Seaborn Cheatsheet โ
โค6๐2
Data-Driven Decision Making
Data-driven decision-making (DDDM) involves using data analytics to guide business strategies instead of relying on intuition. Key techniques include A/B testing, forecasting, trend analysis, and KPI evaluation.
1๏ธโฃ A/B Testing & Hypothesis Testing
A/B testing compares two versions of a product, marketing campaign, or website feature to determine which performs better.
โ Key Metrics in A/B Testing:
Conversion Rate
Click-Through Rate (CTR)
Revenue per User
โ Steps in A/B Testing:
1. Define the hypothesis (e.g., "Changing the CTA button color will increase clicks").
2. Split users into Group A (control) and Group B (test).
3. Analyze differences using statistical tests.
โ SQL for A/B Testing:
Calculate average purchase per user in two test groups
Run a t-test to check statistical significance (Python)
๐น P-value < 0.05 โ Statistically significant difference.
๐น P-value > 0.05 โ No strong evidence of difference.
2๏ธโฃ Forecasting & Trend Analysis
Forecasting predicts future trends based on historical data.
โ Time Series Analysis Techniques:
Moving Averages (smooth trends)
Exponential Smoothing (weights recent data more)
ARIMA Models (AutoRegressive Integrated Moving Average)
โ SQL for Moving Averages:
7-day moving average of sales
โ Python for Forecasting (Using Prophet)
3๏ธโฃ KPI & Metrics Analysis
KPIs (Key Performance Indicators) measure business performance.
โ Common Business KPIs:
Revenue Growth Rate โ (Current Revenue - Previous Revenue) / Previous Revenue
Customer Retention Rate โ Customers at End / Customers at Start
Churn Rate โ % of customers lost over time
Net Promoter Score (NPS) โ Measures customer satisfaction
โ SQL for KPI Analysis:
Calculate Monthly Revenue Growth
โ Python for KPI Dashboard (Using Matplotlib)
4๏ธโฃ Real-Life Use Cases of Data-Driven Decisions
๐ E-commerce: Optimize pricing based on customer demand trends.
๐ Finance: Predict stock prices using time series forecasting.
๐ Marketing: Improve email campaign conversion rates with A/B testing.
๐ Healthcare: Identify disease patterns using predictive analytics.
Mini Task for You: Write an SQL query to calculate the customer churn rate for a subscription-based company.
Data Analyst Roadmap: ๐
https://t.iss.one/sqlspecialist/1159
Like this post if you want me to continue covering all the topics! โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Data-driven decision-making (DDDM) involves using data analytics to guide business strategies instead of relying on intuition. Key techniques include A/B testing, forecasting, trend analysis, and KPI evaluation.
1๏ธโฃ A/B Testing & Hypothesis Testing
A/B testing compares two versions of a product, marketing campaign, or website feature to determine which performs better.
โ Key Metrics in A/B Testing:
Conversion Rate
Click-Through Rate (CTR)
Revenue per User
โ Steps in A/B Testing:
1. Define the hypothesis (e.g., "Changing the CTA button color will increase clicks").
2. Split users into Group A (control) and Group B (test).
3. Analyze differences using statistical tests.
โ SQL for A/B Testing:
Calculate average purchase per user in two test groups
SELECT test_group, AVG(purchase_amount) AS avg_purchase
FROM ab_test_results
GROUP BY test_group;
Run a t-test to check statistical significance (Python)
from scipy.stats import ttest_ind
t_stat, p_value = ttest_ind(group_A['conversion_rate'], group_B['conversion_rate'])
print(f"T-statistic: {t_stat}, P-value: {p_value}")
๐น P-value < 0.05 โ Statistically significant difference.
๐น P-value > 0.05 โ No strong evidence of difference.
2๏ธโฃ Forecasting & Trend Analysis
Forecasting predicts future trends based on historical data.
โ Time Series Analysis Techniques:
Moving Averages (smooth trends)
Exponential Smoothing (weights recent data more)
ARIMA Models (AutoRegressive Integrated Moving Average)
โ SQL for Moving Averages:
7-day moving average of sales
SELECT order_date,
sales,
AVG(sales) OVER (ORDER BY order_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS moving_avg
FROM sales_data;
โ Python for Forecasting (Using Prophet)
from fbprophet import Prophet
model = Prophet()
model.fit(df)
future = model.make_future_dataframe(periods=30)
forecast = model.predict(future)
model.plot(forecast)
3๏ธโฃ KPI & Metrics Analysis
KPIs (Key Performance Indicators) measure business performance.
โ Common Business KPIs:
Revenue Growth Rate โ (Current Revenue - Previous Revenue) / Previous Revenue
Customer Retention Rate โ Customers at End / Customers at Start
Churn Rate โ % of customers lost over time
Net Promoter Score (NPS) โ Measures customer satisfaction
โ SQL for KPI Analysis:
Calculate Monthly Revenue Growth
SELECT month,
revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_month_revenue,
(revenue - prev_month_revenue) / prev_month_revenue * 100 AS growth_rate
FROM revenue_data;
โ Python for KPI Dashboard (Using Matplotlib)
import matplotlib.pyplot as plt
plt.plot(df['month'], df['revenue_growth'], marker='o')
plt.title('Monthly Revenue Growth')
plt.xlabel('Month')
plt.ylabel('Growth Rate (%)')
plt.show()
4๏ธโฃ Real-Life Use Cases of Data-Driven Decisions
๐ E-commerce: Optimize pricing based on customer demand trends.
๐ Finance: Predict stock prices using time series forecasting.
๐ Marketing: Improve email campaign conversion rates with A/B testing.
๐ Healthcare: Identify disease patterns using predictive analytics.
Mini Task for You: Write an SQL query to calculate the customer churn rate for a subscription-based company.
Data Analyst Roadmap: ๐
https://t.iss.one/sqlspecialist/1159
Like this post if you want me to continue covering all the topics! โค๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
๐4โค3
Breaking into Data Science doesnโt need to be complicated.
If youโre just starting out,
Hereโs how to simplify your approach:
Avoid:
๐ซ Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once.
๐ซ Spending months on theoretical concepts without hands-on practice.
๐ซ Overloading your resume with keywords instead of impactful projects.
๐ซ Believing you need a Ph.D. to break into the field.
Instead:
โ Start with Python or Rโfocus on mastering one language first.
โ Learn how to work with structured data (Excel or SQL) - this is your bread and butter.
โ Dive into a simple machine learning model (like linear regression) to understand the basics.
โ Solve real-world problems with open datasets and share them in a portfolio.
โ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests.
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content ๐๐
Hope this helps you ๐
#ai #datascience
If youโre just starting out,
Hereโs how to simplify your approach:
Avoid:
๐ซ Trying to learn every tool and library (Python, R, TensorFlow, Hadoop, etc.) all at once.
๐ซ Spending months on theoretical concepts without hands-on practice.
๐ซ Overloading your resume with keywords instead of impactful projects.
๐ซ Believing you need a Ph.D. to break into the field.
Instead:
โ Start with Python or Rโfocus on mastering one language first.
โ Learn how to work with structured data (Excel or SQL) - this is your bread and butter.
โ Dive into a simple machine learning model (like linear regression) to understand the basics.
โ Solve real-world problems with open datasets and share them in a portfolio.
โ Build a project that tells a story - why the problem matters, what you found, and what actions it suggests.
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Like if you need similar content ๐๐
Hope this helps you ๐
#ai #datascience
โค6๐1๐ฅฐ1
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.
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.
๐2
How to master Python from scratch๐
1. Setup and Basics ๐
- Install Python ๐ฅ๏ธ: Download Python and set it up.
- Hello, World! ๐: Write your first Hello World program.
2. Basic Syntax ๐
- Variables and Data Types ๐: Learn about strings, integers, floats, and booleans.
- Control Structures ๐: Understand if-else statements, for loops, and while loops.
- Functions ๐ ๏ธ: Write reusable blocks of code.
3. Data Structures ๐
- Lists ๐: Manage collections of items.
- Dictionaries ๐: Store key-value pairs.
- Tuples ๐ฆ: Work with immutable sequences.
- Sets ๐ข: Handle collections of unique items.
4. Modules and Packages ๐ฆ
- Standard Library ๐: Explore built-in modules.
- Third-Party Packages ๐: Install and use packages with pip.
5. File Handling ๐
- Read and Write Files ๐
- CSV and JSON ๐
6. Object-Oriented Programming ๐งฉ
- Classes and Objects ๐๏ธ
- Inheritance and Polymorphism ๐จโ๐ฉโ๐ง
7. Web Development ๐
- Flask ๐ผ: Start with a micro web framework.
- Django ๐ฆ: Dive into a full-fledged web framework.
8. Data Science and Machine Learning ๐ง
- NumPy ๐: Numerical operations.
- Pandas ๐ผ: Data manipulation and analysis.
- Matplotlib ๐ and Seaborn ๐: Data visualization.
- Scikit-learn ๐ค: Machine learning.
9. Automation and Scripting ๐ค
- Automate Tasks ๐ ๏ธ: Use Python to automate repetitive tasks.
- APIs ๐: Interact with web services.
10. Testing and Debugging ๐
- Unit Testing ๐งช: Write tests for your code.
- Debugging ๐: Learn to debug efficiently.
11. Advanced Topics ๐
- Concurrency and Parallelism ๐
- Decorators ๐ and Generators โ๏ธ
- Web Scraping ๐ธ๏ธ: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects ๐ก
- Calculator ๐งฎ
- To-Do List App ๐
- Weather App โ๏ธ
- Personal Blog ๐
13. Community and Collaboration ๐ค
- Contribute to Open Source ๐
- Join Coding Communities ๐ฌ
- Participate in Hackathons ๐
14. Keep Learning and Improving ๐
- Read Books ๐: Like "Automate the Boring Stuff with Python".
- Watch Tutorials ๐ฅ: Follow video courses and tutorials.
- Solve Challenges ๐งฉ: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge ๐ข
- Write Blogs โ๏ธ
- Create Video Tutorials ๐น
- Mentor Others ๐จโ๐ซ
I have curated the best interview resources to crack Python Interviews ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
1. Setup and Basics ๐
- Install Python ๐ฅ๏ธ: Download Python and set it up.
- Hello, World! ๐: Write your first Hello World program.
2. Basic Syntax ๐
- Variables and Data Types ๐: Learn about strings, integers, floats, and booleans.
- Control Structures ๐: Understand if-else statements, for loops, and while loops.
- Functions ๐ ๏ธ: Write reusable blocks of code.
3. Data Structures ๐
- Lists ๐: Manage collections of items.
- Dictionaries ๐: Store key-value pairs.
- Tuples ๐ฆ: Work with immutable sequences.
- Sets ๐ข: Handle collections of unique items.
4. Modules and Packages ๐ฆ
- Standard Library ๐: Explore built-in modules.
- Third-Party Packages ๐: Install and use packages with pip.
5. File Handling ๐
- Read and Write Files ๐
- CSV and JSON ๐
6. Object-Oriented Programming ๐งฉ
- Classes and Objects ๐๏ธ
- Inheritance and Polymorphism ๐จโ๐ฉโ๐ง
7. Web Development ๐
- Flask ๐ผ: Start with a micro web framework.
- Django ๐ฆ: Dive into a full-fledged web framework.
8. Data Science and Machine Learning ๐ง
- NumPy ๐: Numerical operations.
- Pandas ๐ผ: Data manipulation and analysis.
- Matplotlib ๐ and Seaborn ๐: Data visualization.
- Scikit-learn ๐ค: Machine learning.
9. Automation and Scripting ๐ค
- Automate Tasks ๐ ๏ธ: Use Python to automate repetitive tasks.
- APIs ๐: Interact with web services.
10. Testing and Debugging ๐
- Unit Testing ๐งช: Write tests for your code.
- Debugging ๐: Learn to debug efficiently.
11. Advanced Topics ๐
- Concurrency and Parallelism ๐
- Decorators ๐ and Generators โ๏ธ
- Web Scraping ๐ธ๏ธ: Extract data from websites using BeautifulSoup and Scrapy.
12. Practice Projects ๐ก
- Calculator ๐งฎ
- To-Do List App ๐
- Weather App โ๏ธ
- Personal Blog ๐
13. Community and Collaboration ๐ค
- Contribute to Open Source ๐
- Join Coding Communities ๐ฌ
- Participate in Hackathons ๐
14. Keep Learning and Improving ๐
- Read Books ๐: Like "Automate the Boring Stuff with Python".
- Watch Tutorials ๐ฅ: Follow video courses and tutorials.
- Solve Challenges ๐งฉ: On platforms like LeetCode, HackerRank, and CodeWars.
15. Teach and Share Knowledge ๐ข
- Write Blogs โ๏ธ
- Create Video Tutorials ๐น
- Mentor Others ๐จโ๐ซ
I have curated the best interview resources to crack Python Interviews ๐๐
https://topmate.io/coding/898340
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
๐5โค1
Essential Topics to Master Data Science Interviews: ๐
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule 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.
Show some โค๏ธ if you're ready to elevate your data science game! ๐
ENJOY LEARNING ๐๐
SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables
2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries
3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)
Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages
2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets
3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)
Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting
2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)
3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards
Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)
2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX
3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule 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.
Show some โค๏ธ if you're ready to elevate your data science game! ๐
ENJOY LEARNING ๐๐
๐7โค1
Guys, Big Announcement!
Weโve officially hit 5 Lakh followers on WhatsApp and itโs time to level up together! โค๏ธ
I've launched a Python Learning Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey โ from basics to advanced โ with real examples and short quizzes after each topic to help you lock in the concepts.
Hereโs what weโll 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
Weโve officially hit 5 Lakh followers on WhatsApp and itโs time to level up together! โค๏ธ
I've launched a Python Learning Series โ designed for beginners to those preparing for technical interviews or building real-world projects.
This will be a step-by-step journey โ from basics to advanced โ with real examples and short quizzes after each topic to help you lock in the concepts.
Hereโs what weโll 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
โค4๐1
4 Career Paths In Data Analytics
1) Data Analyst:
Role: Data Analysts interpret data and provide actionable insights through reports and visualizations.
They focus on querying databases, analyzing trends, and creating dashboards to help businesses make data-driven decisions.
Skills: Proficiency in SQL, Excel, data visualization tools (like Tableau or Power BI), and a good grasp of statistics.
Typical Tasks: Generating reports, creating visualizations, identifying trends and patterns, and presenting findings to stakeholders.
2)Data Scientist:
Role: Data Scientists use advanced statistical techniques, machine learning algorithms, and programming to analyze and interpret complex data.
They develop models to predict future trends and solve intricate problems.
Skills: Strong programming skills (Python, R), knowledge of machine learning, statistical analysis, data manipulation, and data visualization.
Typical Tasks: Building predictive models, performing complex data analyses, developing machine learning algorithms, and working with big data technologies.
3)Business Intelligence (BI) Analyst:
Role: BI Analysts focus on leveraging data to help businesses make strategic decisions.
They create and manage BI tools and systems, analyze business performance, and provide strategic recommendations.
Skills: Experience with BI tools (such as Power BI, Tableau, or Qlik), strong analytical skills, and knowledge of business operations and strategy.
Typical Tasks: Designing and maintaining dashboards and reports, analyzing business performance metrics, and providing insights for strategic planning.
4)Data Engineer:
Role: Data Engineers build and maintain the infrastructure required for data generation, storage, and processing. They ensure that data pipelines are efficient and reliable, and they prepare data for analysis.
Skills: Proficiency in programming languages (such as Python, Java, or Scala), experience with database management systems (SQL and NoSQL), and knowledge of data warehousing and ETL (Extract, Transform, Load) processes.
Typical Tasks: Designing and building data pipelines, managing and optimizing databases, ensuring data quality, and collaborating with data scientists and analysts.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
1) Data Analyst:
Role: Data Analysts interpret data and provide actionable insights through reports and visualizations.
They focus on querying databases, analyzing trends, and creating dashboards to help businesses make data-driven decisions.
Skills: Proficiency in SQL, Excel, data visualization tools (like Tableau or Power BI), and a good grasp of statistics.
Typical Tasks: Generating reports, creating visualizations, identifying trends and patterns, and presenting findings to stakeholders.
2)Data Scientist:
Role: Data Scientists use advanced statistical techniques, machine learning algorithms, and programming to analyze and interpret complex data.
They develop models to predict future trends and solve intricate problems.
Skills: Strong programming skills (Python, R), knowledge of machine learning, statistical analysis, data manipulation, and data visualization.
Typical Tasks: Building predictive models, performing complex data analyses, developing machine learning algorithms, and working with big data technologies.
3)Business Intelligence (BI) Analyst:
Role: BI Analysts focus on leveraging data to help businesses make strategic decisions.
They create and manage BI tools and systems, analyze business performance, and provide strategic recommendations.
Skills: Experience with BI tools (such as Power BI, Tableau, or Qlik), strong analytical skills, and knowledge of business operations and strategy.
Typical Tasks: Designing and maintaining dashboards and reports, analyzing business performance metrics, and providing insights for strategic planning.
4)Data Engineer:
Role: Data Engineers build and maintain the infrastructure required for data generation, storage, and processing. They ensure that data pipelines are efficient and reliable, and they prepare data for analysis.
Skills: Proficiency in programming languages (such as Python, Java, or Scala), experience with database management systems (SQL and NoSQL), and knowledge of data warehousing and ETL (Extract, Transform, Load) processes.
Typical Tasks: Designing and building data pipelines, managing and optimizing databases, ensuring data quality, and collaborating with data scientists and analysts.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02
Hope this helps you ๐
Have you ever thought about this?... ๐ค
When you think about the data scientist role, you probably think about AI and fancy machine learning models. And when you think about the data analyst role, you probably think about good-looking dashboards with plenty of features and insights.
Well, this all looks good until you land a job, and you quickly realize that you will spend probably 60-70% of your time doing something that is called DATA CLEANING... which I agree, itโs not the sexiest topic to talk about.
The thing is that logically, if we spend so much time preparing our data before creating a dashboard or a machine learning model, this means that data cleaning becomes arguably the number one skill for data specialists. And this is exactly why today we will start a series about the most important data cleaning techniques that you will use in the workplace.
So, here is why we need to clean our data ๐๐ป
1๏ธโฃ Precision in Analysis: Clean data minimizes errors and ensures accurate results, safeguarding the integrity of the analytical process.
2๏ธโฃ Maintaining Professional Credibility: The validity of your findings impacts your reputation in data science; unclean data can jeopardize your credibility.
3๏ธโฃ Optimizing Computational Efficiency: Well-formatted data streamlines analysis, akin to a decluttered workspace, making processes run faster, especially with advanced algorithms.
When you think about the data scientist role, you probably think about AI and fancy machine learning models. And when you think about the data analyst role, you probably think about good-looking dashboards with plenty of features and insights.
Well, this all looks good until you land a job, and you quickly realize that you will spend probably 60-70% of your time doing something that is called DATA CLEANING... which I agree, itโs not the sexiest topic to talk about.
The thing is that logically, if we spend so much time preparing our data before creating a dashboard or a machine learning model, this means that data cleaning becomes arguably the number one skill for data specialists. And this is exactly why today we will start a series about the most important data cleaning techniques that you will use in the workplace.
So, here is why we need to clean our data ๐๐ป
1๏ธโฃ Precision in Analysis: Clean data minimizes errors and ensures accurate results, safeguarding the integrity of the analytical process.
2๏ธโฃ Maintaining Professional Credibility: The validity of your findings impacts your reputation in data science; unclean data can jeopardize your credibility.
3๏ธโฃ Optimizing Computational Efficiency: Well-formatted data streamlines analysis, akin to a decluttered workspace, making processes run faster, especially with advanced algorithms.
๐5
Product team cases where a #productteams improved content discovery
Case: Netflix and Personalized Content Recommendations
Problem: Netflix wanted to improve user engagement by enhancing content discovery and reducing churn.
Solution: Using a product outcome mindset, Netflix's product team developed a recommendation algorithm that analyzed user viewing behavior and preferences to offer personalized content suggestions.
Outcome: Netflix saw a significant increase in user engagement, with the personalized recommendations leading to higher watch times and reduced churn.
Learn more: You can read about Netflix's recommendation system in various articles and research papers, such as "Netflix Recommendations: Beyond the 5 stars" (by Netflix).
Case: Spotify and Music Discovery
Problem: Spotify users were overwhelmed by the vast music library and struggled to discover new music.
Solution: Spotify's product team used data-driven insights to create personalized playlists like "Discover Weekly" and "Release Radar," tailored to users' listening habits.
Outcome: The personalized playlists increased user engagement, time spent on the platform, and the likelihood of users discovering and enjoying new music.
Link: Learn more about Spotify's approach to music discovery in articles like "How Spotify Discover Weekly and Release Radar Playlist Work" (by The Verge).
Case: Netflix and Personalized Content Recommendations
Problem: Netflix wanted to improve user engagement by enhancing content discovery and reducing churn.
Solution: Using a product outcome mindset, Netflix's product team developed a recommendation algorithm that analyzed user viewing behavior and preferences to offer personalized content suggestions.
Outcome: Netflix saw a significant increase in user engagement, with the personalized recommendations leading to higher watch times and reduced churn.
Learn more: You can read about Netflix's recommendation system in various articles and research papers, such as "Netflix Recommendations: Beyond the 5 stars" (by Netflix).
Case: Spotify and Music Discovery
Problem: Spotify users were overwhelmed by the vast music library and struggled to discover new music.
Solution: Spotify's product team used data-driven insights to create personalized playlists like "Discover Weekly" and "Release Radar," tailored to users' listening habits.
Outcome: The personalized playlists increased user engagement, time spent on the platform, and the likelihood of users discovering and enjoying new music.
Link: Learn more about Spotify's approach to music discovery in articles like "How Spotify Discover Weekly and Release Radar Playlist Work" (by The Verge).
๐1
๐ฐ How to become a data scientist in 2025?
๐จ๐ปโ๐ป If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
๐ข Step 1: Strengthen your math and statistics!
โ๏ธ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
โ Linear algebra: matrices, vectors, eigenvalues.
๐ Course: MIT 18.06 Linear Algebra
โ Calculus: derivative, integral, optimization.
๐ Course: MIT Single Variable Calculus
โ Statistics and probability: Bayes' theorem, hypothesis testing.
๐ Course: Statistics 110
โโโโโ
๐ข Step 2: Learn to code.
โ๏ธ Learn Python and become proficient in coding. The most important topics you need to master are:
โ Python: Pandas, NumPy, Matplotlib libraries
๐ Course: FreeCodeCamp Python Course
โ SQL language: Join commands, Window functions, query optimization.
๐ Course: Stanford SQL Course
โ Data structures and algorithms: arrays, linked lists, trees.
๐ Course: MIT Introduction to Algorithms
โโโโโ
๐ข Step 3: Clean and visualize data
โ๏ธ Learn how to process and clean data and then create an engaging story from it!
โ Data cleaning: Working with missing values โโand detecting outliers.
๐ Course: Data Cleaning
โ Data visualization: Matplotlib, Seaborn, Tableau
๐ Course: Data Visualization Tutorial
โโโโโ
๐ข Step 4: Learn Machine Learning
โ๏ธ It's time to enter the exciting world of machine learning! You should know these topics:
โ Supervised learning: regression, classification.
โ Unsupervised learning: clustering, PCA, anomaly detection.
โ Deep learning: neural networks, CNN, RNN
๐ Course: CS229: Machine Learning
โโโโโ
๐ข Step 5: Working with Big Data and Cloud Technologies
โ๏ธ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
โ Big Data Tools: Hadoop, Spark, Dask
โ Cloud platforms: AWS, GCP, Azure
๐ Course: Data Engineering
โโโโโ
๐ข Step 6: Do real projects!
โ๏ธ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
โ Kaggle competitions: solving real-world challenges.
โ End-to-End projects: data collection, modeling, implementation.
โ GitHub: Publish your projects on GitHub.
๐ Platform: Kaggle๐ Platform: ods.ai
โโโโโ
๐ข Step 7: Learn MLOps and deploy models
โ๏ธ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
โ MLOps training: model versioning, monitoring, model retraining.
โ Deployment models: Flask, FastAPI, Docker
๐ Course: Stanford MLOps Course
โโโโโ
๐ข Step 8: Stay up to date and network
โ๏ธ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
โ Read scientific articles: arXiv, Google Scholar
โ Connect with the data community:
๐ Site: Papers with code
๐ Site: AI Research at Google
๐จ๐ปโ๐ป If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
๐ข Step 1: Strengthen your math and statistics!
โ๏ธ The foundation of learning data science is mathematics, linear algebra, statistics, and probability. Topics you should master:
โ Linear algebra: matrices, vectors, eigenvalues.
๐ Course: MIT 18.06 Linear Algebra
โ Calculus: derivative, integral, optimization.
๐ Course: MIT Single Variable Calculus
โ Statistics and probability: Bayes' theorem, hypothesis testing.
๐ Course: Statistics 110
โโโโโ
๐ข Step 2: Learn to code.
โ๏ธ Learn Python and become proficient in coding. The most important topics you need to master are:
โ Python: Pandas, NumPy, Matplotlib libraries
๐ Course: FreeCodeCamp Python Course
โ SQL language: Join commands, Window functions, query optimization.
๐ Course: Stanford SQL Course
โ Data structures and algorithms: arrays, linked lists, trees.
๐ Course: MIT Introduction to Algorithms
โโโโโ
๐ข Step 3: Clean and visualize data
โ๏ธ Learn how to process and clean data and then create an engaging story from it!
โ Data cleaning: Working with missing values โโand detecting outliers.
๐ Course: Data Cleaning
โ Data visualization: Matplotlib, Seaborn, Tableau
๐ Course: Data Visualization Tutorial
โโโโโ
๐ข Step 4: Learn Machine Learning
โ๏ธ It's time to enter the exciting world of machine learning! You should know these topics:
โ Supervised learning: regression, classification.
โ Unsupervised learning: clustering, PCA, anomaly detection.
โ Deep learning: neural networks, CNN, RNN
๐ Course: CS229: Machine Learning
โโโโโ
๐ข Step 5: Working with Big Data and Cloud Technologies
โ๏ธ If you're going to work in the real world, you need to know how to work with Big Data and cloud computing.
โ Big Data Tools: Hadoop, Spark, Dask
โ Cloud platforms: AWS, GCP, Azure
๐ Course: Data Engineering
โโโโโ
๐ข Step 6: Do real projects!
โ๏ธ Enough theory, it's time to get coding! Do real projects and build a strong portfolio.
โ Kaggle competitions: solving real-world challenges.
โ End-to-End projects: data collection, modeling, implementation.
โ GitHub: Publish your projects on GitHub.
๐ Platform: Kaggle๐ Platform: ods.ai
โโโโโ
๐ข Step 7: Learn MLOps and deploy models
โ๏ธ Machine learning is not just about building a model! You need to learn how to deploy and monitor a model.
โ MLOps training: model versioning, monitoring, model retraining.
โ Deployment models: Flask, FastAPI, Docker
๐ Course: Stanford MLOps Course
โโโโโ
๐ข Step 8: Stay up to date and network
โ๏ธ Data science is changing every day, so it is necessary to update yourself every day and stay in regular contact with experienced people and experts in this field.
โ Read scientific articles: arXiv, Google Scholar
โ Connect with the data community:
๐ Site: Papers with code
๐ Site: AI Research at Google
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #data
๐2
If you want to get a job as a machine learning engineer, donโt start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
๐๐๐ญ๐ก๐๐ฆ๐๐ญ๐ข๐๐ฌ ๐๐ง๐ ๐๐ญ๐๐ญ๐ข๐ฌ๐ญ๐ข๐๐ฌ - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
๐๐ข๐ง๐๐๐ซ ๐๐ฅ๐ ๐๐๐ซ๐ ๐๐ง๐ ๐๐๐ฅ๐๐ฎ๐ฅ๐ฎ๐ฌ - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
๐๐ซ๐จ๐ ๐ซ๐๐ฆ๐ฆ๐ข๐ง๐ - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ ๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
๐๐๐ฉ๐ฅ๐จ๐ฒ๐ฆ๐๐ง๐ญ ๐๐ง๐ ๐๐ซ๐จ๐๐ฎ๐๐ญ๐ข๐จ๐ง:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
๐๐ฅ๐จ๐ฎ๐ ๐๐จ๐ฆ๐ฉ๐ฎ๐ญ๐ข๐ง๐ ๐๐ง๐ ๐๐ข๐ ๐๐๐ญ๐:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best ๐๐
๐4
FREE DATASET BUILDING YOUR PORTFOLIO โญ
1. Supermarket Sales - https://lnkd.in/e86UpCMv
2.Credit Card Fraud Detection - https://lnkd.in/eFTsZDCW
3. FIFA 22 complete player dataset - https://lnkd.in/eDScdUUM
4. Walmart Store Sales Forecasting - https://lnkd.in/eVT6h-CT
5. Netflix Movies and TV Shows - https://lnkd.in/eZ3cduwK
6.LinkedIn Data Analyst jobs listings - https://lnkd.in/ezqxcmrE
7. Top 50 Fast-Food Chains in USA - https://lnkd.in/esBjf5u4
8. Amazon and Best Buy Electronics - https://lnkd.in/e4fBZvJ3
9. Forecasting Book Sales - https://lnkd.in/eXHN2XsQ
10. Real / Fake Job Posting Prediction - https://lnkd.in/e5SDDW9G
Join for more: https://t.iss.one/DataPortfolio
Hope it helps:)
1. Supermarket Sales - https://lnkd.in/e86UpCMv
2.Credit Card Fraud Detection - https://lnkd.in/eFTsZDCW
3. FIFA 22 complete player dataset - https://lnkd.in/eDScdUUM
4. Walmart Store Sales Forecasting - https://lnkd.in/eVT6h-CT
5. Netflix Movies and TV Shows - https://lnkd.in/eZ3cduwK
6.LinkedIn Data Analyst jobs listings - https://lnkd.in/ezqxcmrE
7. Top 50 Fast-Food Chains in USA - https://lnkd.in/esBjf5u4
8. Amazon and Best Buy Electronics - https://lnkd.in/e4fBZvJ3
9. Forecasting Book Sales - https://lnkd.in/eXHN2XsQ
10. Real / Fake Job Posting Prediction - https://lnkd.in/e5SDDW9G
Join for more: https://t.iss.one/DataPortfolio
Hope it helps:)
โค2๐2๐คฃ1
Guys, Big Announcement! ๐
We've officially hit 3 Lakh subscribers on WhatsAppโ and it's time to kick off the next big learning journey together! ๐คฉ
Artificial Intelligence Complete Series โ a comprehensive, step-by-step journey from scratch to real-world applications. Whether you're a complete beginner or looking to take your AI skills to the next level, this series has got you covered!
This series is packed with real-world examples, hands-on projects, and tips to understand how AI impacts our world.
Hereโs what weโll cover:
*Week 1: Introduction to AI*
- What is AI? Understanding the basics without the jargon
- Types of AI: Narrow vs. General AI
- Key AI concepts (Machine Learning, Deep Learning, and Neural Networks)
- Real-world applications: From Chatbots to Self-Driving Cars ๐
- Tools & frameworks for AI (TensorFlow, Keras, PyTorch)
*Week 2: Core AI Techniques*
- Supervised vs. Unsupervised Learning
- Understanding Data: The backbone of AI
- Linear Regression: Your first AI algorithm!
- Decision Trees, K-Nearest Neighbors, and Support Vector Machines
- Hands-on project: Building a basic classifier with Python ๐
*Week 3: Deep Dive into Machine Learning*
- What makes ML different from AI?
- Gradient Descent & Model Optimization
- Evaluating Models: Accuracy, Precision, Recall, and F1-Score
- Hyperparameter Tuning
- Hands-on project: Building a predictive model with real data ๐
*Week 4: Introduction to Neural Networks*
- The fundamentals of neural networks & deep learning
- Understanding how a neural network mimics the human brain ๐ง
- Training your first Neural Network with TensorFlow
- Introduction to Backpropagation and Activation Functions
- Hands-on project: Build a simple neural network to recognize images ๐ธ
*Week 5: Advanced AI Concepts*
- Natural Language Processing (NLP): Teach machines to understand text and speech ๐ฃ๏ธ
- Computer Vision: Teaching machines to "see" with Convolutional Neural Networks (CNNs)
- Reinforcement Learning: AI that learns through trial and error (think AlphaGo)
- Real-world AI Use Cases: Healthcare, Finance, Gaming, and more
- Hands-on project: Implementing NLP for text classification ๐
*Week 6: Building Real-World AI Applications*
- AI in the real world: Chatbots, Recommendation Systems, and Fraud Detection
- Integrating AI with APIs and Web Services
- Cloud AI: Using AWS, Google Cloud, and Azure for scaling AI projects
- Hands-on project: Build a recommendation system like Netflix ๐ฌ
*Week 7: Preparing for AI Careers*
- Common interview questions for AI & ML roles ๐
- Building an AI Portfolio: Showcase your projects
- Understanding AI in Industry: How itโs transforming businesses
- Networking and building your career in AI ๐
Join our WhatsApp channel to access it for FREE: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y/1031
We've officially hit 3 Lakh subscribers on WhatsAppโ and it's time to kick off the next big learning journey together! ๐คฉ
Artificial Intelligence Complete Series โ a comprehensive, step-by-step journey from scratch to real-world applications. Whether you're a complete beginner or looking to take your AI skills to the next level, this series has got you covered!
This series is packed with real-world examples, hands-on projects, and tips to understand how AI impacts our world.
Hereโs what weโll cover:
*Week 1: Introduction to AI*
- What is AI? Understanding the basics without the jargon
- Types of AI: Narrow vs. General AI
- Key AI concepts (Machine Learning, Deep Learning, and Neural Networks)
- Real-world applications: From Chatbots to Self-Driving Cars ๐
- Tools & frameworks for AI (TensorFlow, Keras, PyTorch)
*Week 2: Core AI Techniques*
- Supervised vs. Unsupervised Learning
- Understanding Data: The backbone of AI
- Linear Regression: Your first AI algorithm!
- Decision Trees, K-Nearest Neighbors, and Support Vector Machines
- Hands-on project: Building a basic classifier with Python ๐
*Week 3: Deep Dive into Machine Learning*
- What makes ML different from AI?
- Gradient Descent & Model Optimization
- Evaluating Models: Accuracy, Precision, Recall, and F1-Score
- Hyperparameter Tuning
- Hands-on project: Building a predictive model with real data ๐
*Week 4: Introduction to Neural Networks*
- The fundamentals of neural networks & deep learning
- Understanding how a neural network mimics the human brain ๐ง
- Training your first Neural Network with TensorFlow
- Introduction to Backpropagation and Activation Functions
- Hands-on project: Build a simple neural network to recognize images ๐ธ
*Week 5: Advanced AI Concepts*
- Natural Language Processing (NLP): Teach machines to understand text and speech ๐ฃ๏ธ
- Computer Vision: Teaching machines to "see" with Convolutional Neural Networks (CNNs)
- Reinforcement Learning: AI that learns through trial and error (think AlphaGo)
- Real-world AI Use Cases: Healthcare, Finance, Gaming, and more
- Hands-on project: Implementing NLP for text classification ๐
*Week 6: Building Real-World AI Applications*
- AI in the real world: Chatbots, Recommendation Systems, and Fraud Detection
- Integrating AI with APIs and Web Services
- Cloud AI: Using AWS, Google Cloud, and Azure for scaling AI projects
- Hands-on project: Build a recommendation system like Netflix ๐ฌ
*Week 7: Preparing for AI Careers*
- Common interview questions for AI & ML roles ๐
- Building an AI Portfolio: Showcase your projects
- Understanding AI in Industry: How itโs transforming businesses
- Networking and building your career in AI ๐
Join our WhatsApp channel to access it for FREE: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y/1031
โค4๐1