๐๐ฒ๐ฎ๐ฟ๐ป ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐ (๐ก๐ผ ๐ฆ๐๐ฟ๐ถ๐ป๐ด๐ ๐๐๐๐ฎ๐ฐ๐ต๐ฒ๐ฑ)
๐ก๐ผ ๐ณ๐ฎ๐ป๐ฐ๐ ๐ฐ๐ผ๐๐ฟ๐๐ฒ๐, ๐ป๐ผ ๐ฐ๐ผ๐ป๐ฑ๐ถ๐๐ถ๐ผ๐ป๐, ๐ท๐๐๐ ๐ฝ๐๐ฟ๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด.
๐๐ฒ๐ฟ๐ฒโ๐ ๐ต๐ผ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐:
1๏ธโฃ Python Programming for Data Science โ Harvardโs CS50P
The best intro to Python for absolute beginners:
โฌ Covers loops, data structures, and practical exercises.
โฌ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://t.iss.one/datasciencefun
2๏ธโฃ Statistics & Probability โ Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
โฌ Clear, beginner-friendly videos.
โฌ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3๏ธโฃ Linear Algebra for Data Science โ 3Blue1Brown
โฌ Learn about matrices, vectors, and transformations.
โฌ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4๏ธโฃ SQL Basics โ Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
โฌ Writing queries, joins, and filtering data.
โฌ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5๏ธโฃ Data Visualization โ freeCodeCamp
Learn to create stunning visualizations using Python libraries:
โฌ Covers Matplotlib, Seaborn, and Plotly.
โฌ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6๏ธโฃ Machine Learning Basics โ Googleโs Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
โฌ Learn supervised and unsupervised learning.
โฌ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7๏ธโฃ Deep Learning โ Fast.aiโs Free Course
Fast.ai makes deep learning easy and accessible:
โฌ Build neural networks with PyTorch.
โฌ Learn by coding real projects.
Link: https://course.fast.ai/
8๏ธโฃ Data Science Projects โ Kaggle
โฌ Compete in challenges to practice your skills.
โฌ Great way to build your portfolio.
Link: https://www.kaggle.com/
๐ก๐ผ ๐ณ๐ฎ๐ป๐ฐ๐ ๐ฐ๐ผ๐๐ฟ๐๐ฒ๐, ๐ป๐ผ ๐ฐ๐ผ๐ป๐ฑ๐ถ๐๐ถ๐ผ๐ป๐, ๐ท๐๐๐ ๐ฝ๐๐ฟ๐ฒ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด.
๐๐ฒ๐ฟ๐ฒโ๐ ๐ต๐ผ๐ ๐๐ผ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฎ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐๐ ๐ณ๐ผ๐ฟ ๐๐ฅ๐๐:
1๏ธโฃ Python Programming for Data Science โ Harvardโs CS50P
The best intro to Python for absolute beginners:
โฌ Covers loops, data structures, and practical exercises.
โฌ Designed to help you build foundational coding skills.
Link: https://cs50.harvard.edu/python/
https://t.iss.one/datasciencefun
2๏ธโฃ Statistics & Probability โ Khan Academy
Want to master probability, distributions, and hypothesis testing? This is where to start:
โฌ Clear, beginner-friendly videos.
โฌ Exercises to test your skills.
Link: https://www.khanacademy.org/math/statistics-probability
https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O
3๏ธโฃ Linear Algebra for Data Science โ 3Blue1Brown
โฌ Learn about matrices, vectors, and transformations.
โฌ Essential for machine learning models.
Link: https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9KzVk3AjplI5PYPxkUr
4๏ธโฃ SQL Basics โ Mode Analytics
SQL is the backbone of data manipulation. This tutorial covers:
โฌ Writing queries, joins, and filtering data.
โฌ Real-world datasets to practice.
Link: https://mode.com/sql-tutorial
https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v
5๏ธโฃ Data Visualization โ freeCodeCamp
Learn to create stunning visualizations using Python libraries:
โฌ Covers Matplotlib, Seaborn, and Plotly.
โฌ Step-by-step projects included.
Link: https://www.youtube.com/watch?v=JLzTJhC2DZg
https://whatsapp.com/channel/0029VaxaFzoEQIaujB31SO34
6๏ธโฃ Machine Learning Basics โ Googleโs Machine Learning Crash Course
An in-depth introduction to machine learning for beginners:
โฌ Learn supervised and unsupervised learning.
โฌ Hands-on coding with TensorFlow.
Link: https://developers.google.com/machine-learning/crash-course
7๏ธโฃ Deep Learning โ Fast.aiโs Free Course
Fast.ai makes deep learning easy and accessible:
โฌ Build neural networks with PyTorch.
โฌ Learn by coding real projects.
Link: https://course.fast.ai/
8๏ธโฃ Data Science Projects โ Kaggle
โฌ Compete in challenges to practice your skills.
โฌ Great way to build your portfolio.
Link: https://www.kaggle.com/
โค3
The Only roadmap you need to become an ML Engineer ๐ฅณ
Phase 1: Foundations (1-2 Months)
๐น Math & Stats Basics โ Linear Algebra, Probability, Statistics
๐น Python Programming โ NumPy, Pandas, Matplotlib, Scikit-Learn
๐น Data Handling โ Cleaning, Feature Engineering, Exploratory Data Analysis
Phase 2: Core Machine Learning (2-3 Months)
๐น Supervised & Unsupervised Learning โ Regression, Classification, Clustering
๐น Model Evaluation โ Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC)
๐น Hyperparameter Tuning โ Grid Search, Random Search, Bayesian Optimization
๐น Basic ML Projects โ Predict house prices, customer segmentation
Phase 3: Deep Learning & Advanced ML (2-3 Months)
๐น Neural Networks โ TensorFlow & PyTorch Basics
๐น CNNs & Image Processing โ Object Detection, Image Classification
๐น NLP & Transformers โ Sentiment Analysis, BERT, LLMs (GPT, Gemini)
๐น Reinforcement Learning Basics โ Q-learning, Policy Gradient
Phase 4: ML System Design & MLOps (2-3 Months)
๐น ML in Production โ Model Deployment (Flask, FastAPI, Docker)
๐น MLOps โ CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow)
๐น Cloud & Big Data โ AWS/GCP/Azure, Spark, Kafka
๐น End-to-End ML Projects โ Fraud detection, Recommendation systems
Phase 5: Specialization & Job Readiness (Ongoing)
๐น Specialize โ Computer Vision, NLP, Generative AI, Edge AI
๐น Interview Prep โ Leetcode for ML, System Design, ML Case Studies
๐น Portfolio Building โ GitHub, Kaggle Competitions, Writing Blogs
๐น Networking โ Contribute to open-source, Attend ML meetups, LinkedIn presence
The data field is vast, offering endless opportunities so start preparing now.
Phase 1: Foundations (1-2 Months)
๐น Math & Stats Basics โ Linear Algebra, Probability, Statistics
๐น Python Programming โ NumPy, Pandas, Matplotlib, Scikit-Learn
๐น Data Handling โ Cleaning, Feature Engineering, Exploratory Data Analysis
Phase 2: Core Machine Learning (2-3 Months)
๐น Supervised & Unsupervised Learning โ Regression, Classification, Clustering
๐น Model Evaluation โ Cross-validation, Metrics (Accuracy, Precision, Recall, AUC-ROC)
๐น Hyperparameter Tuning โ Grid Search, Random Search, Bayesian Optimization
๐น Basic ML Projects โ Predict house prices, customer segmentation
Phase 3: Deep Learning & Advanced ML (2-3 Months)
๐น Neural Networks โ TensorFlow & PyTorch Basics
๐น CNNs & Image Processing โ Object Detection, Image Classification
๐น NLP & Transformers โ Sentiment Analysis, BERT, LLMs (GPT, Gemini)
๐น Reinforcement Learning Basics โ Q-learning, Policy Gradient
Phase 4: ML System Design & MLOps (2-3 Months)
๐น ML in Production โ Model Deployment (Flask, FastAPI, Docker)
๐น MLOps โ CI/CD, Model Monitoring, Model Versioning (MLflow, Kubeflow)
๐น Cloud & Big Data โ AWS/GCP/Azure, Spark, Kafka
๐น End-to-End ML Projects โ Fraud detection, Recommendation systems
Phase 5: Specialization & Job Readiness (Ongoing)
๐น Specialize โ Computer Vision, NLP, Generative AI, Edge AI
๐น Interview Prep โ Leetcode for ML, System Design, ML Case Studies
๐น Portfolio Building โ GitHub, Kaggle Competitions, Writing Blogs
๐น Networking โ Contribute to open-source, Attend ML meetups, LinkedIn presence
The data field is vast, offering endless opportunities so start preparing now.
โค4
How do you start AI and ML ?
Where do you go to learn these skills? What courses are the best?
Thereโs no best answer๐ฅบ. Everyoneโs path will be different. Some people learn better with books, others learn better through videos.
Whatโs more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, youโve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what Iโve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
Theyโre all world class. Iโm a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If youโre an absolute beginner, start with some introductory Python courses and when youโre a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.iss.one/machinelearning_deeplearning
Like for more โค๏ธ
All the best ๐๐
Where do you go to learn these skills? What courses are the best?
Thereโs no best answer๐ฅบ. Everyoneโs path will be different. Some people learn better with books, others learn better through videos.
Whatโs more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, youโve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what Iโve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
Theyโre all world class. Iโm a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If youโre an absolute beginner, start with some introductory Python courses and when youโre a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.iss.one/machinelearning_deeplearning
Like for more โค๏ธ
All the best ๐๐
โค7
Machine Learning isn't easy!
Itโs the field that powers intelligent systems and predictive models.
To truly master Machine Learning, focus on these key areas:
0. Understanding the Basics of Algorithms: Learn about linear regression, decision trees, and k-nearest neighbors to build a solid foundation.
1. Mastering Data Preprocessing: Clean, normalize, and handle missing data to prepare your datasets for training.
2. Learning Supervised Learning Techniques: Dive deep into classification and regression models, such as SVMs, random forests, and logistic regression.
3. Exploring Unsupervised Learning: Understand clustering techniques (K-means, hierarchical) and dimensionality reduction (PCA, t-SNE).
4. Mastering Model Evaluation: Use techniques like cross-validation, confusion matrices, ROC curves, and F1 scores to assess model performance.
5. Understanding Overfitting and Underfitting: Learn how to balance bias and variance to build robust models.
6. Optimizing Hyperparameters: Use grid search, random search, and Bayesian optimization to fine-tune your models for better performance.
7. Diving into Neural Networks and Deep Learning: Explore deep learning with frameworks like TensorFlow and PyTorch to create advanced models like CNNs and RNNs.
8. Working with Natural Language Processing (NLP): Master text data, sentiment analysis, and techniques like word embeddings and transformers.
9. Staying Updated with New Techniques: Machine learning evolves rapidlyโkeep up with emerging models, techniques, and research.
Machine learning is about learning from data and improving models over time.
๐ก Embrace the challenges of building algorithms, experimenting with data, and solving complex problems.
โณ With time, practice, and persistence, youโll develop the expertise to create systems that learn, predict, and adapt.
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
#datascience
Itโs the field that powers intelligent systems and predictive models.
To truly master Machine Learning, focus on these key areas:
0. Understanding the Basics of Algorithms: Learn about linear regression, decision trees, and k-nearest neighbors to build a solid foundation.
1. Mastering Data Preprocessing: Clean, normalize, and handle missing data to prepare your datasets for training.
2. Learning Supervised Learning Techniques: Dive deep into classification and regression models, such as SVMs, random forests, and logistic regression.
3. Exploring Unsupervised Learning: Understand clustering techniques (K-means, hierarchical) and dimensionality reduction (PCA, t-SNE).
4. Mastering Model Evaluation: Use techniques like cross-validation, confusion matrices, ROC curves, and F1 scores to assess model performance.
5. Understanding Overfitting and Underfitting: Learn how to balance bias and variance to build robust models.
6. Optimizing Hyperparameters: Use grid search, random search, and Bayesian optimization to fine-tune your models for better performance.
7. Diving into Neural Networks and Deep Learning: Explore deep learning with frameworks like TensorFlow and PyTorch to create advanced models like CNNs and RNNs.
8. Working with Natural Language Processing (NLP): Master text data, sentiment analysis, and techniques like word embeddings and transformers.
9. Staying Updated with New Techniques: Machine learning evolves rapidlyโkeep up with emerging models, techniques, and research.
Machine learning is about learning from data and improving models over time.
๐ก Embrace the challenges of building algorithms, experimenting with data, and solving complex problems.
โณ With time, practice, and persistence, youโll develop the expertise to create systems that learn, predict, and adapt.
Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.iss.one/datasciencefun
Like if you need similar content ๐๐
Hope this helps you ๐
#datascience
โค3
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:
Letโs 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 โ โOh, they have four legs, fur, floppy ears...โ
Next time the kid sees a new picture, they might say, โThatโs a dog!โ โ even if theyโve never seen that exact dog before.
Thatโs what machine learning does โ 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 โthis is a dogโ, โthis is not a dogโ).
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
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like for more โค๏ธ
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:
Letโs 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 โ โOh, they have four legs, fur, floppy ears...โ
Next time the kid sees a new picture, they might say, โThatโs a dog!โ โ even if theyโve never seen that exact dog before.
Thatโs what machine learning does โ 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 โthis is a dogโ, โthis is not a dogโ).
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
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like for more โค๏ธ
โค4๐2
Stanford packed 1.5 hours with everything you need to know about LLMs
Here are 5 lessons that stood out from the lecture:
1/ Architecture โ Everything
โ Transformers arenโt the bottleneck anymore.
โ In practice, data quality, evaluation design, and system efficiency drive real gains.
2/ Tokenizers Are Underrated
โ A single tokenization choice can break performance on math, code, or logic.
โ Most models can't generalize numerically because 327 might be one token, while 328 is split.
3/ Scaling Laws Guide Everything
โ More data + bigger models = better loss. But it's predictable.
โ You can estimate how much performance youโll gain before you even train.
4/ Post-training = The Real Upgrade
โ SFT teaches the model how to behave like an assistant.
โ RLHF and DPO tune what it says and how it says it.
5/ Training is 90% Logistics
โ The web is dirty. Deduplication, PII filtering, and domain weighting are massive jobs.
โ Good data isnโt scraped, itโs curated, reweighted, and post-processed for weeks.
Here are 5 lessons that stood out from the lecture:
1/ Architecture โ Everything
โ Transformers arenโt the bottleneck anymore.
โ In practice, data quality, evaluation design, and system efficiency drive real gains.
2/ Tokenizers Are Underrated
โ A single tokenization choice can break performance on math, code, or logic.
โ Most models can't generalize numerically because 327 might be one token, while 328 is split.
3/ Scaling Laws Guide Everything
โ More data + bigger models = better loss. But it's predictable.
โ You can estimate how much performance youโll gain before you even train.
4/ Post-training = The Real Upgrade
โ SFT teaches the model how to behave like an assistant.
โ RLHF and DPO tune what it says and how it says it.
5/ Training is 90% Logistics
โ The web is dirty. Deduplication, PII filtering, and domain weighting are massive jobs.
โ Good data isnโt scraped, itโs curated, reweighted, and post-processed for weeks.
โค6
Data Scientist Roadmap
|
|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
| |
| | |
| |
| |
|
|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
|
| | |
| |
| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | |
| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| |
| |
|
|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| |
| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| |
| |
|
|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| |
| |
|
|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| |
|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
|
|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement
|
|-- 1. Basic Foundations
| |-- a. Mathematics
| | |-- i. Linear Algebra
| | |-- ii. Calculus
| | |-- iii. Probability
| |
-- iv. Statistics
| |
| |-- b. Programming
| | |-- i. Python
| | | |-- 1. Syntax and Basic Concepts
| | | |-- 2. Data Structures
| | | |-- 3. Control Structures
| | | |-- 4. Functions
| | | -- 5. Object-Oriented Programming| | |
| |
-- ii. R (optional, based on preference)
| |
| |-- c. Data Manipulation
| | |-- i. Numpy (Python)
| | |-- ii. Pandas (Python)
| | -- iii. Dplyr (R)| |
|
-- d. Data Visualization
| |-- i. Matplotlib (Python)
| |-- ii. Seaborn (Python)
| -- iii. ggplot2 (R)|
|-- 2. Data Exploration and Preprocessing
| |-- a. Exploratory Data Analysis (EDA)
| |-- b. Feature Engineering
| |-- c. Data Cleaning
| |-- d. Handling Missing Data
|
-- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
| |-- a. Supervised Learning
| | |-- i. Regression
| | | |-- 1. Linear Regression
| | | -- 2. Polynomial Regression| | |
| |
-- ii. Classification
| | |-- 1. Logistic Regression
| | |-- 2. k-Nearest Neighbors
| | |-- 3. Support Vector Machines
| | |-- 4. Decision Trees
| | -- 5. Random Forest| |
| |-- b. Unsupervised Learning
| | |-- i. Clustering
| | | |-- 1. K-means
| | | |-- 2. DBSCAN
| | |
-- 3. Hierarchical Clustering
| | |
| | -- ii. Dimensionality Reduction| | |-- 1. Principal Component Analysis (PCA)
| | |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
| |
-- 3. Linear Discriminant Analysis (LDA)
| |
| |-- c. Reinforcement Learning
| |-- d. Model Evaluation and Validation
| | |-- i. Cross-validation
| | |-- ii. Hyperparameter Tuning
| | -- iii. Model Selection| |
|
-- e. ML Libraries and Frameworks
| |-- i. Scikit-learn (Python)
| |-- ii. TensorFlow (Python)
| |-- iii. Keras (Python)
| -- iv. PyTorch (Python)|
|-- 4. Deep Learning
| |-- a. Neural Networks
| | |-- i. Perceptron
| |
-- ii. Multi-Layer Perceptron
| |
| |-- b. Convolutional Neural Networks (CNNs)
| | |-- i. Image Classification
| | |-- ii. Object Detection
| | -- iii. Image Segmentation| |
| |-- c. Recurrent Neural Networks (RNNs)
| | |-- i. Sequence-to-Sequence Models
| | |-- ii. Text Classification
| |
-- iii. Sentiment Analysis
| |
| |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
| | |-- i. Time Series Forecasting
| | -- ii. Language Modeling| |
|
-- e. Generative Adversarial Networks (GANs)
| |-- i. Image Synthesis
| |-- ii. Style Transfer
| -- iii. Data Augmentation|
|-- 5. Big Data Technologies
| |-- a. Hadoop
| | |-- i. HDFS
| |
-- ii. MapReduce
| |
| |-- b. Spark
| | |-- i. RDDs
| | |-- ii. DataFrames
| | -- iii. MLlib| |
|
-- c. NoSQL Databases
| |-- i. MongoDB
| |-- ii. Cassandra
| |-- iii. HBase
| -- iv. Couchbase|
|-- 6. Data Visualization and Reporting
| |-- a. Dashboarding Tools
| | |-- i. Tableau
| | |-- ii. Power BI
| | |-- iii. Dash (Python)
| |
-- iv. Shiny (R)
| |
| |-- b. Storytelling with Data
| -- c. Effective Communication|
|-- 7. Domain Knowledge and Soft Skills
| |-- a. Industry-specific Knowledge
| |-- b. Problem-solving
| |-- c. Communication Skills
| |-- d. Time Management
|
-- e. Teamwork
|
-- 8. Staying Updated and Continuous Learning|-- a. Online Courses
|-- b. Books and Research Papers
|-- c. Blogs and Podcasts
|-- d. Conferences and Workshops
`-- e. Networking and Community Engagement
โค10
Data Analyst vs Data Engineer vs Data Scientist โ
Skills required to become a Data Analyst ๐
- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic scripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.
Skills required to become a Data Engineer: ๐
- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.
Skills required to become a Data Scientist: ๐
- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.
Bonus Skills Across All Roles:
- Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
Skills required to become a Data Analyst ๐
- Advanced Excel: Proficiency in Excel is crucial for data manipulation, analysis, and creating dashboards.
- SQL/Oracle: SQL is essential for querying databases to extract, manipulate, and analyze data.
- Python/R: Basic scripting knowledge in Python or R for data cleaning, analysis, and simple automations.
- Data Visualization: Tools like Power BI or Tableau for creating interactive reports and dashboards.
- Statistical Analysis: Understanding of basic statistical concepts to analyze data trends and patterns.
Skills required to become a Data Engineer: ๐
- Programming Languages: Strong skills in Python or Java for building data pipelines and processing data.
- SQL and NoSQL: Knowledge of relational databases (SQL) and non-relational databases (NoSQL) like Cassandra or MongoDB.
- Big Data Technologies: Proficiency in Hadoop, Hive, Pig, or Spark for processing and managing large data sets.
- Data Warehousing: Experience with tools like Amazon Redshift, Google BigQuery, or Snowflake for storing and querying large datasets.
- ETL Processes: Expertise in Extract, Transform, Load (ETL) tools and processes for data integration.
Skills required to become a Data Scientist: ๐
- Advanced Tools: Deep knowledge of R, Python, or SAS for statistical analysis and data modeling.
- Machine Learning Algorithms: Understanding and implementation of algorithms using libraries like scikit-learn, TensorFlow, and Keras.
- SQL and NoSQL: Ability to work with both structured and unstructured data using SQL and NoSQL databases.
- Data Wrangling & Preprocessing: Skills in cleaning, transforming, and preparing data for analysis.
- Statistical and Mathematical Modeling: Strong grasp of statistics, probability, and mathematical techniques for building predictive models.
- Cloud Computing: Familiarity with AWS, Azure, or Google Cloud for deploying machine learning models.
Bonus Skills Across All Roles:
- Data Visualization: Mastery in tools like Power BI and Tableau to visualize and communicate insights effectively.
- Advanced Statistics: Strong statistical foundation to interpret and validate data findings.
- Domain Knowledge: Industry-specific knowledge (e.g., finance, healthcare) to apply data insights in context.
- Communication Skills: Ability to explain complex technical concepts to non-technical stakeholders.
I have curated best 80+ top-notch Data Analytics Resources ๐๐
https://t.iss.one/DataSimplifier
Like this post for more content like this ๐โฅ๏ธ
Share with credits: https://t.iss.one/sqlspecialist
Hope it helps :)
โค4
Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
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For Promotions: @love_data
โค5๐1
You wonโt become an AI Engineer in a month.
You wonโt suddenly build world-class systems after a bootcamp.
You wonโt unlock next-level skills just by binge-watching tutorials for 30 days.
Because in a month, youโll realize:
โ Most of your blockers arenโt about โAIโ, theyโre about solid engineering: writing clean code, debugging, and shipping reliable software.
โ Learning a new tool is easy; building things that donโt break under pressure is where people struggle.
โ Progress comes from showing up every day, not burning out in a week.
So what should you actually do?
Hereโs what works:
โ Spend 30 minutes daily on a core software skill.
One day, refactor old code for readability. Next, write unit tests. After that, dive into error handling or learn how to set up a new deployment pipeline.
โ Block out 3โ4 hours every weekend to build something real.
Create a simple REST API. Automate a repetitive task. Try deploying a toy app to the cloud.
Donโt worry about perfection. Focus on finishing.
โ Each week, pick one engineering topic to dig into.
Maybe itโs version control, maybe itโs CI/CD, maybe itโs understanding how authentication actually works.
The goal: get comfortable with the โplumbingโ that real software runs on.
You donโt need to cram.
You need to compound.
A little progress, done daily
Thatโs how you build confidence.
Thatโs how you get job-ready.
Small efforts. Done consistently.
Thatโs the unfair advantage youโre waiting to find, always has been.
You wonโt suddenly build world-class systems after a bootcamp.
You wonโt unlock next-level skills just by binge-watching tutorials for 30 days.
Because in a month, youโll realize:
โ Most of your blockers arenโt about โAIโ, theyโre about solid engineering: writing clean code, debugging, and shipping reliable software.
โ Learning a new tool is easy; building things that donโt break under pressure is where people struggle.
โ Progress comes from showing up every day, not burning out in a week.
So what should you actually do?
Hereโs what works:
โ Spend 30 minutes daily on a core software skill.
One day, refactor old code for readability. Next, write unit tests. After that, dive into error handling or learn how to set up a new deployment pipeline.
โ Block out 3โ4 hours every weekend to build something real.
Create a simple REST API. Automate a repetitive task. Try deploying a toy app to the cloud.
Donโt worry about perfection. Focus on finishing.
โ Each week, pick one engineering topic to dig into.
Maybe itโs version control, maybe itโs CI/CD, maybe itโs understanding how authentication actually works.
The goal: get comfortable with the โplumbingโ that real software runs on.
You donโt need to cram.
You need to compound.
A little progress, done daily
Thatโs how you build confidence.
Thatโs how you get job-ready.
Small efforts. Done consistently.
Thatโs the unfair advantage youโre waiting to find, always has been.
โค6