6 Tips for Building a Robust Machine Learning Model

1. Understand the problem thoroughly before jumping into the model.
➝ Taking time to understand the problem helps build a solution aligned with business needs and goals.

2. Focus on feature engineering to improve accuracy.
➝ Well-engineered features make a big difference in model performance. Collaborating with data engineers on clean and well-structured data can simplify feature engineering.

3. Start simple, test assumptions, and iterate.
➝ Begin with straightforward models to test ideas quickly. Iteration and experimentation will lead to stronger results.

4. Keep track of versions for reproducibility. 
➝  Documenting versions of data and code helps maintain consistency, making it easier to reproduce results.

5. Regularly validate your model with new data.
➝ Models should be updated and validated as new data becomes available to avoid performance degradation.

6. Always prioritize interpretability alongside accuracy.
➝ Building interpretable models helps stakeholders understand and trust your results, making insights more actionable.

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Use this checklist to see if you’re truly JOB-READY. The more items you complete, the closer you are to landing your dream data science job! 😎

Check Your Skills with This Checklist!

Python:-
Master Python fundamentals
Understand Pandas for data manipulation
Learn data visualization with Matplotlib and Seaborn
Practice error handling and debugging

Statistics:-
Grasp probability theory
Know denoscriptive and inferential statistics
Learn statistical machine learning concepts

Exploratory Data Analysis (EDA):-
Perform data summarization
Work on data cleaning and transformation
Visualize data effectively

SQL:-
Understand the BIG 6 SQL statements
Practice joins and common table expressions (CTEs)
Use window functions
Learn to write stored procedures

Machine Learning:-
Master feature engineering
Understand regression and classification techniques
Learn clustering methods

Model Evaluation:-
Work with confusion matrices
Understand precision, recall, and F1-score
Practice cross-validation
Learn about overfitting and underfitting

Deep Learning:-
Get familiar with Convolutional Neural Networks (CNNs)
Understand transformers
Learn PyTorch or TensorFlow basics
Practice model training and optimization

Resume:-
Ensure your resume is ATS-friendly
Customize for the job denoscription
Use the STAR method to highlight achievements
Include a link to your portfolio

AI-Enabled Mindset:-
Develop Googling skills
Use AI tools like ChatGPT or Bard for learning
Commit to continuous learning
Hone problem-solving abilities

Communication:-
Practice presenting insights clearly
Write professional emails
Manage stakeholder communication
Utilize project management tools

LinkedIn:-
Have a good profile picture and banner
Get 10+ endorsed skills
Collect at least 3 recommendations
Link your portfolio in your profile

Portfolio:-
Include 4+ business-related projects
Showcase one project per tool you know
Create an insights desk
Prepare a video presentation

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Skills for Data Scientists 👆

🔗 Machine Learning libraries

Creating a one-month data analytics roadmap requires a focused approach to cover essential concepts and skills. Here's a structured plan along with free resources:

🗓️Week 1: Foundation of Data Analytics

◾Day 1-2: Basics of Data Analytics
Resource: Khan Academy's Introduction to Statistics
Focus Areas: Understand denoscriptive statistics, types of data, and data distributions.

◾Day 3-4: Excel for Data Analysis
Resource: Microsoft Excel tutorials on YouTube or Excel Easy
Focus Areas: Learn essential Excel functions for data manipulation and analysis.

◾Day 5-7: Introduction to Python for Data Analysis
Resource: Codecademy's Python course or Google's Python Class
Focus Areas: Basic Python syntax, data structures, and libraries like NumPy and Pandas.

🗓️Week 2: Intermediate Data Analytics Skills

◾Day 8-10: Data Visualization
Resource: Data Visualization with Matplotlib and Seaborn tutorials
Focus Areas: Creating effective charts and graphs to communicate insights.

◾Day 11-12: Exploratory Data Analysis (EDA)
Resource: Towards Data Science articles on EDA techniques
Focus Areas: Techniques to summarize and explore datasets.

◾Day 13-14: SQL Fundamentals
Resource: Mode Analytics SQL Tutorial or SQLZoo
Focus Areas: Writing SQL queries for data manipulation.

🗓️Week 3: Advanced Techniques and Tools

◾Day 15-17: Machine Learning Basics
Resource: Andrew Ng's Machine Learning course on Coursera
Focus Areas: Understand key ML concepts like supervised learning and evaluation metrics.

◾Day 18-20: Data Cleaning and Preprocessing
Resource: Data Cleaning with Python by Packt
Focus Areas: Techniques to handle missing data, outliers, and normalization.

◾Day 21-22: Introduction to Big Data
Resource: Big Data University's courses on Hadoop and Spark
Focus Areas: Basics of distributed computing and big data technologies.


🗓️Week 4: Projects and Practice

◾Day 23-25: Real-World Data Analytics Projects
Resource: Kaggle datasets and competitions
Focus Areas: Apply learned skills to solve practical problems.

◾Day 26-28: Online Webinars and Community Engagement
Resource: Data Science meetups and webinars (Meetup.com, Eventbrite)
Focus Areas: Networking and learning from industry experts.


◾Day 29-30: Portfolio Building and Review
Activity: Create a GitHub repository showcasing projects and code
Focus Areas: Present projects and skills effectively for job applications.

👉Additional Resources:
Books: "Python for Data Analysis" by Wes McKinney, "Data Science from Scratch" by Joel Grus.
Online Platforms: DataSimplifier, Kaggle, Towards Data Science

Tailor this roadmap to your learning pace and adjust the resources based on your preferences. Consistent practice and hands-on projects are crucial for mastering data analytics within a month. Good luck!

You don't need to spend several $𝟭𝟬𝟬𝟬𝘀 to learn Data Science.❌

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2. Data Science: Machine Learning (Harvard)

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3. Artificial Intelligence (MIT)

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4. Introduction to Computational Thinking and Data Science (MIT)

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5. Machine Learning (MIT)

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6. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)

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8. Mining Massive Data Sets (Stanford)

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“The Best Public Datasets for Machine Learning and Data Science” by Stacy Stanford

https://datasimplifier.com/best-data-analyst-projects-for-freshers/

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http://mlr.cs.umass.edu/ml/

https://www.visualdata.io/

https://guides.library.cmu.edu/machine-learning/datasets

https://www.data.gov/

https://nces.ed.gov/

https://www.ukdataservice.ac.uk/

https://datausa.io/

https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html

https://www.kaggle.com/xiuchengwang/python-dataset-download

https://www.quandl.com/

https://data.worldbank.org/

https://www.imf.org/en/Data

https://markets.ft.com/data/

https://trends.google.com/trends/?q=google&ctab=0&geo=all&date=all&sort=0

https://www.aeaweb.org/resources/data/us-macro-regional

http://xviewdataset.org/#dataset

http://labelme.csail.mit.edu/Release3.0/browserTools/php/dataset.php

http://image-net.org/

http://cocodataset.org/

http://visualgenome.org/

https://ai.googleblog.com/2016/09/introducing-open-images-dataset.html?m=1

http://vis-www.cs.umass.edu/lfw/

http://vision.stanford.edu/aditya86/ImageNetDogs/

http://web.mit.edu/torralba/www/indoor.html

http://www.cs.jhu.edu/~mdredze/datasets/sentiment/

http://ai.stanford.edu/~amaas/data/sentiment/

http://nlp.stanford.edu/sentiment/code.html

http://help.sentiment140.com/for-students/

https://www.kaggle.com/crowdflower/twitter-airline-sentiment

https://hotpotqa.github.io/

https://www.cs.cmu.edu/~./enron/

https://snap.stanford.edu/data/web-Amazon.html

https://aws.amazon.com/datasets/google-books-ngrams/

http://u.cs.biu.ac.il/~koppel/BlogCorpus.htm

https://code.google.com/archive/p/wiki-links/downloads

http://www.dt.fee.unicamp.br/~tiago/smsspamcollection/

https://www.yelp.com/dataset

https://news.1rj.ru/str/DataPortfolio/2

https://archive.ics.uci.edu/ml/datasets/Spambase

https://bdd-data.berkeley.edu/

http://apolloscape.auto/

https://archive.org/details/comma-dataset

https://www.cityscapes-dataset.com/

http://aplicaciones.cimat.mx/Personal/jbhayet/ccsad-dataset

http://www.vision.ee.ethz.ch/~timofter/traffic_signs/

http://cvrr.ucsd.edu/LISA/datasets.html

https://hci.iwr.uni-heidelberg.de/node/6132

http://www.lara.prd.fr/benchmarks/trafficlightsrecognition

http://computing.wpi.edu/dataset.html

https://mimic.physionet.org/

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