for complex tasks. C++'s performance, versatility, and ability to work closely with hardware make it a preferred choice for performance-critical applications.
5. C#:
- Benefits: C# is a versatile language developed by Microsoft and commonly used for building Windows applications, web applications (with ASP.NET), and games (with Unity). It offers a modern syntax, strong type safety, and seamless integration with the .NET framework. C#'s ease of use, robustness, and support for various platforms make it a popular choice for developing a wide range of applications.
6. R:
- Benefits: R is a language specifically designed for statistical computing and data analysis. It has a rich set of built-in functions and packages for data manipulation, visualization, and machine learning. R's focus on data science, statistical modeling, and visualization makes it an ideal choice for researchers, analysts, and data scientists working with large datasets.
7. Swift:
- Benefits: Swift is Apple's modern programming language for developing iOS, macOS, watchOS, and tvOS applications. It offers safety features to prevent common programming errors, high performance, and interoperability with Objective-C. Swift's clean syntax, powerful features, and seamless integration with Apple's platforms make it a preferred choice for building native applications in the Apple ecosystem.
These are just a few of the many programming languages available today, each with its unique strengths and use cases.
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👍2
Complete Roadmap to become a data scientist in 5 months
Free Resources to learn Data Science: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Week 1-2: Fundamentals
- Day 1-3: Introduction to Data Science, its applications, and roles.
- Day 4-7: Brush up on Python programming.
- Day 8-10: Learn basic statistics and probability.
Week 3-4: Data Manipulation and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.
Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.
Work on Data Science Projects: https://t.iss.one/pythonspecialist/29
Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.
Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.
Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).
Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).
Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).
Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.
Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.
Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.
Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.
Week 27-28: Apply for Jobs
- Day 121-125: Start applying for entry-level data scientist positions.
Week 29-30: Interviews
- Day 126-130: Attend interviews, practice whiteboard problems.
Week 31-32: Continuous Learning
- Day 131-135: Stay updated with the latest trends in data science.
Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.
Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.
ENJOY LEARNING 👍👍
Free Resources to learn Data Science: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Week 1-2: Fundamentals
- Day 1-3: Introduction to Data Science, its applications, and roles.
- Day 4-7: Brush up on Python programming.
- Day 8-10: Learn basic statistics and probability.
Week 3-4: Data Manipulation and Visualization
- Day 11-15: Pandas for data manipulation.
- Day 16-20: Data visualization with Matplotlib and Seaborn.
Week 5-6: Machine Learning Foundations
- Day 21-25: Introduction to scikit-learn.
- Day 26-30: Linear regression and logistic regression.
Work on Data Science Projects: https://t.iss.one/pythonspecialist/29
Week 7-8: Advanced Machine Learning
- Day 31-35: Decision trees and random forests.
- Day 36-40: Clustering (K-Means, DBSCAN) and dimensionality reduction.
Week 9-10: Deep Learning
- Day 41-45: Basics of Neural Networks and TensorFlow/Keras.
- Day 46-50: Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Week 11-12: Data Engineering
- Day 51-55: Learn about SQL and databases.
- Day 56-60: Data preprocessing and cleaning.
Week 13-14: Model Evaluation and Optimization
- Day 61-65: Cross-validation, hyperparameter tuning.
- Day 66-70: Evaluation metrics (accuracy, precision, recall, F1-score).
Week 15-16: Big Data and Tools
- Day 71-75: Introduction to big data technologies (Hadoop, Spark).
- Day 76-80: Basics of cloud computing (AWS, GCP, Azure).
Week 17-18: Deployment and Production
- Day 81-85: Model deployment with Flask or FastAPI.
- Day 86-90: Containerization with Docker, cloud deployment (AWS, Heroku).
Week 19-20: Specialization
- Day 91-95: NLP or Computer Vision, based on your interests.
Week 21-22: Projects and Portfolios
- Day 96-100: Work on personal data science projects.
Week 23-24: Soft Skills and Networking
- Day 101-105: Improve communication and presentation skills.
- Day 106-110: Attend online data science meetups or forums.
Week 25-26: Interview Preparation
- Day 111-115: Practice coding interviews on platforms like LeetCode.
- Day 116-120: Review your projects and be ready to discuss them.
Week 27-28: Apply for Jobs
- Day 121-125: Start applying for entry-level data scientist positions.
Week 29-30: Interviews
- Day 126-130: Attend interviews, practice whiteboard problems.
Week 31-32: Continuous Learning
- Day 131-135: Stay updated with the latest trends in data science.
Week 33-34: Accepting Offers
- Day 136-140: Evaluate job offers and negotiate if necessary.
Week 35-36: Settling In
- Day 141-150: Start your new data science job, adapt to the team, and continue learning on the job.
ENJOY LEARNING 👍👍
👍5
Top 15 Fastest Growing Jobs
1. Big Data Specialist
2. FinTech Engineer
3. Al & Machine Learning Specialist
4. Software & Applications Developer
5. Security Management Specialist
6. Data Warehousing Specialist
7. Autonomous & Electric Vehicle Specialist
8. Ul & UX Designer
9. Light Truck or Delivery Service Driver
10. Internet of Things Specialist
11. Data Analyst & Scientist
12. Environmental Engineer
13. Information Security Analyst
14. DevOps Engineer
15. Renewable Energy Engineer
1. Big Data Specialist
2. FinTech Engineer
3. Al & Machine Learning Specialist
4. Software & Applications Developer
5. Security Management Specialist
6. Data Warehousing Specialist
7. Autonomous & Electric Vehicle Specialist
8. Ul & UX Designer
9. Light Truck or Delivery Service Driver
10. Internet of Things Specialist
11. Data Analyst & Scientist
12. Environmental Engineer
13. Information Security Analyst
14. DevOps Engineer
15. Renewable Energy Engineer
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Data structures in Python - cheat sheet
Cost of living (monthly expenses) for one person by country:
🇨🇭 Switzerland: $3,900
🇳🇴 Norway: $3,200
🇮🇸 Iceland: $3,000
🇯🇵 Japan: $2,800
🇱🇺 Luxembourg: $2,700
🇩🇰 Denmark: $2,650
🇸🇬 Singapore: $2,600
🇮🇪 Ireland: $2,500
🇺🇸 United States: $2,450
🇭🇰 Hong Kong: $2,400
🇫🇮 Finland: $2,350
🇦🇪 UAE: $2,300
🇬🇧 UK: $2,250
🇸🇪 Sweden: $2,200
🇩🇪 Germany: $2,150
🇧🇪 Belgium: $2,100
🇫🇷 France: $2,050
🇳🇱 Netherlands: $2,000
🇨🇦 Canada: $1,950
🇦🇹 Austria: $1,900
🇦🇺 Australia: $1,850
🇳🇿 New Zealand: $1,800
🇨🇭 Switzerland: $3,900
🇳🇴 Norway: $3,200
🇮🇸 Iceland: $3,000
🇯🇵 Japan: $2,800
🇱🇺 Luxembourg: $2,700
🇩🇰 Denmark: $2,650
🇸🇬 Singapore: $2,600
🇮🇪 Ireland: $2,500
🇺🇸 United States: $2,450
🇭🇰 Hong Kong: $2,400
🇫🇮 Finland: $2,350
🇦🇪 UAE: $2,300
🇬🇧 UK: $2,250
🇸🇪 Sweden: $2,200
🇩🇪 Germany: $2,150
🇧🇪 Belgium: $2,100
🇫🇷 France: $2,050
🇳🇱 Netherlands: $2,000
🇨🇦 Canada: $1,950
🇦🇹 Austria: $1,900
🇦🇺 Australia: $1,850
🇳🇿 New Zealand: $1,800
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Classes That SHOULD Be Mandatory in High School:
• Taxes
• Investing
• Real Estate
• Negotiating
• Basic coding
• Building credit
• Microsoft Excel
• Personal Finance
• Entrepreneurship
• Time Management
• Money Management
What would you add to the list?
• Taxes
• Investing
• Real Estate
• Negotiating
• Basic coding
• Building credit
• Microsoft Excel
• Personal Finance
• Entrepreneurship
• Time Management
• Money Management
What would you add to the list?
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Popular API Architecture Styles
1. gRPC: A high-performance, language-agnostic remote procedure call (RPC) framework for efficient communication between distributed systems, often used in microservices architectures.
2. SOAP: A protocol for exchanging structured information in the implementation of web services, known for its strict standards and XML-based message format.
3. GraphQL: A query language and runtime for APIs that allows clients to request only the data they need, reducing over-fetching and under-fetching of data.
4. Webhook: A mechanism for real-time communication where an application sends HTTP POST requests to a predefined URL to notify and trigger actions in another system.
5. REST: Representational State Transfer, an architectural style for designing networked applications, using standard HTTP methods (GET, POST, PUT, DELETE) to manipulate resources.
1. gRPC: A high-performance, language-agnostic remote procedure call (RPC) framework for efficient communication between distributed systems, often used in microservices architectures.
2. SOAP: A protocol for exchanging structured information in the implementation of web services, known for its strict standards and XML-based message format.
3. GraphQL: A query language and runtime for APIs that allows clients to request only the data they need, reducing over-fetching and under-fetching of data.
4. Webhook: A mechanism for real-time communication where an application sends HTTP POST requests to a predefined URL to notify and trigger actions in another system.
5. REST: Representational State Transfer, an architectural style for designing networked applications, using standard HTTP methods (GET, POST, PUT, DELETE) to manipulate resources.
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Many people still aren't fully utilizing the power of Telegram.
There are numerous channels on Telegram that can help you find the latest job and internship opportunities?
Here are some of my top channel recommendations to help you get started 👇👇
Latest Jobs & Internships: https://t.iss.one/getjobss
Jobs Preparation Resources:
https://t.iss.one/jobinterviewsprep
Web Development Jobs:
https://t.iss.one/webdeveloperjob
Data Science Jobs:
https://t.iss.one/datasciencej
Interview Tips:
https://t.iss.one/Interview_Jobs
Data Analyst Jobs:
https://t.iss.one/jobs_SQL
AI Jobs:
https://t.iss.one/AIjobz
Remote Jobs:
https://t.iss.one/jobs_us_uk
FAANG Jobs:
https://t.iss.one/FAANGJob
Software Developer Jobs: https://t.iss.one/internshiptojobs
If you found this helpful, don’t forget to like, share, and follow for more resources that can boost your career journey!
Let me know if you know any other useful telegram channel
ENJOY LEARNING👍👍
There are numerous channels on Telegram that can help you find the latest job and internship opportunities?
Here are some of my top channel recommendations to help you get started 👇👇
Latest Jobs & Internships: https://t.iss.one/getjobss
Jobs Preparation Resources:
https://t.iss.one/jobinterviewsprep
Web Development Jobs:
https://t.iss.one/webdeveloperjob
Data Science Jobs:
https://t.iss.one/datasciencej
Interview Tips:
https://t.iss.one/Interview_Jobs
Data Analyst Jobs:
https://t.iss.one/jobs_SQL
AI Jobs:
https://t.iss.one/AIjobz
Remote Jobs:
https://t.iss.one/jobs_us_uk
FAANG Jobs:
https://t.iss.one/FAANGJob
Software Developer Jobs: https://t.iss.one/internshiptojobs
If you found this helpful, don’t forget to like, share, and follow for more resources that can boost your career journey!
Let me know if you know any other useful telegram channel
ENJOY LEARNING👍👍
👍5
LLM Project Ideas for Resume
1️⃣ AI Image Captioning
Train an LLM to generate accurate, context-aware image captions for better accessibility and engagement.
2️⃣ Large Text Analysis
Use LLMs to summarize and extract key insights from massive text documents in various industries.
3️⃣ AI Code Generation
Automate code snippet creation from natural language descriptions to boost developer productivity.
4️⃣ Text Completion
Fine-tune LLMs for smarter text predictions in chatbots and content tools, enhancing user interactions.
1️⃣ AI Image Captioning
Train an LLM to generate accurate, context-aware image captions for better accessibility and engagement.
2️⃣ Large Text Analysis
Use LLMs to summarize and extract key insights from massive text documents in various industries.
3️⃣ AI Code Generation
Automate code snippet creation from natural language descriptions to boost developer productivity.
4️⃣ Text Completion
Fine-tune LLMs for smarter text predictions in chatbots and content tools, enhancing user interactions.
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