There’s no single powerful machine learning algorithm that works well on any problem.

Yes, algorithms like XGBoost can help you in Kaggle Competitions to build more accurate models.

But the real world is different. Choose algorithms based on your data characteristics, the assumptions of algorithms, and the problem type.

Complete Machine Learning Roadmap
👇👇

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
- Denoscriptive 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

📚 Learning Resources for Machine Learning:
- [Python for Machine Learning](https://news.1rj.ru/str/udacityfreecourse/167)
- [Fast.ai: Practical Deep Learning for Coders](https://course.fast.ai/)
- [Intro to Machine Learning](https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/)

📚 Books:
- Machine Learning Interviews
- Machine Learning for Absolute Beginners

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Data Scientist Vs. Data Analyst Vs. Data Engineer

What’s the difference between the data roles?

The data role family is more than just one role that does it all.

Here are the key differences.

Data Scientist

- Focuses on deriving insights and creating predictive models.
- Strong background in math, statistics, and machine learning.
- Analyzing complex datasets to identify patterns, trends, and insights.
- Developing predictive models and machine learning algorithms.
- Communicating findings to stakeholders through reports and visualizations.
- Working with data engineers and analysts to implement data-driven solutions.
- Uses tools like Python, R, SQL, Tableau, and others

Data analyst

- Focuses more on interpreting and visualizing data rather than creating predictive models.
- Often works closely with business teams to provide actionable insights.
- Collecting, processing, and performing statistical analyses on large data sets.
- Creating data visualizations and dashboards to communicate insights.
- Conducting ad-hoc analyses and generating reports for business decision-making.
- Ensuring data quality and accuracy.
- Uses tools like Excel, SQL, BI Tools, SAS

Data Engineer

- Focuses on the infrastructure and tools needed to store, process, and retrieve data.
- Designing, building, and maintaining data pipelines and architectures.
- Ensuring data is accessible, reliable, and efficient to process.
- Integrating data from various sources and formats.
- Optimizing database performance and data storage solutions.
- Uses languages like Python, Java, Scala, as well as SQL and NOSQL, ETL tools, data warehouse tools and others

Like if you need similar content 😄👍

Hope this helps you 😊

ML Engineer vs AI Engineer

ML Engineer / MLOps

-Focuses on the deployment of machine learning models.
-Bridges the gap between data scientists and production environments.
-Designing and implementing machine learning models into production.
-Automating and orchestrating ML workflows and pipelines.
-Ensuring reproducibility, scalability, and reliability of ML models.
-Programming: Python, R, Java
-Libraries: TensorFlow, PyTorch, Scikit-learn
-MLOps: MLflow, Kubeflow, Docker, Kubernetes, Git, Jenkins, CI/CD tools

AI Engineer / Developer

- Applying AI techniques to solve specific problems.
- Deep knowledge of AI algorithms and their applications.
- Developing and implementing AI models and systems.
- Building and integrating AI solutions into existing applications.
- Collaborating with cross-functional teams to understand requirements and deliver AI-powered solutions.
- Programming: Python, Java, C++
- Libraries: TensorFlow, PyTorch, Keras, OpenCV
- Frameworks: ONNX, Hugging Face

TOP 10 SQL Concepts for Job Interview

1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)


TOP 10 Statistics Concepts for Job Interview

1. Sampling
2. Experiments (A/B tests)
3. Denoscriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression


TOP 10 Python Concepts for Job Interview

1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming

🔺 Free Machine learning Courses

1️⃣ Intro to ML course : an introductory and self-paced course to start machine learning.

2️⃣ ML for Everybody course : A simple approach to learning machine learning concepts.

3️⃣ ML in Python course : focus on machine learning with Python and Scikit-Learn.

4️⃣ ML Crash Course : A quick but comprehensive introduction to machine learning.

5️⃣ CS229 : ML : An advanced course for those who want to deepen their knowledge

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No one tells you to train Machine Learning models in Data Science interviews.

Problems in Data Science interviews are focused on:

1. SQL for Querying Data

2. Python/R for Data Manipulation

3. Scenario Based Problems to test your way of approaching problems

10 Best Practices for Data Science

The main bottleneck in data science are no longer compute power or sophisticated algorithms, but craftsmanship, communication, and process.

And that the aim is to not only produce work that is accurate and correct, but also can be understood, work that others can collaborate on.

Rule 1: Start Organized, Stay Organized
Rule 2: Everything Comes from Somewhere, and the Raw Data is Immutable
Rule 3: Version Control is Basic Professionalism
Rule 4: Notebooks are for Exploration, Source Files are for Repetition
Rule 5: Tests and Sanity Checks Prevent Catastrophes
Rule 6: Fail Loudly, Fail Quickly
Rule 7: Project Runs are Fully Automated from Raw Data to Final Outputs
Rule 8: Important Parameters are Extracted and Centralized
Rule 9: Project Runs are Verbose by Default and Result in Tangible Artifacts
Rule 10: Start with the Simplest Possible End-to-End Pipeline
Lessons

Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started:

1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.

2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.

3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.

4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.

5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.

6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.

7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.

Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!

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