Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data Buy ads: https://telega.io/c/datasciencefun
Machine learning is a subset of artificial intelligence that involves developing algorithms and models that enable computers to learn from and make predictions or decisions based on data. In machine learning, computers are trained on large datasets to identify patterns, relationships, and trends without being explicitly programmed to do so.
There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the algorithm is trained on labeled data, where the correct output is provided along with the input data. Unsupervised learning involves training the algorithm on unlabeled data, allowing it to identify patterns and relationships on its own. Reinforcement learning involves training an algorithm to make decisions by rewarding or punishing it based on its actions.
Machine learning algorithms can be used for a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, predictive analytics, and more. These algorithms can be trained using various techniques such as neural networks, decision trees, support vector machines, and clustering algorithms.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
What is the difference between data scientist, data engineer, data analyst and business intelligence?
🧑🔬 Data Scientist
Focus: Using data to build models, make predictions, and solve complex problems.
Cleans and analyzes data
Builds machine learning models
Answers “Why is this happening?” and “What will happen next?”
Works with statistics, algorithms, and coding (Python, R)
Example: Predict which customers are likely to cancel next month
🛠️ Data Engineer
Focus: Building and maintaining the systems that move and store data.
Designs and builds data pipelines (ETL/ELT)
Manages databases, data lakes, and warehouses
Ensures data is clean, reliable, and ready for others to use
Uses tools like SQL, Airflow, Spark, and cloud platforms (AWS, Azure, GCP)
Example: Create a system that collects app data every hour and stores it in a warehouse
📊 Data Analyst
Focus: Exploring data and finding insights to answer business questions.
Pulls and visualizes data (dashboards, reports)
Answers “What happened?” or “What’s going on right now?”
Works with SQL, Excel, and tools like Tableau or Power BI
Less coding and modeling than a data scientist
Example: Analyze monthly sales and show trends by region
📈 Business Intelligence (BI) Professional
Focus: Helping teams and leadership understand data through reports and dashboards.
Designs dashboards and KPIs (key performance indicators)
Translates data into stories for non-technical users
Often overlaps with data analyst role but more focused on reporting
Tools: Power BI, Looker, Tableau, Qlik
Example: Build a dashboard showing company performance by department
🧩 Summary Table
Data Scientist - What will happen? Tools: Python, R, ML tools, predictions & models
Data Engineer - How does the data move and get stored? Tools: SQL, Spark, cloud tools, infrastructure & pipelines
Data Analyst - What happened? Tools: SQL, Excel, BI tools, reports & exploration
BI Professional - How can we see business performance clearly? Tools: Power BI, Tableau, dashboards & insights for decision-makers
🎯 In short:
Data Engineers build the roads.
Data Scientists drive smart cars to predict traffic.
Data Analysts look at traffic data to see patterns.
BI Professionals show everyone the traffic report on a screen.
What is the output of following Python Code?
Читать полностью…𝗪𝗮𝗻𝘁 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘?😍
YouTube has your back! Here’s a full learning path to take your analytics game from beginner to confident analyst — all through real-world examples and expert walkthroughs💡
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/42UO2OZ
Save this post and start learning step by step!✅️
𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗝𝗼𝗯𝘀 𝗜𝗻 𝗧𝗼𝗽 𝗠𝗡𝗖𝘀|𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄 😍
𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇
Optum:- https://pdlink.in/4dmh6lR
eBay:- https://pdlink.in/43l3SkM
Ford:- https://pdlink.in/3YOfUSm
Walmart:- https://pdlink.in/3H0fORx
Clarivate:- https://pdlink.in/3SDwdxQ
Yash:- https://pdlink.in/4j6mScw
Apply before the link expires 💫
𝟰 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗜𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲😍
You don’t need an Ivy League budget to learn from the best🚀
Thanks to MIT OpenCourseWare, you can now access world-class data science education for free🎊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4kmYOn1
Enjoy Learning ✅️
𝗪𝗼𝗿𝗸 𝗙𝗿𝗼𝗺 𝗛𝗼𝗺𝗲 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆😍
Top 5 global tech companies hiring
▪️ CTC: ₹3.2–₹4 LPA
▪️ Exp: 0–4 yrs (Freshers welcome)
▪️ Location: Remote
Apply by:- 18 May 2025, 11:59 PM
𝗔𝗽𝗽𝗹𝘆 𝗡𝗼𝘄👇 :-
https://pdlink.in/4mcgy6p
A great chance to work with a global e-commerce leader—don’t miss it!
𝟮𝟬 𝗠𝘂𝘀𝘁-𝗞𝗻𝗼𝘄 𝗦𝗤𝗟 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗔𝘀𝗸𝗲𝗱 𝗯𝘆 𝗚𝗼𝗼𝗴𝗹𝗲, 𝗔𝗺𝗮𝘇𝗼𝗻 & 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁😍
Are you preparing for SQL interviews but feeling unsure about what to expect?🎯
Whether you’re aiming for roles at Google, Amazon, Microsoft, or top startups, these 20 commonly asked SQL interview questions are your secret weapon to ace the technical rounds📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jXfSAh
All The Best 🎊
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗙𝗥𝗘𝗘 𝗗𝗲𝗺𝗼 𝗖𝗹𝗮𝘀𝘀 𝗜𝗻 𝗛𝘆𝗱𝗲𝗿𝗮𝗯𝗮𝗱 😍
📊 “Data Analyst” is one of the hottest careers in tech — and guess what? NO coding needed!
Now it’s YOUR turn to break into tech! 💼
Here’s what you get:-
✅No Coding Required
✅100% Placement Support
✅Offline Classes in Hyderabad with Expert Mentors
✅Real-world Projects & Industry Certification
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:-
https://pdlink.in/3SIOrhj
Location:- Gachibowli Centre, Hyderabad!
Date & Time:- 17th May, 4 To 6PM
𝗖𝗶𝘀𝗰𝗼 𝗙𝗥𝗘𝗘 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Stand out in the competitive job market.Cisco Networking Academy has you covered with free courses designed to enhance your professional skills.
✅ Learn the Most In-Demand Skills:
✅ Perfect for Everyone
✅ Earn Recognized Certificates
𝗟𝗶𝗻𝗸👇:-
https://pdlink.in/3PeiTOW
Enroll for FREE & Get Certified 🎓
Amazon Interview Process for Data Scientist position
📍Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.
After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).
📍 𝗥𝗼𝘂𝗻𝗱 𝟮- 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗕𝗿𝗲𝗮𝗱𝘁𝗵:
In this round the interviewer tested my knowledge on different kinds of topics.
📍𝗥𝗼𝘂𝗻𝗱 𝟯- 𝗗𝗲𝗽𝘁𝗵 𝗥𝗼𝘂𝗻𝗱:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.
📍𝗥𝗼𝘂𝗻𝗱 𝟰- 𝗖𝗼𝗱𝗶𝗻𝗴 𝗥𝗼𝘂𝗻𝗱-
This was a Python coding round, which I cleared successfully.
📍𝗥𝗼𝘂𝗻𝗱 𝟱- This was 𝗛𝗶𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 where my fitment for the team got assessed.
📍𝗟𝗮𝘀𝘁 𝗥𝗼𝘂𝗻𝗱- 𝗕𝗮𝗿 𝗥𝗮𝗶𝘀𝗲𝗿- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.
So, here are my Tips if you’re targeting any Data Science role:
-> Never make up stuff & don’t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
ENJOY LEARNING 👍👍
🚀 Complete Roadmap to Become a Data Scientist in 5 Months
📅 Week 1-2: Fundamentals
✅ Day 1-3: Introduction to Data Science, its applications, and roles.
✅ Day 4-7: Brush up on Python programming 🐍.
✅ Day 8-10: Learn basic statistics 📊 and probability 🎲.
🔍 Week 3-4: Data Manipulation & Visualization
📝 Day 11-15: Master Pandas for data manipulation.
📈 Day 16-20: Learn Matplotlib & Seaborn for data visualization.
🤖 Week 5-6: Machine Learning Foundations
🔬 Day 21-25: Introduction to scikit-learn.
📊 Day 26-30: Learn Linear & Logistic Regression.
🏗 Week 7-8: Advanced Machine Learning
🌳 Day 31-35: Explore Decision Trees & Random Forests.
📌 Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
🧠 Week 9-10: Deep Learning
🤖 Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
📸 Day 46-50: Learn CNNs & RNNs for image & text data.
🏛 Week 11-12: Data Engineering
🗄 Day 51-55: Learn SQL & Databases.
🧹 Day 56-60: Data Preprocessing & Cleaning.
📊 Week 13-14: Model Evaluation & Optimization
📏 Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
📉 Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
🏗 Week 15-16: Big Data & Tools
🐘 Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
☁️ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
🚀 Week 17-18: Deployment & Production
🛠 Day 81-85: Deploy models using Flask or FastAPI.
📦 Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
🎯 Week 19-20: Specialization
📝 Day 91-95: Choose NLP or Computer Vision, based on your interest.
🏆 Week 21-22: Projects & Portfolio
📂 Day 96-100: Work on Personal Data Science Projects.
💬 Week 23-24: Soft Skills & Networking
🎤 Day 101-105: Improve Communication & Presentation Skills.
🌐 Day 106-110: Attend Online Meetups & Forums.
🎯 Week 25-26: Interview Preparation
💻 Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
📂 Day 116-120: Review your projects & prepare for discussions.
👨💻 Week 27-28: Apply for Jobs
📩 Day 121-125: Start applying for Entry-Level Data Scientist positions.
🎤 Week 29-30: Interviews
📝 Day 126-130: Attend Interviews & Practice Whiteboard Problems.
🔄 Week 31-32: Continuous Learning
📰 Day 131-135: Stay updated with the Latest Data Science Trends.
🏆 Week 33-34: Accepting Offers
📝 Day 136-140: Evaluate job offers & Negotiate Your Salary.
🏢 Week 35-36: Settling In
🎯 Day 141-150: Start your New Data Science Job, adapt & keep learning!
🎉 Enjoy Learning & Build Your Dream Career in Data Science! 🚀🔥
𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗙𝗿𝗼𝗺 𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀😍
Top Companies Offering FREE Certification Courses To Upskill In 2025
Google:- https://pdlink.in/3YsujTV
Microsoft :- https://pdlink.in/4jpmI0I
Cisco :- https://pdlink.in/4fYr1xO
HP :- https://pdlink.in/3DrNsxI
IBM :- https://pdlink.in/44GsWoC
Qualc :- https://pdlink.in/3YrFTyK
TCS :- https://pdlink.in/4cHavCa
Infosys :- https://pdlink.in/4jsHZXf
Enroll For FREE & Get Certified 🎓
If you want to Excel in Data Science and become an expert, master these essential concepts:
Core Data Science Skills:
• Python for Data Science – Pandas, NumPy, Matplotlib, Seaborn
• SQL for Data Extraction – SELECT, JOIN, GROUP BY, CTEs, Window Functions
• Data Cleaning & Preprocessing – Handling missing data, outliers, duplicates
• Exploratory Data Analysis (EDA) – Visualizing data trends
Machine Learning (ML):
• Supervised Learning – Linear Regression, Decision Trees, Random Forest
• Unsupervised Learning – Clustering, PCA, Anomaly Detection
• Model Evaluation – Cross-validation, Confusion Matrix, ROC-AUC
• Hyperparameter Tuning – Grid Search, Random Search
Deep Learning (DL):
• Neural Networks – TensorFlow, PyTorch, Keras
• CNNs & RNNs – Image & sequential data processing
• Transformers & LLMs – GPT, BERT, Stable Diffusion
Big Data & Cloud Computing:
• Hadoop & Spark – Handling large datasets
• AWS, GCP, Azure – Cloud-based data science solutions
• MLOps – Deploy models using Flask, FastAPI, Docker
Statistics & Mathematics for Data Science:
• Probability & Hypothesis Testing – P-values, T-tests, Chi-square
• Linear Algebra & Calculus – Matrices, Vectors, Derivatives
• Time Series Analysis – ARIMA, Prophet, LSTMs
Real-World Applications:
• Recommendation Systems – Personalized AI suggestions
• NLP (Natural Language Processing) – Sentiment Analysis, Chatbots
• AI-Powered Business Insights – Data-driven decision-making
Like this post if you need a complete tutorial on essential data science topics! 👍❤️
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗙𝗥𝗘𝗘 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 😍
Learn directly from industry leaders at Microsoft and LinkedIn Learning and gain in-demand skills to elevate your career
📈 Don’t miss this chance to build your skills, earn certifications, and get job-ready—all for free.
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/41ODJMi
Enroll for FREE & Get Certified 🎓
🚀 𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 – 𝗘𝗮𝗿𝗻 𝗨𝗽𝘁𝗼 ₹𝟰𝟭 𝗟𝗣𝗔!
Upskill from scratch and secure top tech jobs in top tech companies.
Program Highlights:-
✅ 60+ Hiring Drives/Month
✅ 7500+ Students Trained
✅ 500+ Hiring Partners
✅ Avg. Package: ₹7.2 LPA | Highest: ₹41 LPA
🎓 Eligibility: BTech, BCA, BSc, MCA, MSc
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗡𝗼𝘄👇:-
𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸:- https://pdlink.in/4hO7rWY
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:- https://bit.ly/4g3kyT6
Limited Seats. Enroll Today!💫
Data Science Interview Questions with Answers
Q. Explain the data preprocessing steps in data analysis.
Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.
Q. What Are the Three Stages of Building a Model in Machine Learning?
Ans. The three stages of building a machine learning model are:
Model Building: Choosing a suitable algorithm for the model and train it according to the requirement
Model Testing: Checking the accuracy of the model through the test data
Applying the Model: Making the required changes after testing and use the final model for real-time projects
Q. What are the subsets of SQL?
Ans. The following are the four significant subsets of the SQL:
Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.
Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.
Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.
Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.
Q. What is a Parameter in Tableau? Give an Example.
Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
Here are some essential data science concepts from A to Z:
A - Algorithm: A set of rules or instructions used to solve a problem or perform a task in data science.
B - Big Data: Large and complex datasets that cannot be easily processed using traditional data processing applications.
C - Clustering: A technique used to group similar data points together based on certain characteristics.
D - Data Cleaning: The process of identifying and correcting errors or inconsistencies in a dataset.
E - Exploratory Data Analysis (EDA): The process of analyzing and visualizing data to understand its underlying patterns and relationships.
F - Feature Engineering: The process of creating new features or variables from existing data to improve model performance.
G - Gradient Descent: An optimization algorithm used to minimize the error of a model by adjusting its parameters.
H - Hypothesis Testing: A statistical technique used to test the validity of a hypothesis or claim based on sample data.
I - Imputation: The process of filling in missing values in a dataset using statistical methods.
J - Joint Probability: The probability of two or more events occurring together.
K - K-Means Clustering: A popular clustering algorithm that partitions data into K clusters based on similarity.
L - Linear Regression: A statistical method used to model the relationship between a dependent variable and one or more independent variables.
M - Machine Learning: A subset of artificial intelligence that uses algorithms to learn patterns and make predictions from data.
N - Normal Distribution: A symmetrical bell-shaped distribution that is commonly used in statistical analysis.
O - Outlier Detection: The process of identifying and removing data points that are significantly different from the rest of the dataset.
P - Precision and Recall: Evaluation metrics used to assess the performance of classification models.
Q - Quantitative Analysis: The process of analyzing numerical data to draw conclusions and make decisions.
R - Random Forest: An ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy.
S - Support Vector Machine (SVM): A supervised learning algorithm used for classification and regression tasks.
T - Time Series Analysis: A statistical technique used to analyze and forecast time-dependent data.
U - Unsupervised Learning: A type of machine learning where the model learns patterns and relationships in data without labeled outputs.
V - Validation Set: A subset of data used to evaluate the performance of a model during training.
W - Web Scraping: The process of extracting data from websites for analysis and visualization.
X - XGBoost: An optimized gradient boosting algorithm that is widely used in machine learning competitions.
Y - Yield Curve Analysis: The study of the relationship between interest rates and the maturity of fixed-income securities.
Z - Z-Score: A standardized score that represents the number of standard deviations a data point is from the mean.
Credits: /channel/free4unow_backup
Like if you need similar content 😄👍
Machine Learning Algorithms every data scientist should know:
📌 Supervised Learning:
🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression
🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)
📌 Unsupervised Learning:
🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN
🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)
📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)
📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking
Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.
Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
React ❤️ for more free resources
Various types of test used in statistics for data science
T-test: used to test whether the means of two groups are significantly different from each other.
ANOVA: used to test whether the means of three or more groups are significantly different from each other.
Chi-squared test: used to test whether two categorical variables are independent or associated with each other.
Pearson correlation test: used to test whether there is a significant linear relationship between two continuous variables.
Wilcoxon signed-rank test: used to test whether the median of two related samples is significantly different from each other.
Mann-Whitney U test: used to test whether the median of two independent samples is significantly different from each other.
Kruskal-Wallis test: used to test whether the medians of three or more independent samples are significantly different from each other.
Friedman test: used to test whether the medians of three or more related samples are significantly different from each other.
Data Science Learning Plan
Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)
Step 2: Python for Data Science (Basics and Libraries)
Step 3: Data Manipulation and Analysis (Pandas, NumPy)
Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)
Step 5: Databases and SQL for Data Retrieval
Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)
Step 7: Data Cleaning and Preprocessing
Step 8: Feature Engineering and Selection
Step 9: Model Evaluation and Tuning
Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)
Step 11: Working with Big Data (Hadoop, Spark)
Step 12: Building Data Science Projects and Portfolio
Data Science Resources
👇👇
https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Like for more 😄
Today, lets understand Machine Learning in simplest way possible
What is Machine Learning?
Think of it like this:
Machine Learning is when you teach a computer to learn from data, so it can make decisions or predictions without being told exactly what to do step-by-step.
Real-Life Example:
Let’s say you want to teach a kid how to recognize a dog.
You show the kid a bunch of pictures of dogs.
The kid starts noticing patterns — “Oh, they have four legs, fur, floppy ears...”
Next time the kid sees a new picture, they might say, “That’s a dog!” — even if they’ve never seen that exact dog before.
That’s what machine learning does — but instead of a kid, it's a computer.
In Tech Terms (Still Simple):
You give the computer data (like pictures, numbers, or text).
You give it examples of the right answers (like “this is a dog”, “this is not a dog”).
It learns the patterns.
Later, when you give it new data, it makes a smart guess.
Few Common Uses of ML You See Every Day:
Netflix: Suggesting shows you might like.
Google Maps: Predicting traffic.
Amazon: Recommending products.
Banks: Detecting fraud in transactions.
I have curated the best interview resources to crack Data Science Interviews
👇👇
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like for more ❤️
Some important questions to crack data science interview
Q. Describe how Gradient Boosting works.
A. Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. If a small change in the prediction for a case causes no change in error, then next target outcome of the case is zero. Gradient boosting produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees.
Q. Describe the decision tree model.
A. Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter. The leaves are the decisions or the final outcomes. A decision tree is a machine learning algorithm that partitions the data into subsets.
Q. What is a neural network?
A. Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input. They, also known as Artificial Neural Networks, are the subset of Deep Learning.
Q. Explain the Bias-Variance Tradeoff
A. The bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.
Q. What’s the difference between L1 and L2 regularization?
A. The main intuitive difference between the L1 and L2 regularization is that L1 regularization tries to estimate the median of the data while the L2 regularization tries to estimate the mean of the data to avoid overfitting. That value will also be the median of the data distribution mathematically.
ENJOY LEARNING 👍👍
Guys, Big Announcement!
We’ve officially hit 5 Lakh followers on WhatsApp and it’s time to level up together! ❤️
I've launched a Python Learning Series — designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey — from basics to advanced — with real examples and short quizzes after each topic to help you lock in the concepts.
Here’s what we’ll cover in the coming days:
Week 1: Python Fundamentals
- Variables & Data Types
- Operators & Expressions
- Conditional Statements (if, elif, else)
- Loops (for, while)
- Functions & Parameters
- Input/Output & Basic Formatting
Week 2: Core Python Skills
- Lists, Tuples, Sets, Dictionaries
- String Manipulation
- List Comprehensions
- File Handling
- Exception Handling
Week 3: Intermediate Python
- Lambda Functions
- Map, Filter, Reduce
- Modules & Packages
- Scope & Global Variables
- Working with Dates & Time
Week 4: OOP & Pythonic Concepts
- Classes & Objects
- Inheritance & Polymorphism
- Decorators (Intro level)
- Generators & Iterators
- Writing Clean & Readable Code
Week 5: Real-World & Interview Prep
- Web Scraping (BeautifulSoup)
- Working with APIs (Requests)
- Automating Tasks
- Data Analysis Basics (Pandas)
- Interview Coding Patterns
You can join our WhatsApp channel to access it for free: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1527
𝗟𝗲𝗮𝗿𝗻 𝗖𝗼𝗱𝗶𝗻𝗴 𝗡𝗼𝘄, 𝗣𝗮𝘆 𝗔𝗳𝘁𝗲𝗿 𝗣𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁!😍
Learn Coding from Top Software Developers & Analytics from Top Data Scientists Working at Leading Tech Companies !🚀
Eligibility:- BTech / BCA / BSc
🌟 2000+ Students Placed
🤝 500+ Hiring Partners
💼 Avg. Rs. 7.4 LPA
🚀 41 LPA Highest Package
𝗙𝘂𝗹𝗹 𝗦𝘁𝗮𝗰𝗸:- https://pdlink.in/4hO7rWY
𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀:- https://bit.ly/4g3kyT6
Hurry, limited seats available!
Projects to boost your resume for data roles
Читать полностью…5 Algorithms you must know as a data scientist 👩💻 🧑💻
1. Dimensionality Reduction
- PCA, t-SNE, LDA
2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression
3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification
4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models
5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Join our WhatsApp channel: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content 😄👍
𝟱 𝗙𝗿𝗲𝗲 𝗠𝗜𝗧 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗧𝗵𝗮𝘁 𝗪𝗶𝗹𝗹 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿😍
📊 Want to Learn Data Analytics but Hate the High Price Tags?💰📌
Good news: MIT is offering free, high-quality data analytics courses through their OpenCourseWare platform💻🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4iXNfS3
All The Best 🎊