Machine Learning (17.4%)
Models: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes, Neural Networks (including Deep Learning)

Techniques: Training/testing data splitting, cross-validation, feature scaling, model evaluation metrics (accuracy, precision, recall, F1-score)

Data Manipulation (13.9%)
Techniques: Data cleaning (handling missing values, outliers), data wrangling (sorting, filtering, aggregating), data transformation (scaling, normalization), merging datasets

Programming Skills (11.7%)
Languages: Python (widely used in data science for its libraries like pandas, NumPy, scikit-learn), R (another popular choice for statistical computing), SQL (for querying relational databases)

Statistics and Probability (11.7%)
Concepts: Descriptive statistics (mean, median, standard deviation), hypothesis testing, probability distributions (normal, binomial, Poisson), statistical inference

Big Data Technologies (9.3%)
Tools: Apache Spark, Hadoop, Kafka (for handling large and complex datasets)

Data Visualization (9.3%)
Techniques: Creating charts and graphs (scatter plots, bar charts, heatmaps), storytelling with data, choosing the right visualizations for the data

Model Deployment (9.3%)
Techniques: Cloud platforms (AWS SageMaker, Google Cloud AI Platform, Microsoft Azure Machine Learning), containerization (Docker), model monitoring

Data Science Roles

Types of Databases

Free Programming and Data Analytics Resources 👇👇

✅ Data science and Data Analytics Free Courses by Google

https://developers.google.com/edu/python/introduction

https://grow.google/intl/en_in/data-analytics-course/?tab=get-started-in-the-field

https://cloud.google.com/data-science?hl=en

https://developers.google.com/machine-learning/crash-course

https://t.iss.one/datasciencefun/1371

🔍 Free Data Analytics Courses by Microsoft

1. Get started with microsoft dataanalytics
https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/

2. Introduction to version control with git
https://learn.microsoft.com/en-us/training/paths/intro-to-vc-git/

3. Microsoft azure ai fundamentals
https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/

🤖 Free AI Courses by Microsoft

1. Fundamentals of AI by Microsoft

https://learn.microsoft.com/en-us/training/paths/get-started-with-artificial-intelligence-on-azure/

2. Introduction to AI with python by Harvard.

https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python

📚 Useful Resources for the Programmers

Data Analyst Roadmap
https://t.iss.one/sqlspecialist/94

Free C course from Microsoft
https://docs.microsoft.com/en-us/cpp/c-language/?view=msvc-170&viewFallbackFrom=vs-2019

Interactive React Native Resources
https://fullstackopen.com/en/part10

Python for Data Science and ML
https://t.iss.one/datasciencefree/68

Ethical Hacking Bootcamp
https://t.iss.one/ethicalhackingtoday/3

Unity Documentation
https://docs.unity3d.com/Manual/index.html

Advanced Javascript concepts
https://t.iss.one/Programming_experts/72

Oops in Java
https://nptel.ac.in/courses/106105224

Intro to Version control with Git
https://docs.microsoft.com/en-us/learn/modules/intro-to-git/0-introduction

Python Data Structure and Algorithms
https://t.iss.one/programming_guide/76

Free PowerBI course by Microsoft
https://docs.microsoft.com/en-us/users/microsoftpowerplatform-5978/collections/k8xidwwnzk1em

Data Structures Interview Preparation
https://t.iss.one/crackingthecodinginterview/309?single

🍻 Free Programming Courses by Microsoft

❯ JavaScript
https://learn.microsoft.com/training/paths/web-development-101/

❯ TypeScript
https://learn.microsoft.com/training/paths/build-javascript-applications-typescript/

❯ C#
https://learn.microsoft.com/users/dotnet/collections/yz26f8y64n7k07

Join @free4unow_backup for more free resources.

ENJOY LEARNING 👍👍

An important collection of the 15 best machine learning cheat sheets.

1- Supervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf

2- Unsupervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf

3- Deep Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf

4- Machine Learning Tips and Tricks

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf

5- Probabilities and Statistics

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf

6- Comprehensive Stanford Master Cheat Sheet

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf

7- Linear Algebra and Calculus

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf

8- Data Science Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf

9- Keras Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf

10- Deep Learning with Keras Cheat Sheet

https://github.com/rstudio/cheatsheets/raw/master/keras.pdf

11- Visual Guide to Neural Network Infrastructures

https://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png

12- Skicit-Learn Python Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf

13- Scikit-learn Cheat Sheet: Choosing the Right Estimator

https://scikit-learn.org/stable/tutorial/machine_learning_map/

14- Tensorflow Cheat Sheet

https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf

15- Machine Learning Test Cheat Sheet

https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/

ENJOY LEARNING 👍👍

How to start a data science career

Coursera Plus is available at the least possible cost: 👇 https://imp.i384100.net/xLyEmx

If you want to learn Data Science, Data Analytics, Project Management, Artificial Intelligence, etc.

10 commonly asked data science interview questions along with their answers

1️⃣ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.

2️⃣ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.

3️⃣ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.

4️⃣ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.

5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.

6️⃣ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.

7️⃣ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.

8️⃣ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.

9️⃣ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.

🔟 What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.iss.one/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊

How do you start AI and ML ?

Where do you go to learn these skills? What courses are the best?

There’s no best answer🥺. Everyone’s path will be different. Some people learn better with books, others learn better through videos.

What’s more important than how you start is why you start.

Start with why.

Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.

Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.

Got a why? Good. Time for some hard skills.

I can only recommend what I’ve tried every week new course lauch better than others its difficult to recommend any course

You can completed courses from (in order):

Treehouse / youtube( free) - Introduction to Python

Udacity - Deep Learning & AI Nanodegree

fast.ai - Part 1and Part 2

They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.

If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.

Join for more: https://t.iss.one/machinelearning_deeplearning

👉Telegram Link: https://t.iss.one/addlist/ID95piZJZa0wYzk5

Like for more ❤️

All the best 👍👍

FREE FREE FREE

10 Books on Data Science & Data Analysis will be posted on this channel daily basis
👇👇
https://t.iss.one/codingfreebooks/414

7 Baby steps to start with Machine Learning:

1. Start with Python
2. Learn to use Google Colab
3. Take a Pandas tutorial
4. Then a Seaborn tutorial
5. Decision Trees are a good first algorithm
6. Finish Kaggle's "Intro to Machine Learning"
7. Solve the Titanic challenge

Skills for Data Scientist

Data Science Roadmap

The Roadmap for Mastering Machine Learning in 2025

Link: https://machinelearningmastery.com/roadmap-mastering-machine-learning-2025/

For those of you who are new to Data Science and Machine learning algorithms, let me try to give you a brief overview. ML Algorithms can be categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.

1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.

2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.

3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.iss.one/datasciencefun

Like if you need similar content

ENJOY LEARNING 👍👍

✅𝟓-𝐒𝐭𝐞𝐩 𝐑𝐨𝐚𝐝𝐦𝐚𝐩 𝐭𝐨 𝐒𝐰𝐢𝐭𝐜𝐡 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐅𝐢𝐞𝐥𝐝✅

💁‍♀️𝐁𝐮𝐢𝐥𝐝 𝐊𝐞𝐲 𝐒𝐤𝐢𝐥𝐥𝐬: Focus on core skills—Excel, SQL, Power BI, and Python.

💁‍♀️𝐇𝐚𝐧𝐝𝐬-𝐎𝐧 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬: Apply your skills to real-world data sets. Projects like sales analysis or customer segmentation show your practical experience. You can find projects on Youtube.

💁‍♀️𝐅𝐢𝐧𝐝 𝐚 𝐌𝐞𝐧𝐭𝐨𝐫: Connect with someone experienced in data analytics for guidance(like me 😅). They can provide valuable insights, feedback, and keep you on track.

💁‍♀️𝐂𝐫𝐞𝐚𝐭𝐞 𝐏𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨: Compile your projects in a portfolio or on GitHub. A solid portfolio catches a recruiter’s eye.

💁‍♀️𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬: Practice SQL queries and Python coding challenges on Hackerrank & LeetCode. Strengthening your problem-solving skills will prepare you for interviews.

Libraries for Data Science in Python

🔹 Supervised Learning - Key Algorithms 🔹

1️⃣ Linear Regression – Predicts continuous values by fitting a straight line. (📈 House prices)
2️⃣ Logistic Regression – Classifies data into categories (yes/no). (📩 Spam detection)
3️⃣ SVM (Support Vector Machine) – Finds the best boundary to separate classes. (🚀 Image classification)
4️⃣ Decision Tree – Splits data based on conditions to classify. (🌳 Diagnosing diseases)
5️⃣ Random Forest – Multiple decision trees combined for accuracy. (🏦 Loan predictions)
6️⃣ k-NN (k-Nearest Neighbors) – Classifies based on the nearest neighbors. (🛒 Product recommendations)
7️⃣ Naive Bayes – Uses probability to classify data. (📨 Spam filter)
8️⃣ Gradient Boosting – Combines weak models to build a strong one. (📊 Customer churn prediction)
9️⃣ XGBoost – Faster and more efficient gradient boosting. (🏆 Machine learning competitions)

✨ Key Tip: Choose algorithms based on data type (classification/regression)

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

Credits: https://t.iss.one/datasciencefun

Like if you need similar content 😄👍

Hope this helps you 😊