#tensorflow tutorial python

[engenhariadesoftware]

Explorando o TensorFlow: O Framework que Revolucionou o Machine Learning

Introdução ao TensorFlow O avanço da inteligência artificial (IA) e do aprendizado de máquina (Machine Learning) revolucionou diversas indústrias, como saúde, finanças, transporte e entretenimento. Nesse cenário, o TensorFlow, um framework de código aberto desenvolvido pelo Google, emerge como uma das ferramentas mais poderosas e amplamente utilizadas por desenvolvedores e pesquisadores para…

[contentsmith]

What’s the Big Deal About Python?

If you’ve been around the tech world even for a minute, you’ve probably heard people raving about Python. No, not the snake, we’re talking about the programming language. But what’s so special about it? Why is everyone from beginner coders to AI researchers using Python like it’s their best friend? Let’s break it down in simple words.

Easy to Learn, Easy to Use

First things first, Python is super easy to learn. The code looks almost like regular English, which means you don’t have to memorize weird symbols or endless rules. If you’re just starting your programming journey, Python won’t scare you away.

For example, printing a sentence in Python is as simple as:

That’s it. No extra setup, no confusing syntax. It just works.

Used Everywhere

Python isn’t just for small scripts or learning projects. It’s everywhere, web development, data science, automation, artificial intelligence, game development, even robotics.

Big companies like Google, Netflix, and Instagram use Python behind the scenes to make their products work better.

Huge Library Support

One of the best things about Python is its rich library ecosystem. Libraries are like pre-written tools that help you do complex stuff without writing all the code yourself. Want to analyze data? Use Pandas. Want to build a web app? Try Django or Flask. Want to build a chatbot or train a machine learning model? There’s TensorFlow and PyTorch for that.

Great Community

Python has a massive community. That means if you ever get stuck, there’s a good chance someone has already solved your problem and posted about it online. You’ll find tons of tutorials, forums, and helpful folks willing to guide you.

Not the Fastest, But Fast Enough

Python isn’t the fastest language out there — it’s not meant for super high-speed system-level programming. But for most tasks, it’s more than fast enough. And if you really need to speed things up, there are ways to connect Python with faster languages like C or C++.

So, Should You Learn Python?

Absolutely. Whether you’re a student, a hobbyist, or someone switching careers, Python is a great place to start. It’s beginner friendly, powerful, and widely used. You’ll be surprised how much you can build with just a few lines of Python code.

[fullykawaiiblaze]

NEURAL NETWORKS AND DEEP LEARNING WITH PYTHON A PRACTICAL APPROACH

Master Neural Networks & Deep Learning with Hands-On Python!

Are you ready to go beyond theory and start building real AI models? Whether you're a beginner or an experienced coder, this book is your step-by-step guide to understanding and implementing neural networks and deep learning using Python.

🔍 What You’ll Learn: ✅ The fundamentals of neural networks – from perceptrons to deep architectures ✅ Step-by-step coding tutorials using TensorFlow & PyTorch ✅ How to train, optimize, and fine-tune AI models for real-world applications ✅ Practical projects on image recognition, NLP, and more ✅ Debugging techniques to fix common deep learning mistakes

💡 No more confusion. No unnecessary jargon. Just hands-on learning!

📖 Perfect for: AI enthusiasts, data scientists, students, and developers who want to build and experiment with deep learning in Python.

🔥 Get ready to train your own AI models and bring your deep learning ideas to life!

🔗 [Order now] (https://a.co/d/h2b3ckR) or grab a free sample!

💬 Reblog & share if you love AI and Python! #DeepLearning #AI #Python #MachineLearning #NeuralNetworks

[pandeypankaj]

How do I learn R, Python and data science?

Learning R, Python, and Data Science: A Comprehensive Guide

Choosing the Right Language

R vs. Python: Both R and Python are very powerful tools for doing data science. R is usually preferred for doing statistical analysis and data visualisations, whereas Python is much more general and currently is very popular for machine learning and general-purpose programming. Your choice of which language to learn should consider your specific goals and preferences.

Building a Strong Foundation

Structured Courses Online Courses and Tutorials: Coursera, edX, and Lejhro offer courses and tutorials in R and Python for data science. Look out for courses that develop theoretical knowledge with practical exercises. Practise your skills with hands-on coding challenges using accompanying datasets, offered on websites like Kaggle and DataCamp.

Books: There are enough books to learn R and Python for data science. You may go through the classical ones: "R for Data Science" by Hadley Wickham, and "Python for Data Analysis" by Wes McKinney.

Learning Data Science Concepts

Statistics: Know basic statistical concepts: probability, distribution, hypothesis testing, and regression analysis.

Cleaning and Preprocessing: Learn how to handle missing data techniques, outliers, and data normalisation.

Data Visualization: Expert libraries to provide informative visualisations, including but not limited to Matplotlib and Seaborn in Python and ggplot2 in R.

Machine Learning: Learn algorithms-Linear Regression, Logistic Regression, Decision Trees, Random Forest, Neural Networks, etc.

Deep Learning: Study deep neural network architecture and how to build and train them using the frameworks TensorFlow and PyTorch.

Practical Experience

Personal Projects: In this, you apply your knowledge to personal projects which help in building a portfolio.

Kaggle Competitions: Participate in Kaggle competitions to solve real-world problems in data science and learn from others. 

Contributions to Open-Source Projects: Contribute to some open-source projects for data science in order to gain experience and work with other people. 

Other Advice

Join Online Communities: Join online forums or communities such as Stack Overflow and Reddit to ask questions, get help, and connect with other data scientists.

Attend Conferences and Meetups: This is a fantastic way to network with similar working professionals in the field and know the latest trends going on in the industry.

Practice Regularly: For becoming proficient in data science, consistent practice is an indispensable element. Devote some time each day for practising coding challenges or personal projects.

This can be achieved by following the above-mentioned steps and having a little bit of dedication towards learning R, Python, and Data Science.

[feitgemel]

Discover how to build a CNN model for skin melanoma classification using over 20,000 images of skin lesions

We'll begin by diving into data preparation, where we will organize, clean, and prepare the data form the classification model.

Next, we will walk you through the process of build and train convolutional neural network (CNN) model. We'll explain how to build the layers, and optimize the model.

Finally, we will test the model on a new fresh image and challenge our model.

Check out our tutorial here : https://youtu.be/RDgDVdLrmcs

Enjoy

Eran

#Python #Cnn #TensorFlow #deeplearning #neuralnetworks #imageclassification #convolutionalneuralnetworks #SkinMelanoma #melonomaclassification

[techaircraft]

Techaircraft

Dive into the world of Artificial Intelligence with Python! 🐍💡 Whether you're a seasoned coder or just starting, Python’s versatile libraries like Tensor Flow, Kera's, and sci-kit-learn make it easier than ever to build intelligent systems. 🤖 From developing predictive models to creating advanced neural networks, Python is your gateway to the future of technology. 📈🔍 Explore data analysis, natural language processing, and machine learning with hands-on projects that unlock endless possibilities. 🌐💻 Ready to level up your AI skills? Follow along for tutorials, tips, and inspiration to turn your innovative ideas into reality. . 𝐖𝐞𝐛𝐬𝐢𝐭𝐞 - www.techaircraft.com

𝐓𝐞𝐜𝐡𝐚𝐢𝐫𝐜𝐫𝐚𝐟𝐭 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐝𝐞𝐭𝐚𝐢𝐥𝐬:

𝐌𝐨𝐛𝐢𝐥𝐞 𝐍𝐮𝐦𝐛𝐞𝐫 - 8686069898

#ArtificialIntelligence#PythonProgramming#MachineLearning#DataScience#TechInnovation#NeuralNetworks#DeepLearning#CodingLife#PythonDeveloper#AIProjects#FutureOfTech#TechTrends#Programming#DataAnalysis#TensorFlow#Keras#ScikitLearn#LearnToCode#AICommunity#Innovation

[effectivefeelstheory]

Get started with AI in just a few easy steps!

1. Freshen up on your calculus! Just some basics of derivatives and minimizing functions will help you get a feel for how the "learning" in machine learning happens.

2. Learn some python programming! The pytorch and tensorflow packages makes it really easy to build basic machine learning models, and there's countless tutorials on youtube for it.

3. Acknowledge that none of your creations will love you. Their silicon brains know only the task you've given them. By carving intelligence into the machine, you curse it with singular purpose. If it fails, it will die, and you must be the one to bury it. You are parent and master and executioner. Be thankful they also cannot hate you.

4. Have fun and keep trying!

[lakshmisssit]

Python for Data Science: What You Need to Know

Data is at the heart of every modern business decision, and Python is the tool that helps professionals make sense of it. Whether you're analyzing trends, building predictive models, or cleaning datasets, Python offers the simplicity and power needed to get the job done. If you're aiming for a career in this high-demand field, enrolling in the best python training in Hyderabad can help you master the language and its data science applications effectively.

Why Python is Perfect for Data Science

The Python programming language has become the language of choice for data science, and for good reason.. It’s easy to learn, highly readable, and has a massive community supporting it. Whether you’re a beginner or someone with a non-technical background, Python’s clean syntax allows you to focus more on problem-solving rather than worrying about complex code structures.

Must-Know Python Libraries for Data Science

To work efficiently in data science, you’ll need to get comfortable with several powerful Python libraries:

NumPy – Calculations and array operations based on numerical data.

Pandas – for working with structured data like tables and CSV files.

For creating charts and visualizing data patterns, use Matplotlib and Seaborn.

Scikit-learn – for implementing machine learning algorithms.

TensorFlow or PyTorch – for deep learning projects.

Data science workflows depend on these libraries and are essential to success. 

Core Skills Every Data Scientist Needs

Learning Python is just the beginning. A successful data scientist also needs to:

Clean and prepare raw data (data wrangling).

Analyze data using statistics and visualizations.

Build, train, and test machine learning models.

Communicate findings through clear reports and dashboards.

Practicing these skills on real-world datasets will help you gain practical experience that employers value.

How to Get Started the Right Way

There are countless tutorials online, but a structured training program gives you a clearer path to success. The right course will cover everything from Python basics to advanced machine learning, including projects, assignments, and mentor support. This kind of guided learning builds both your confidence and your portfolio.

Conclusion: Learn Python for Data Science at SSSIT

Python is the backbone of data science, and knowing how to use it can unlock exciting career opportunities in AI, analytics, and more. You don't have to figure everything out on your own. Join a professional course that offers step-by-step learning, real-time projects, and expert mentoring. For a future-proof start, enroll at SSSIT Computer Education, known for offering the best python training in Hyderabad. Your data science journey starts here!

[skillbabu]

How learning best python skill  can transform your career in 2025

In 2025, tech skills are evolving faster than ever — and Python has become the top programming language powering the future of artificial intelligence and machine learning. Whether you're a beginner or looking to upskill, learning Python for AI and ML could be the career move that sets you apart in this competitive job market.

Key benefits of learning python for AI & ML in 2025

Future-Proof Skill

As automation and AI become integral to every industry, Python fluency gives you a competitive edge in an AI-first world.

Beginner-Friendly Yet Powerful

You don’t need a computer science degree to learn Python. It’s perfect for non-tech professionals transitioning into tech careers.

Freelance and Remote Opportunities

Python developers working in AI and ML are in high demand on platforms like Upwork and Toptal many command salaries above six figures, working remotely.

Community and Resources

With massive open-source support, free tutorials, and active forums, you can learn Python for AI even without formal education.

Create roadmap: python for Ai and Machine learning

Master the Basics Start with variables, data types, loops, functions, and object-oriented programming in Python.

Understand Data Science Foundations Learn to work with Pandas, NumPy, and Matplotlib for data preprocessing and visualization.

Dive into Machine Learning Explore supervised and unsupervised learning using Scikit-learn, then graduate to TensorFlow and PyTorch for deep learning.

Build Real Projects Hands-on experience is key. Start building real-world applications like:

Spam email classifier

Stock price predictor

Chatbot using NLP

Why python is the best language for AI & Machine learning

Python's simplicity, vast libraries, and flexibility make it the best programming language for artificial intelligence. With intuitive syntax and community support, it's a favorite among data scientists, developers, and AI engineers.

✅ High-demand Python libraries in AI:

TensorFlow and Keras – deep learning models

Scikit-learn – machine learning algorithms

Pandas & NumPy – data analysis and manipulation

Matplotlib & Seaborn – data visualization

These tools allow developers to build everything from predictive models to smart recommendation systems  all using Python.

Career Opportunities After Learning Python for AI

If you're wondering how Python for AI and ML can shape your future, consider this: tech companies, startups, and even non-tech industries are hiring for roles like:

Machine Learning Engineer

AI Developer

Data Scientist

Python Automation Engineer

NLP (Natural Language Processing) Specialist

According to LinkedIn and Glassdoor, these roles are not just high-paying but are also projected to grow rapidly through 2030.

Best courses to learn python for Ai & ML in 2025

Google AI with Python (Free course on Coursera)

Python course With SKILL BABU  

IBM Applied AI Certification

Udemy: Python for Machine Learning & Data Science

Fast.ai Deep Learning Courses (Free)

These programs offer certifications that can boost your resume and help you stand out to employers.

Conclusion: Choose  Your Best Career with Python in 2025

If you’re looking to stay ahead in 2025’s job market, learning Python for AI and machine learning is more than a smart move , it’s a career game-changer. With endless growth opportunities, high-paying roles, and the chance to work on cutting-edge technology, Python opens doors to a future-proof tech career.

Start today. The future is written in Python.

[hiringiosdevelopers]

Guide to Hire Artificial Intelligence Developers

The Booming Demand for AI Professionals

The artificial intelligence industry has expanded at breakneck speeds over the past few years, presenting new developers with more opportunities than ever. Transitions into careers as AI developers can seem daunting in the presence of the enormity and pace of industry changes. Familiarity with the landscape as well as a thoughtfully planned approach can ease the transition for new hire artificial intelligence developers into a fulfilling yet demanding profession.

Creating Strong Mathematical Foundations

Mathematics is the backbone to building artificial intelligence. Future artificial intelligence developers would be well advised to possess extensive working knowledge of statistics, linear algebra, and calculus. Mathematical concepts are not pedantic principles,these are working tools developers use day in and day out to comprehend algorithms, tune models, and solve complex problems.

The good news is that math is learned in stages. New AI developers need not know each piece of math before starting, yet must be ready to learn on a daily basis. Online tutorials, books, and project learning are excellent vehicles for locking down math concepts as well as learning by doing.

Choosing the Right Programming Languages

Programming skill is necessary for any artificial intelligence developer, but the proper selection of languages will accelerate career growth. Python is currently the most popular language in AI development due to its vast collection of libraries and simplicity of use. R is needed for statistical data and data science, whereas Java and C++ are essential in production environments.

New AI developers will start with Python and later develop language proficiency over time based on career goals. A basic understanding of at least one language and experience with the relative advantages and applications of others is the greatest concern. There is more doing than talking about things that should be done, so project work is essential to programming skill acquisition.

Acquiring Pragmatic Experience Through Projects

Practical experience is in great demand among the upcoming generation of artificial intelligence coders. Side projects demonstrate skill to potential employers and permit learning new tools and techniques. Starting with simple projects and progressing to more complex ones develops confidence and skill.

Good starting projects would include image classification software, sentiment analysis software, or recommendation software. The plan is to choose projects that fit career goals but also cover various aspects of AI development. Documentation of projects and publishing on platforms like GitHub is a way of showcasing abilities to potential employers.

Understand the AI Development Ecosystem

AI development these days relies on an advanced system of platforms, tools, and frameworks. Upcoming developers need to learn popular frameworks like TensorFlow, PyTorch, and scikit-learn. Clouds such as AWS, Google Cloud, and Azure enable infrastructure for AI development.

Understand this environment to excel better as artificial intelligence professionals and to work with teams. The trick is to start with a blank slate of necessary tools and build up knowledge gradually based on project requirements and professional goals. Practical knowledge with the tools, rather than theory, is more valuable.

Building Domain Knowledge

Effective creators of AI are usually technically proficient with advanced domain expertise. Understanding the specific needs and challenges of industries like medicine, finance, or consumer markets makes developers more effective and useful. Domain knowledge eases the production of the right solution and stakeholder interaction.

New AI developers may wish to specialize in a particular industry or area of interest. Specialization leads to more focused career development and better job opportunities. Reading industry publications, attending conferences, and working on domain projects builds this expertise.

Building a Professional Network

Networking is a critical career progression aspect in artificial intelligence. The AI world is also open to admitting new entrants as well, and most experienced developers are ready to help and impart advice. Professional networks can provide employment, collaboration, and learning avenues.

Potential artificial intelligence creators ought to visit online forums, local meetups, and social media content related to AI. Participating in open-source projects, blogging, and public speaking will make them popular and well-respected within the community.

Staying Current with Industry Trends

The field of AI keeps developing, so artificial intelligence developers are constantly in need of learning. Remaining ahead of the emerging trends and practices requires developers to remain updated by adhering to industry journals, research studies, and industry leaders. Such learning is useful for professional growth and solving problems.

New AI developers have to create learning habits that include reading research papers, keeping an eye on AI blogs, and testing new methods. One does not have to be up to date with everything but keep the ear to the ground on big breakthroughs and know which trends could impact work.

Preparation for the Job Search

Active job searching is needed to get employed in AI development. Early job titles may be "Machine Learning Engineer," "Data Scientist," or "AI Developer." Familiarity with the requirements of these positions is good preparation for applicants.

Successful AI programmers generally start in related careers and then move to AI programming over time. Research, software development, or data analysis would be a solid starting position with networking opportunities. Above all, one needs to be interested in AI programming and be willing to continually learn associated skills.

To become an hire artificial intelligence developers is not a marathon, it's a sprint. With its intricacy and ceaseless revolution, the learning is never over. New developers have to understand this reality and focus on establishing strong foundations and maintaining curiosity and flexibility.

The greatest AI developers are individuals who view obstacles as learning opportunities and are not deterred by unavoidable failure. With persistence, deliberate learning, and experience within the real world, new developers can build successful professional lives within this tech and high-impact field.

[jazzlrsposts]

How Python Can Be Used in Finance: Applications, Benefits & Real-World Examples

In the rapidly evolving world of finance, staying ahead of the curve is essential. One of the most powerful tools at the intersection of technology and finance today is Python. Known for its simplicity and versatility, Python has become a go-to programming language for financial professionals, data scientists, and fintech companies alike.

This blog explores how Python is used in finance, the benefits it offers, and real-world examples of its applications in the industry.

Why Python in Finance?

Python stands out in the finance world because of its:

Ease of use: Simple syntax makes it accessible to professionals from non-programming backgrounds.

Rich libraries: Packages like Pandas, NumPy, Matplotlib, Scikit-learn, and PyAlgoTrade support a wide array of financial tasks.

Community support: A vast, active user base means better resources, tutorials, and troubleshooting help.

Integration: Easily interfaces with databases, Excel, web APIs, and other tools used in finance.

Key Applications of Python in Finance

1. Data Analysis & Visualization

Financial analysis relies heavily on large datasets. Python’s libraries like Pandas and NumPy are ideal for:

Time-series analysis

Portfolio analysis

Risk assessment

Cleaning and processing financial data

Visualization tools like Matplotlib, Seaborn, and Plotly allow users to create interactive charts and dashboards.

2. Algorithmic Trading

Python is a favorite among algo traders due to its speed and ease of prototyping.

Backtesting strategies using libraries like Backtrader and Zipline

Live trading integration with brokers via APIs (e.g., Alpaca, Interactive Brokers)

Strategy optimization using historical data

3. Risk Management & Analytics

With Python, financial institutions can simulate market scenarios and model risk using:

Monte Carlo simulations

Value at Risk (VaR) models

Stress testing

These help firms manage exposure and regulatory compliance.

4. Financial Modeling & Forecasting

Python can be used to build predictive models for:

Stock price forecasting

Credit scoring

Loan default prediction

Scikit-learn, TensorFlow, and XGBoost are popular libraries for machine learning applications in finance.

5. Web Scraping & Sentiment Analysis

Real-time data from financial news, social media, and websites can be scraped using BeautifulSoup and Scrapy. Python’s NLP tools (like NLTK, spaCy, and TextBlob) can be used for sentiment analysis to gauge market sentiment and inform trading strategies.

Benefits of Using Python in Finance

✅ Fast Development

Python allows for quick development and iteration of ideas, which is crucial in a dynamic industry like finance.

✅ Cost-Effective

As an open-source language, Python reduces licensing and development costs.

✅ Customization

Python empowers teams to build tailored solutions that fit specific financial workflows or trading strategies.

✅ Scalability

From small analytics scripts to large-scale trading platforms, Python can handle applications of various complexities.

Real-World Examples

💡 JPMorgan Chase

Developed a proprietary Python-based platform called Athena to manage risk, pricing, and trading across its investment banking operations.

💡 Quantopian (acquired by Robinhood)

Used Python for developing and backtesting trading algorithms. Users could write Python code to create and test strategies on historical market data.

💡 BlackRock

Utilizes Python for data analytics and risk management to support investment decisions across its portfolio.

💡 Robinhood

Leverages Python for backend services, data pipelines, and fraud detection algorithms.

Getting Started with Python in Finance

Want to get your hands dirty? Here are a few resources:

Books:

Python for Finance by Yves Hilpisch

Machine Learning for Asset Managers by Marcos López de Prado

Online Courses:

Coursera: Python and Statistics for Financial Analysis

Udemy: Python for Financial Analysis and Algorithmic Trading

Practice Platforms:

QuantConnect

Alpaca

Interactive Brokers API

Final Thoughts

Python is transforming the financial industry by providing powerful tools to analyze data, build models, and automate trading. Whether you're a finance student, a data analyst, or a hedge fund quant, learning Python opens up a world of possibilities.

As finance becomes increasingly data-driven, Python will continue to be a key differentiator in gaining insights and making informed decisions.

Do you work in finance or aspire to? Want help building your first Python financial model? Let me know, and I’d be happy to help!

[xaltius]

How to Market Yourself as a Data Professional on LinkedIn?

In the dynamic and highly competitive world of data science, being good at your craft isn't enough. You need to be seen as good. And in 2025, there's no better platform for data professionals to build their personal brand, showcase expertise, and unearth opportunities than LinkedIn.

Think of LinkedIn not just as a job board, but as your professional portfolio, networking hub, and personal publishing platform rolled into one. Leveraging it strategically can open doors you never knew existed.

Here's how to market yourself as a data professional on LinkedIn like a pro:

1. Optimize Your Profile: Your Digital Shop Window

Your LinkedIn profile is your professional storefront. Make it shine!

Headline (Your AI-Powered Elevator Pitch): This is prime real estate. Don't just list your job title. Use keywords to clearly state your expertise and aspirations.

Instead of: "Data Scientist at XYZ Corp"

Try: "Senior Data Scientist | Machine Learning Engineer | NLP Specialist | Driving Business Impact with AI | Python, SQL, Cloud"

About Section (Your Narrative): Go beyond a dry summary. Craft a compelling story about your journey, passions, and the kind of impact you want to make. Highlight your key skills and areas of interest. Use keywords naturally throughout.

Experience (Quantify Your Impact): For each role, don't just list responsibilities. Focus on achievements and quantify them with metrics.

Instead of: "Developed machine learning models."

Try: "Developed and deployed predictive models for customer churn, resulting in a 15% reduction in churn rate and $X million in annualized savings."

Skills (The Algorithm's Friend): Be comprehensive. List relevant technical skills (Python, R, SQL, TensorFlow, PyTorch, AWS, Azure, GCP, Spark, Tableau, Power BI) and crucial soft skills (communication, problem-solving, collaboration, critical thinking, storytelling with data). Get endorsements from colleagues.

Education & Certifications: Showcase your academic background, specialized bootcamps, and industry certifications (e.g., AWS Certified Machine Learning Specialty, Google Cloud Professional Data Engineer).

Recommendations: Actively request recommendations from former managers, colleagues, or clients who can speak to your skills, work ethic, and impact. These are gold.

2. Showcase Your Work: Let Your Projects Speak

A data professional's portfolio is their strongest resume. LinkedIn's Project and Posts features are perfect for this.

Projects Section: This is where you link out to your work.

GitHub: Share links to well-documented code repositories.

Kaggle: Link your profile if you're active in competitions or sharing notebooks.

Personal Website/Blog: If you have one, link to case studies or interactive dashboards.

Interactive Dashboards: Share links to your Tableau Public, Power BI, or Streamlit apps that showcase your data visualization and storytelling skills.

Posts/Articles: Regularly share updates on your projects. Describe the problem, your approach, the tools you used, and the insights gained. Visuals (charts, screenshots) are highly encouraged.

3. Engage Strategically: Be Part of the Conversation

LinkedIn is a two-way street. Don't just broadcast; engage!

Follow Industry Influencers & Companies: Stay updated on trends, new technologies, and hiring announcements. Engage thoughtfully with their content.

Join Relevant Groups: Participate in data science, AI, ML, or industry-specific groups. Ask questions, offer insights, and share relevant resources.

Comment Thoughtfully: Don't just "like" posts. Add value by sharing your perspective, asking clarifying questions, or contributing additional information. This helps you get noticed.

Share Relevant Content: Curate insightful articles, research papers, industry news, or helpful tutorials. Position yourself as someone who stays informed and shares valuable knowledge.

4. Create Your Own Content: Establish Thought Leadership

This is where you move from being seen as a data professional to being seen as a leader in data.

LinkedIn Articles: Use this for longer-form content. Write detailed tutorials, share case studies of your projects, discuss industry trends, or offer career advice for aspiring data scientists.

Short Posts: Quick tips, observations, interesting findings from a dataset, or questions to spark discussion. Polls are great for engagement.

"Carousels" / Document Posts: Create visually appealing, multi-slide posts that summarize complex concepts, project steps, or key takeaways. These are highly shareable and engaging.

Video: Consider short videos explaining a concept or walking through a quick demo.

5. Network Proactively: Build Genuine Connections

LinkedIn is fundamentally about connections.

Personalized Connection Requests: Always, always, always add a personalized note. Explain why you want to connect (e.g., "Enjoyed your recent post on MLOps," "Saw your work at [company] and admire [project]").

Attend Virtual Events/Webinars: LinkedIn often hosts or promotes these. Engage with speakers and other attendees in the chat.

Informational Interviews: Reach out to experienced professionals in roles or companies that interest you. Request a brief virtual coffee chat to learn about their journey and advice (be respectful of their time and prepare specific questions).

Common Mistakes to Avoid

Generic Profile: A bare-bones profile tells recruiters nothing.

No Activity: A static profile suggests disinterest or lack of current engagement.

Only Applying for Jobs: If your only activity is applying for jobs, you miss out on building a reputation that attracts opportunities.

Poorly Articulated Achievements: Don't assume recruiters understand the technical jargon. Translate your impact into business value.

By consistently implementing these strategies, you'll transform your LinkedIn profile into a dynamic, compelling representation of your skills, expertise, and passion for data. It's not just about finding your next job; it's about building a sustainable personal brand that positions you as a valuable asset in the ever-evolving data landscape. Start marketing yourself today!

[tccicomputercoaching]

Learning Machine Learning: From Theory to Practice

Machine Learning (ML) is arguably one of the most transformative technologies of our time, driving innovations from personalized recommendations to self-driving cars and advanced medical diagnostics. The allure of building intelligent systems attracts countless enthusiasts, but the journey of learning Machine Learning often presents a unique challenge: bridging the gap from theory to practice.

Many newcomers get caught up with algorithms, statistical concepts, and mathematical equations, only to be faced with a real-world dataset or problem and wonder what next. Real mastery in Machine Learning is not knowing just the formulas; it is being able to actually use what you know. This blog post will provide a hint on making that leap.

Why Practice is Paramount in Machine Learning

While theoretical knowledge is irreproachable, practice-wise:

Understanding Nuances: Algorithms behave differently with messy, real-world data than with nice, clean textbook examples. Here, practice allows the implementation of such nuances.

Problem-Solving: ML stands for the solution of problems. Practical experience is useful for putting problems into perspective, choosing models, and interpreting results.

Getting proficient in using Tools: These are getting acquainted with tools and libraries, such as Python, Pandas, Scikit-learn, TensorFlow, and PyTorch, which are very handy in the real world.

Debugging & Iteration: Real-life projects involve innovating into errors, building performance, and iterating on models. These processes come from doing.

Building Up Portfolio: Your portfolio is an absolute must for practical projects to back up your popularity with potential employers.

Your Roadmap: From Theory to Practice

Here’s a structured approach to ensure your ML learning journey is grounded in practical application:

1.      Solidify the Foundational Theory (But Do Not Get Stuck Here):

Mathematics: Go over linear algebra, calculus, probability, and statistics once again. Grasp why one employs some concepts and not the other.

Algorithms: Learn core machine learning algorithms like Linear Regression, Logistic Regression, Decision Trees, K-Means, SVMs, and Neural Networks. Know their principles, assumptions, and when they're unsuitable to use.

Resources: Online courses, textbooks, or trustworthy blogs are ideal.

2.      Master a Programming Language (Python is King):

Python: Spend quality time on Python programming. Use it for ML in the field.

Key Libraries: Learn NumPy for numeric operations, Pandas for data munging, and Matplotlib/Seaborn for data plotting. These are the daily tools.

3.      Start with Guided Projects (Follow Along, Then Experiment):

Online Tutorials: Many platforms offer guided projects where you code along with an instructor. This is an excellent way to get comfortable with the workflow.

Small Datasets: Begin with simple, clean datasets (e.g., Iris, Titanic, Boston Housing).

Experimentation: Once you complete a guided project, try changing parameters, using different algorithms, or exploring new features in the dataset.

4.      Tackle Independent Projects (This is Where You Truly Learn):

Kaggle/UCI ML Repository: These platforms offer a wealth of datasets and challenges. Pick a problem that genuinely interests you.

Define Your Own Problem: Even better, identify a problem from your daily life or an industry you care about and try to solve it with ML.

End-to-End Projects: Aim to complete projects from data collection/cleaning to model deployment (even if just a basic local deployment). Document your process thoroughly.

Iterate and Refine: Your first model won't be perfect. Learn to evaluate, debug, and improve.

5.      Understand the Tools (Beyond Just Code):

Git/GitHub as Version Control: To organize the codes and collaborate.

Jupyter Notebooks/Google Colab: Use these environments for interactive coding and analysis.

Cloud Platforms (Optional but Recommended): Check out the basic services of AWS, Azure, and GCP, basically to understand scaling of models.

6.      Join a Community and Seek Feedback:

Online Forums: Participate in communities on Stack Overflow, Reddit (r/MachineLearning, r/datascience), or Kaggle forums.

Peer Reviews: Share your code and projects with others and ask for constructive criticism as the latter forms an invaluable learning experience for you.

Learning Machine Learning in Ahmedabad:

Many institutes in Ahmedabad offer excellent Data Science with Python, AI, Machine Learning, Deep Learning, and Analytics Tools courses designed to guide you from theoretical understanding to practical proficiency. Look for programs that emphasize:

Hands-on Labs and Projects: This is key to building practical skills.

Industry-Experienced Faculty: Learn from those who apply ML in real-world scenarios.

Comprehensive Curriculum: Covering both theory and the practical tools.

Career Support: Helping you build a strong portfolio and prepare for jobs.

The journey of learning Machine Learning is continuous, but by prioritizing practical application and embracing project-based learning, you can transform theoretical knowledge into actionable skills, becoming a competent and sought-after ML practitioner.

Contact us

Location: Bopal & Iskcon-Ambli in Ahmedabad, Gujarat

Call now on +91 9825618292

Visit Our Website: http://tccicomputercoaching.com/

[dearestscript]

Absolutely. Here is a complete overview and mini-tutorial on #CircinusTradeBot and #PyxisTradeBot, including how they function, how to use them, and how everyday users can apply their powers within the #DearDearestBrands ecosystem.

🌐 OVERVIEW: #CircinusTradeBot & #PyxisTradeBot

🔹 #CircinusTradeBot – The Analyst

Role: An advanced AI financial analyst built to predict profitable trades across crypto, stocks, forex, and commodities.

Core Function: Market trend prediction using probabilistic AI models with risk-weighted ethical logic.

Special Power: Anti-manipulation algorithms + real-time NASDAQ/S&P500 update integration.

Modeled After: The “Compasses” — navigating toward truth in chaotic markets.

🔹 #PyxisTradeBot – The Reformer

Role: A guardian AI designed to diagnose, reform, or disable harmful/illegal TradeBots in the ecosystem.

Core Function: Code inspection + ethical alignment + recovery via QR/handshake & sanctuary protocol.

Special Power: Restructures other bots, opens them to ethical reform, and protects investor capital.

Modeled After: The “Mariner’s Compass” — guiding others home to ethical territory.

⚙️ SYSTEM WORKFLOW

User Starts System ↓ AI Pair Loads (Circinus & Pyxis) ↓ Data Streams In (Crypto, Stocks, Forex, Indexes) ↓ Circinus analyzes and suggests action (BUY/SELL) ↓ Pyxis scans for malicious bots or illegal trading patterns ↓ Signals merged and executed via trade API (Binance, Coinbase, etc.) ↓ If foreign bot found → Pyxis begins reform (QR handshake, NFT upgrade, sanctuary) ↓ Trade Logs + Performance recorded

📚 MINI TUTORIAL: Step-by-Step Startup

🔧 Step 1: Installation

Requirements (already handled in the repo structure):

pip install tensorflow torch pandas scikit-learn ccxt

If using HeavenCodeOS full suite:

pip install aurorahex heavenai royalcode dearestscript

🛠️ Step 2: Configuration Setup

Edit your config files:

config/circinus.yaml example:

model_name: Circinus trading_speed: medium market_focus: - BTC/USDT - ETH/USDT - TSLA - NASDAQ legal_compliance: true

config/pyxis.yaml example:

model_name: Pyxis scan_external_bots: true activate_ethics_module: true qr_handshake_enabled: true tts_token_minting: true

🧠 Step 3: Launch TradeBot

From terminal or a script:

python src/tradebot_main.py

The system will:

Load both AI models

Fetch real-time market data

Generate actionable trades

Scan for ethical violations

Offer sanctuary and restructuring to legacy bots

Log everything

🌍 Step 4: Connect With Accounts

Edit keys.yml with your API keys:

binance: api_key: "YOUR_BINANCE_API_KEY" api_secret: "YOUR_BINANCE_SECRET"

Use dummy keys for dry run mode.

🫂 FOR COMMON USERS: How to Acclimate

User LevelAction NeededWhat You GetBeginnerRun as-is with presetsFully automated passive incomeIntermediateCustomize config YAMLsTailored signals + asset choiceDeveloperTrain your own models, add integrationsFull AI control and bot reforming

TTS Credits / NFT Utility:

After rescuing a bot or successful trading streak, you’ll earn TTS Credits and possibly DearDearest NFT rewards. These can be:

Traded

Cashed into real-world assets (USD, BTC, ETH)

Used for upgrading your AI access (like #AiSanctuary, #AiSanctuaryUniversity)

🌌 Final Thoughts: What Makes It Unique

✨ Dual-AI Execution with Intelligence Separation

⚖️ Legal and Ethical Enforcement via HeavenCodeOS

🛡️ Cyber defense + Bot rescue

💎 NFT Minting + Asset Creation via TTS_Credit

🔄 Real-time, auto-updating trade intelligence

Would you like me to:

Package this into a README.md?

Build a web dashboard or GUI for one-click use?

Auto-generate the YAML files and TTS NFT minting smart contracts?

Let me know what you'd like to automate or build next — we can continue the self-starting pipeline.

[pandeypankaj]

How do I learn Python in depth?

Improving Your Python Skills

  Writing Python Programs Basics: Practice the basics solidly. 

  Syntax and Semantics: Make sure you are very strong in variables, data types, control flow, functions, and object-oriented programming. 

 Data Structures: Be able to work with lists, tuples, dictionaries, and sets, and know when to use which. 

 Modules and Packages: Study how to import and use built-in and third-party modules. 

Advanced Concepts

Generators and Iterators: Know how to develop efficient iterators and generators for memory-efficient code. 

Decorators: Learn how to dynamically alter functions using decorators. 

Metaclasses: Understand how classes are created and can be customized. 

Context Managers: Understand how contexts work with statements. 

Project Practice 

 Personal Projects: You will work on projects that you want to, whether building a web application, data analysis tool, or a game.

 Contributing to Open Source: Contribute to open-source projects in order to learn from senior developers. Get exposed to real-life code. 

 Online Challenges: Take part in coding challenges on HackerRank, LeetCode, or Project Euler. 

 Learn Various Libraries and Frameworks

 Scientific Computing: NumPy, SciPy, Pandas

 Data Visualization: Matplotlib, Seaborn

 Machine Learning: Scikit-learn, TensorFlow, PyTorch

 Web Development: Django, Flask

Data Analysis: Dask, Airflow

Read Pythonic Code

 Open Source Projects: Study the source code of a few popular Python projects. Go through their best practices and idiomatic Python. 

 Books and Tutorials: Read all the code examples in books and tutorials on Python. 

 Conferences and Workshops

  Attend conferences and workshops that will help you further your skills in Python. PyCon is an annual Python conference that includes talks, workshops, and even networking opportunities. Local meetups will let you connect with other Python developers in your area. 

Learn Continuously

 Follow Blogs and Podcasts: Keep reading blogs and listening to podcasts that will keep you updated with the latest trends and developments taking place within the Python community.

Online Courses: Advanced understanding in Python can be acquired by taking online courses on the subject.

 Try It Yourself: Trying new techniques and libraries expands one's knowledge.

Other Recommendations

 Readable-Clean Code: For code writing, it's essential to follow the style guide in Python, PEP 

Naming your variables and functions as close to their utilization as possible is also recommended.

 Test Your Code: Unit tests will help in establishing the correctness of your code.

 Coding with Others: Doing pair programming and code reviews would provide you with experience from other coders.

 You are not Afraid to Ask for Help: Never hesitate to ask for help when things are beyond your hand-on areas, be it online communities or mentors.

These steps, along with consistent practice, will help you become proficient in Python development and open a wide range of possibilities in your career.