IA Inteligencia Artificial – Machine Learning.

IA Inteligencia Artificial – Data Science Learning

Inteligencia Artificial – Curso de IA para contadores

I Studied Data Job Trends for 24 Hours to Save Your Career! (ft Datalore): Según el video de abajo, Machine Learning es la carrea con más demanda, inclusive más que Data Science Learning.

Top 3 No-Code Machine Learning Tools – in 2023 | AI Tools for Data Science

Machine Learning in 2024 – Beginner’s Course.

 

https://www.coursera.org/specializations/machine-learning-introduction?irclickid=341QJRT5nxyKRbnSyL1EEyV6UkCXOqyH-RKqSg0&irgwc=1&utm_medium=partners&utm_source=impact&utm_campaign=3317930&utm_content=b2c

 

https://www.youtube.com/results?search_query=Machine+Learning+Course+no+code

https://www.udemy.com/course/10-code-less-days-of-artificial-intelligence/?utm_source=adwords&utm_medium=udemyads&utm_campaign=Search_DSA_Beta_Prof_la.EN_cc.ROW-English&campaigntype=Search&portfolio=ROW-English&language=EN&product=Course&test=&audience=DSA&topic=&priority=Beta&utm_content=deal4584&utm_term=_._ag_162511579404_._ad_696197165418_._kw__._de_c_._dm__._pl__._ti_dsa-1677053911088_._li_9069516_._pd__._&matchtype=&gad_source=2&gclid=CjwKCAiApY-7BhBjEiwAQMrrEZL9oEQmQJ30zo66tbpnN1hWU8wRPGVX_zvUllCenFfv1E9kwu2dKRoCMhQQAvD_BwE&couponCode=2021PM25

https://www.udemy.com/course/machinelearning/?utm_source=adwords&utm_medium=udemyads&utm_campaign=Search_Keyword_Beta_Prof_la.EN_cc.ROW-English&campaigntype=Search&portfolio=ROW-English&language=EN&product=Course&test=&audience=Keyword&topic=Machine_Learning&priority=Beta&utm_content=deal4584&utm_term=_._ag_166536831328_._ad_696233842753_._kw_machine+learning+course_._de_c_._dm__._pl__._ti_kwd-338471928646_._li_9069516_._pd__._&matchtype=b&gad_source=2&gclid=CjwKCAiApY-7BhBjEiwAQMrrEcIKhc6YrxUTQXEg7w3HriULPKKeIt–Q3soBpV46WUhhfVRokBT5xoCu88QAvD_BwE&couponCode=ST21MT121624

Your learning roadmap is well-structured and progressive! Here’s a refined version of your roadmap with actionable steps for each phase:

1. Learn Make

  • Goal: Master the Make platform for automation and integration.
  • Action Steps:
    • Complete Make tutorials and documentation.
    • Explore Make’s HTTP modules, webhooks, and JSON parsing.
    • Build basic automation workflows, such as integrating email and task management tools.
    • Experiment with AI integrations in Make, such as ChatGPT.
    • Create increasingly complex workflows to solidify your understanding.

2. Chatbot Generation

  • Goal: Understand and create advanced chatbots for automation and customer interaction.
  • Action Steps:
    • Learn the basics of chatbot design (e.g., intents, entities, context).
    • Explore platforms like Dialogflow, Rasa, or ChatGPT API for chatbot creation.
    • Practice creating simple Q&A bots and gradually introduce dynamic features like API integrations.
    • Study conversational design principles to make bots user-friendly.
    • Build a chatbot project integrated into a Make workflow.

3. Upgrade Excel Skills

  • Goal: Become an advanced Excel user for data analysis and reporting.
  • Action Steps:
    • Master intermediate functions (e.g., VLOOKUP, INDEX/MATCH, and pivot tables).
    • Learn advanced Excel features like Power Query and VBA for automation.
    • Practice creating dashboards with Excel charts and conditional formatting.
    • Study use cases of Excel in business analytics and integrate them into your work.

4. Learn Power BI

  • Goal: Use Power BI for advanced data visualization and reporting.
  • Action Steps:
    • Complete beginner and intermediate Power BI tutorials.
    • Practice connecting Power BI to different data sources (Excel, SQL, APIs).
    • Learn how to create interactive dashboards and reports.
    • Understand DAX (Data Analysis Expressions) for custom calculations.
    • Create projects analyzing real-world data to showcase your skills.

5. Become an AI Engineer

  • Goal: Gain expertise in AI to implement and optimize machine learning solutions.
  • Action Steps:
    • Understand AI basics: machine learning vs. deep learning, neural networks, etc.
    • Learn popular AI tools and frameworks like TensorFlow, PyTorch, or Hugging Face.
    • Study Python libraries for AI, such as NumPy, pandas, and scikit-learn.
    • Build small AI projects like recommendation systems or image classification.
    • Deep dive into AI ethics and responsible AI practices.

6. Learn Python

  • Goal: Build a strong foundation in Python for automation, AI, and data analysis.
  • Action Steps:
    • Start with Python basics: syntax, loops, functions, and file handling.
    • Learn data manipulation using pandas and NumPy.
    • Practice Python scripting for automating repetitive tasks.
    • Work on Python projects related to your Make workflows and AI goals.

7. Learn SQL

  • Goal: Use SQL for data extraction and analysis.
  • Action Steps:
    • Learn SQL syntax and commands (SELECT, JOIN, WHERE, GROUP BY, etc.).
    • Practice querying real-world datasets using platforms like SQLZOO or LeetCode (SQL problems).
    • Understand database design concepts: normalization, indexes, and constraints.
    • Work on SQL projects integrating databases with your Power BI dashboards.

Suggested Approach:

  1. Dedicate focused blocks of time to each phase (e.g., 1-2 months per topic).
  2. Apply a project-based learning approach to reinforce skills (build something tangible for each phase).
  3. Continuously revisit earlier phases to integrate learnings into new topics (e.g., use Python and SQL together for AI projects).

This roadmap aligns well with becoming a versatile AI engineer and technology consultant. Let me know if you’d like resources or more detailed plans for any specific phase!

AI engineers design, develop, and deploy artificial intelligence and machine learning systems. Their work involves combining computer science, data engineering, and domain expertise to create solutions that leverage AI to solve real-world problems. Here’s an overview of what AI engineers typically do:

Core Responsibilities of AI Engineers

1. Data Preparation and Processing

  • Collect and curate data from various sources (databases, APIs, web scraping).
  • Clean and preprocess data to ensure quality and usability.
  • Engineer features from raw data that are most relevant for AI models.
  • Use tools like pandas, NumPy, or PySpark for data manipulation.

2. Building and Training AI Models

  • Develop machine learning models using frameworks like TensorFlow, PyTorch, or scikit-learn.
  • Train models on large datasets, optimizing for performance metrics like accuracy, precision, or recall.
  • Experiment with different algorithms (e.g., decision trees, neural networks, support vector machines) to solve specific problems.
  • Tune hyperparameters to improve model performance.

3. AI Model Deployment

  • Package trained models into deployable formats using tools like ONNX, Docker, or TensorFlow Serving.
  • Deploy models on platforms such as AWS SageMaker, Google AI Platform, or Azure ML.
  • Use APIs or integrate models into applications for real-time predictions.

4. Developing AI-Powered Applications

  • Build applications powered by AI, such as:
    • Chatbots and virtual assistants.
    • Recommendation engines (e.g., Netflix or Amazon).
    • Image recognition systems (e.g., facial recognition).
    • Predictive analytics systems (e.g., fraud detection).
  • Integrate AI models into existing systems using REST APIs or SDKs.

5. Monitoring and Optimizing AI Systems

  • Monitor model performance post-deployment to ensure accuracy and reliability.
  • Address issues like data drift or concept drift when models encounter new data.
  • Regularly retrain models to maintain performance.
  • Implement CI/CD pipelines for AI models to streamline updates and improvements.

6. Research and Experimentation

  • Stay updated on the latest advancements in AI (e.g., large language models, reinforcement learning).
  • Experiment with state-of-the-art techniques and apply them to projects.
  • Publish findings or share knowledge with peers.

Skills Required for AI Engineers

Technical Skills

  • Programming: Proficiency in Python, R, Java, or C++.
  • Machine Learning: Understanding supervised, unsupervised, and reinforcement learning.
  • Deep Learning: Knowledge of neural networks and frameworks like TensorFlow and PyTorch.
  • Data Engineering: Skills in SQL, NoSQL, and ETL pipelines for handling large datasets.
  • Cloud Platforms: Familiarity with AWS, GCP, or Azure for deploying AI solutions.
  • APIs and Integrations: Experience in creating and consuming APIs for AI applications.

Soft Skills

  • Problem-Solving: Identify the best AI solutions for complex problems.
  • Collaboration: Work with data scientists, developers, and business stakeholders.
  • Communication: Explain technical concepts to non-technical audiences.
  • Adaptability: Keep up with the rapidly evolving AI landscape.

Use Cases and Applications

AI engineers work across various industries, including:

  • Healthcare: Diagnosing diseases using AI, drug discovery.
  • Finance: Fraud detection, stock price predictions.
  • Retail: Personalization, inventory management.
  • Transportation: Autonomous vehicles, route optimization.
  • Entertainment: Content recommendations, game AI.
  • Manufacturing: Predictive maintenance, quality control.

In summary, AI engineers bridge the gap between theoretical machine learning concepts and practical applications, making AI solutions scalable, efficient, and impactful for businesses. Let me know if you’d like to explore specific roles or AI use cases!