One of the most effective ways to learn machine learning is by getting hands-on experience and building something yourself.

While finding inspiration can be challenging, exploring projects by others can open your eyes to the endless possibilities. 💡

The projects I am sharing are perfect for those new to machine learning and curious about its potential.

Free Data sets for Data Science & Machine Learning Projects
👇👇
https://t.iss.one/DataPortfolio

knn notes

20 Python Libraries You
Aren’t Using (But Should)
Caleb Hattingh, 2016

Classification notes

Resource Pdf :- Advice from 25 Amazing Data Scientists.

Source :- Jake Klamka

Data_Science Pandas

Free Data sets for Data Science & Machine Learning Projects
👇👇
https://t.iss.one/DataPortfolio

Data_science Numpy cheat sheet

Data Scientist Roadmap
|
|-- 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

Python Data Science Projects For Boosting Your Portfolio

Important Topics to become a data scientist
[Advanced Level]
👇👇

1. Mathematics

Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification

2. Probability

Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution

3. Statistics

Introduction to Statistics
Data Description
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression

4. Programming

Python:

Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn

R Programming:

R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny

DataBase:
SQL
MongoDB

Data Structures

Web scraping

Linux

Git

5. Machine Learning

How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage

6. Deep Learning

Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification

7. Feature Engineering

Baseline Model
Categorical Encodings
Feature Generation
Feature Selection

8. Natural Language Processing

Text Classification
Word Vectors

9. Data Visualization Tools

BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense

10. Deployment

Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django

Join @datasciencefun to learning important data science and machine learning concepts

ENJOY LEARNING 👍👍