Machine Learning Roadmap

𝟲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝗙𝗿𝗼𝗺 𝗧𝗼𝗽 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 😍

A power-packed selection of 100% free, certified courses from top institutions:

- Data Analytics – Cisco
- Digital Marketing – Google
- Python for AI – IBM/edX
- SQL & Databases – Stanford
- Generative AI – Google Cloud
- Machine Learning – Harvard

𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 
 
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Master in‑demand tech skills with these 6 certified, top-tier free courses

As a data analyst, your focus isn't on creating dashboards, writing SQL queries, doing pivot tables, generating reports, or cleaning data.

Your focus should be solving business problems using these skills

- Don’t just write SQL—ask why you're querying that data and what decision it will influence.

- Don’t just build a dashboard—ask who will use it and how it will help them take action.

- Don’t just clean data—know what insight lies beneath the mess.

- Don’t just report metrics—ask what story they’re telling and what recommendation can follow.

🚀 𝟳 𝗙𝗿𝗲𝗲 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 + 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗖𝗮𝗿𝗲𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟱 😍

Gain globally recognized skills with Microsoft x LinkedIn Career Essentials – completely FREE!

🎯 Top Certifications:
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🔹 Software Development
🔹 Project Management
🔹 Business Analysis
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𝗘𝗻𝗿𝗼𝗹𝗹 𝗙𝗼𝗿 𝗙𝗥𝗘𝗘👇:- 
 
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💼 Perfect for students, freshers & working professionals

𝗧𝗶𝗿𝗲𝗱 𝗼𝗳 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘁𝗼 𝗳𝗶𝗻𝗱 𝗴𝗼𝗼𝗱 𝗔𝗜/𝗠𝗟 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲?😍

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𝐋𝐢𝐧𝐤👇:-

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Use Chat GPT to prepare for your next Interview

This could be the most helpful thing for people aspiring for new jobs.

A few prompts that can help you here are:

💡Prompt 1: Here is a Job denoscription of a job I am looking to apply for. Can you tell me what skills and questions should I prepare for? {Paste JD}

💡Prompt 2: Here is my resume. Can you tell me what optimization I can do to make it more likely to get selected for this interview? {Paste Resume in text}

💡Prompt 3: Act as an Interviewer for the role of a {product manager} at {Company}. Ask me 5 questions one by one, wait for my response, and then tell me how I did. You should give feedback in the following format: What was good, where are the gaps, and how to address the gaps?

💡Prompt 4: I am interviewing for this job given in the JD. Can you help me understand the company, its role, its products, main competitors, and challenges for the company?

💡Prompt 5: What are the few questions I should ask at the end of the interview which can help me learn about the culture of the company?

Free book to master ChatGPT: https://news.1rj.ru/str/InterviewBooks/166

ENJOY LEARNING 👍👍

𝟱 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗧𝗲𝗰𝗵 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 – 𝗪𝗶𝘁𝗵 𝗙𝘂𝗹𝗹 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹𝘀!😍

Are you ready to build real-world tech projects that don’t just look good on your resume, but actually teach you practical, job-ready skills?🧑‍💻📌

Here’s a curated list of 5 high-value development tutorials — covering everything from full-stack development and real-time chat apps to AI form builders and reinforcement learning✨️💻

𝐋𝐢𝐧𝐤👇:-

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They’re real, portfolio-worthy projects you can start today✅️

Complete Syllabus for Data Analytics interview:

SQL:
1. Basic
  - SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
  - Basic JOINS (INNER, LEFT, RIGHT, FULL)
  - Creating and using simple databases and tables

2. Intermediate
  - Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
  - Subqueries and nested queries
  - Common Table Expressions (WITH clause)
  - CASE statements for conditional logic in queries

3. Advanced
  - Advanced JOIN techniques (self-join, non-equi join)
  - Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
  - optimization with indexing
  - Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Basic
  - Syntax, variables, data types (integers, floats, strings, booleans)
  - Control structures (if-else, for and while loops)
  - Basic data structures (lists, dictionaries, sets, tuples)
  - Functions, lambda functions, error handling (try-except)
  - Modules and packages

2. Pandas & Numpy
  - Creating and manipulating DataFrames and Series
  - Indexing, selecting, and filtering data
  - Handling missing data (fillna, dropna)
  - Data aggregation with groupby, summarizing data
  - Merging, joining, and concatenating datasets

3. Basic Visualization
  - Basic plotting with Matplotlib (line plots, bar plots, histograms)
  - Visualization with Seaborn (scatter plots, box plots, pair plots)
  - Customizing plots (sizes, labels, legends, color palettes)
  - Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Basic
  - Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
  - Introduction to charts and basic data visualization
  - Data sorting and filtering
  - Conditional formatting

2. Intermediate
  - Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
  - PivotTables and PivotCharts for summarizing data
  - Data validation tools
  - What-if analysis tools (Data Tables, Goal Seek)

3. Advanced
  - Array formulas and advanced functions
  - Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables
  - Dynamic charts and interactive dashboards

Power BI:
1. Data Modeling
  - Importing data from various sources
  - Creating and managing relationships between different datasets
  - Data modeling basics (star schema, snowflake schema)

2. Data Transformation
  - Using Power Query for data cleaning and transformation
  - Advanced data shaping techniques
  - Calculated columns and measures using DAX

3. Data Visualization and Reporting
  - Creating interactive reports and dashboards
  - Visualizations (bar, line, pie charts, maps)
  - Publishing and sharing reports, scheduling data refreshes

Statistics Fundamentals:
Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.

Essential Programming Languages to Learn Data Science 👇👇

1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).

2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.

3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.

4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.

5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.

6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.

7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.

Free Resources to master data analytics concepts 👇👇

Data Analysis with R

Intro to Data Science

Practical Python Programming

SQL for Data Analysis

Java Essential Concepts

Machine Learning with Python

Data Science Project Ideas

Learning SQL FREE Book

Join @free4unow_backup for more free resources.

ENJOY LEARNING👍👍

Best way to prepare for a SQL interviews 👇👇

1. Review Basic Concepts: Ensure you understand fundamental SQL concepts like SELECT statements, JOINs, GROUP BY, and WHERE clauses.

2. Practice SQL Queries: Work on writing and executing SQL queries. Practice retrieving, updating, and deleting data.

3. Understand Database Design: Learn about normalization, indexes, and relationships to comprehend how databases are structured.

4. Know Your Database: If possible, find out which database system the company uses (e.g., MySQL, PostgreSQL, SQL Server) and familiarize yourself with its specific syntax.

5. Data Types and Constraints: Understand various data types and constraints such as PRIMARY KEY, FOREIGN KEY, and UNIQUE constraints.

6. Stored Procedures and Functions: Learn about stored procedures and functions, as interviewers may inquire about these.

7. Data Manipulation Language (DML): Be familiar with INSERT, UPDATE, and DELETE statements.

8. Data Definition Language (DDL): Understand statements like CREATE, ALTER, and DROP for database and table management.

9. Normalization and Optimization: Brush up on database normalization and optimization techniques to demonstrate your understanding of efficient database design.

10. Troubleshooting Skills: Be prepared to troubleshoot queries, identify errors, and optimize poorly performing queries.

11. Scenario-Based Questions: Practice answering scenario-based questions. Understand how to approach problems and design solutions.

12. Latest Trends: Stay updated on the latest trends in database technologies and SQL best practices.

13. Review Resume Projects: If you have projects involving SQL on your resume, be ready to discuss them in detail.

14. Mock Interviews: Conduct mock interviews with a friend or use online platforms to simulate real interview scenarios.

15. Ask Questions: Prepare questions to ask the interviewer about the company's use of databases and SQL.

Best Resources to learn SQL 👇

SQL Topics for Data Analysts

SQL Udacity Course

Download SQL Cheatsheet

SQL Interview Questions

Learn & Practice SQL

Also try to apply what you learn through hands-on projects or challenges.

Please give us credits while sharing: -> https://news.1rj.ru/str/free4unow_backup

ENJOY LEARNING 👍👍

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

Basically, there are 3 different layers in a neural network :

Input Layer (All the inputs are fed in the model through this layer)

Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)

Output Layer (The data after processing is made available at the output layer)

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

Common Machine Learning Algorithms!

1️⃣ Linear Regression
->Used for predicting continuous values.
->Models the relationship between dependent and independent variables by fitting a linear equation.

2️⃣ Logistic Regression
->Ideal for binary classification problems.
->Estimates the probability that an instance belongs to a particular class.

3️⃣ Decision Trees
->Splits data into subsets based on the value of input features.
->Easy to visualize and interpret but can be prone to overfitting.

4️⃣ Random Forest
->An ensemble method using multiple decision trees.
->Reduces overfitting and improves accuracy by averaging multiple trees.

5️⃣ Support Vector Machines (SVM)
->Finds the hyperplane that best separates different classes.
->Effective in high-dimensional spaces and for classification tasks.

6️⃣ k-Nearest Neighbors (k-NN)
->Classifies data based on the majority class among the k-nearest neighbors.
->Simple and intuitive but can be computationally intensive.

7️⃣ K-Means Clustering
->Partitions data into k clusters based on feature similarity.
->Useful for market segmentation, image compression, and more.

8️⃣ Naive Bayes
->Based on Bayes' theorem with an assumption of independence among predictors.
->Particularly useful for text classification and spam filtering.

9️⃣ Neural Networks
->Mimic the human brain to identify patterns in data.
->Power deep learning applications, from image recognition to natural language processing.

🔟 Gradient Boosting Machines (GBM)
->Combines weak learners to create a strong predictive model.
->Used in various applications like ranking, classification, and regression.

Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y

ENJOY LEARNING 👍👍

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

9 tips to get started with Data Analysis:

Learn Excel, SQL, and a programming language (Python or R)

Understand basic statistics and probability

Practice with real-world datasets (Kaggle, Data.gov)

Clean and preprocess data effectively

Visualize data using charts and graphs

Ask the right questions before diving into data

Use libraries like Pandas, NumPy, and Matplotlib

Focus on storytelling with data insights

Build small projects to apply what you learn

Data Science & Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Top 10 machine Learning algorithms for beginners 👇👇

1. Linear Regression: A simple algorithm used for predicting a continuous value based on one or more input features.

2. Logistic Regression: Used for binary classification problems, where the output is a binary value (0 or 1).

3. Decision Trees: A versatile algorithm that can be used for both classification and regression tasks, based on a tree-like structure of decisions.

4. Random Forest: An ensemble learning method that combines multiple decision trees to improve the accuracy and robustness of the model.

5. Support Vector Machines (SVM): Used for both classification and regression tasks, with the goal of finding the hyperplane that best separates the classes.

6. K-Nearest Neighbors (KNN): A simple algorithm that classifies a new data point based on the majority class of its k nearest neighbors in the feature space.

7. Naive Bayes: A probabilistic algorithm based on Bayes' theorem that is commonly used for text classification and spam filtering.

8. K-Means Clustering: An unsupervised learning algorithm used for clustering data points into k distinct groups based on similarity.

9. Principal Component Analysis (PCA): A dimensionality reduction technique used to reduce the number of features in a dataset while preserving the most important information.

10. Gradient Boosting Machines (GBM): An ensemble learning method that builds a series of weak learners to create a strong predictive model through iterative optimization.

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

Credits: https://news.1rj.ru/str/datasciencefun

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🔍 𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐈𝐓 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 🔍

The world of data is vast and diverse, and understanding the nuances between different data roles can help both professionals and organizations thrive.

This visual breakdown offers a fantastic comparison of key data roles:

💚 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 – The backbone of any data-driven team. They build robust data pipelines, manage infrastructure, and ensure data is accessible and reliable. Strong in deployment, ML-Ops, and working closely with Data Scientists.

💜 𝐌𝐋 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 – These experts bridge software engineering and data science. They focus on building and deploying machine learning models at scale, emphasizing ML Ops, experimentation, and data analysis.

❤️ 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 – The creative problem solvers. They blend statistical analysis, machine learning, and storytelling to uncover insights and predict future trends. Skilled in experimentation, ML modeling, and storytelling.

💛 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 – Their strengths lie in reporting, business insights, and visualization.

5 Essential Portfolio Projects for data analysts 😄👇

1. Exploratory Data Analysis (EDA) on a Real Dataset: Choose a dataset related to your interests, perform thorough EDA, visualize trends, and draw insights. This showcases your ability to understand data and derive meaningful conclusions.
Free websites to find datasets: https://news.1rj.ru/str/DataPortfolio/8

2. Predictive Modeling Project: Build a predictive model, such as a linear regression or classification model. Use a dataset to train and test your model, and evaluate its performance. Highlight your skills in machine learning and statistical analysis.

3. Data Cleaning and Transformation: Take a messy dataset and demonstrate your skills in cleaning and transforming data. Showcase your ability to handle missing values, outliers, and prepare data for analysis.

4. Dashboard Creation: Utilize tools like Tableau or Power BI to create an interactive dashboard. This project demonstrates your ability to present data insights in a visually appealing and user-friendly manner.

5. Time Series Analysis: Work with time-series data to forecast future trends. This could involve stock prices, weather data, or any other time-dependent dataset. Showcase your understanding of time-series concepts and forecasting techniques.

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