Complete Machine Learning Roadmap with Resources

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Today AI And ML trending subjects that are used everywhere.

So Let’s Start

First, watch this video, which helps to understand how to change the world through AI.”

This Video released by the Tesla Company

Company Tesla
Founder Elon Mask – 2003
HeadquartersAustin, Texas, U.S.
Websitetesla.com
Company TypePublic

Machine Learning Roadmap

Machine Learning is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed.


ML focuses on the development of algorithms that can analyze data, identify patterns and make decisions with minimal human intervention

  1. Introduction to Machine Learning
       – Definition
       – Purpose
       – Types of Machine Learning (Supervised, Unsupervised, Reinforcement)
  2. Mathematics for Machine Learning
       – Linear Algebra
       – Calculus
       – Statistics and Probability
  3. Programming Languages for ML
       – Python and Libraries (NumPy, Pandas, Matplotlib)
       – R
  4. Data Preprocessing
       – Handling Missing Data
       – Feature Scaling
       – Data Transformation
  5. Exploratory Data Analysis (EDA)
       – Data Visualization
       – Descriptive Statistics
  6. Supervised Learning
       – Regression
       – Classification
       – Model Evaluation
  7. Unsupervised Learning
       – Clustering (K-Means, Hierarchical)
       – Dimensionality Reduction (PCA)
  8. Model Selection and Evaluation
       – Cross-Validation
       – Hyperparameter Tuning
       – Evaluation Metrics (Precision, Recall, F1 Score)
  9. Ensemble Learning
       – Random Forest
       – Gradient Boosting
  10. Neural Networks and Deep Learning
        – Introduction to Neural Networks
        – Building and Training Neural Networks
        – Convolutional Neural Networks (CNN)
        – Recurrent Neural Networks (RNN)
  11. Natural Language Processing (NLP)
        – Text Preprocessing
        – Sentiment Analysis
        – Named Entity Recognition (NER)
  12. Reinforcement Learning
        – Basics
        – Markov Decision Processes
        – Q-Learning
  13. Machine Learning Frameworks
        – TensorFlow
        – PyTorch
        – Scikit-Learn
  14. Deployment of ML Models
        – Flask for Web Deployment
        – Docker and Kubernetes
  15. Ethical and Responsible AI
        – Bias and Fairness
        – Ethical Considerations
  16. Machine Learning in Production
        – Model Monitoring
        – Continuous Integration/Continuous Deployment (CI/CD)
  17. Real-world Projects and Case Studies
  18. Machine Learning Resources
        – Online Courses
        – Books
        – Blogs and Journals

Trending Jobs in 2024

  • Data Scientist
  • ML Engineer 
  • Software Engineer
  • Cloud Engineer
  • Graphic Designer 
  • Blockchain Specialist
  • Data Analyst
  • Fullstack Developer
  • Mobile Developer
  • Data Engineer 

Machine Learning Resources Link

  1. http://www.maths.qmul.ac.uk/~pjc/notes/linalg.pdf (Maths)
  2. https://www.mathsbox.org.uk/twi/astats.pdf (Maths)
  3. https://www.youtube.com/playlist?list=PLLy_2iUCG87D1CXFxE-SxCFZUiJzQ3IvE (Maths)
  4. https://developers.google.com/machine-learning/crash-course (ML by Google)
  5. https://www.datacamp.com/courses/intro-to-python-for-data-science (Python Basics)
  6. https://www.coursera.org/learn/machine-learning (Stanford Course by Andrew ng)
  7. https://madewithml.com/ 
  8. https://www.javatpoint.com/data-preprocessing-machine-learning (Data Preprocessing)
  9. https://scikit-learn.org/stable/ (Scikit Learn)
  10. https://www.tensorflow.org/ (Tensorflow)
  11. https://www.kaggle.com/ (Kaggle)

Try these Free courses from Microsoft in 2024 to level up your Skills

𝟭. 𝗗𝗮𝘁𝗮 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
https://learn.microsoft.com/en-us/training/paths/azure-data-fundamentals-explore-non-relational-data/

𝟮. 𝗦𝗾𝗹 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
https://learn.microsoft.com/en-us/training/paths/azure-sql-fundamentals/

𝟯. 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜
https://learn.microsoft.com/en-us/training/paths/create-use-analvtics-reports-power-bi/

𝟰. 𝗔𝘇𝘂𝗿𝗲 𝗰𝗼𝘀𝗺𝗼𝘀 𝗗𝗕
https://learn.microsoft.com/en-us/training/paths/create-use-analytics-reports-power-bi/

𝟱. 𝗔𝗜 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀
https://learn.microsoft.com/en-us/training/paths/create-no-code-predictive-models-azure-machine-learning/

𝟲. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
https://learn.microsoft.com/en-us/training/modules/introduction-to-machine-learning/

𝟳. 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗔𝗜 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀
https://learn.microsoft.com/en-us/training/modules/get-started-ai-fundamentals/

𝟴. 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗼𝗳 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜
https://learn.microsoft.com/en-us/training/modules/fundamentals-generative-ai/

𝟵. 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗼𝗳 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻
https://learn.microsoft.com/en-us/training/modules/analyze-images-computer-vision/

𝟭𝟬. 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 𝗼𝗳 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜
https://learn.microsoft.com/en-us/training/modules/responsible-generative-ai/

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