Artificial Neural Networks with Java
Igor Livshin, 2019

Some helpful Data science projects for beginners

https://www.kaggle.com/c/house-prices-advanced-regression-techniques

https://www.kaggle.com/c/digit-recognizer

https://www.kaggle.com/c/titanic

BEST RESOURCES TO LEARN DATA SCIENCE AND MACHINE LEARNING FOR FREE

https://developers.google.com/machine-learning/crash-course

https://www.kaggle.com/learn/overview

https://forums.fast.ai/t/recommended-python-learning-resources/26888

https://www.fast.ai/

https://imp.i115008.net/JrBjZR

https://ern.li/OP/1qvkxbfaxqj

ENJOY LEARNING 👍👍

Machine Learning in Microservices
Mohamed Abouahmed, 2023

Where to get data for your next machine learning project?

An overview of 8 amazing resources to accelerate your next project with data!

📌 Google Datasets
Easy to search Datasets on Google Dataset Search engine as it is to search for anything on Google Search! You just enter the topic on which you need to find a Dataset.

📌 Papers with Code Datasets
An exclusive collection of 4053 machine learning datasets with a supreme search and a good composition of datasets .

📌 Kaggle Dataset
Explore, analyze, and share quality data.

📌 Big Bad NLP Datasets
One of the best sources for sophisticated Natural Language Processing datasets

📌 Hugging Face Datasets
Well known for NLP but good news hugging face is expanding and they can add datasets for machine learning soon, they have 921 datasets.

📌 Open Data on AWS
This registry exists to help people discover and share datasets that are available via AWS resources

📌 Awesome Public Datasets
A topic-centric list of HQ open datasets.

📌 Azure public data sets
This one has public data sets for testing and prototyping.

📌 Carnegie Mellon University
A listing of 750 databases, datasets, and research support tools.

Bonus: This articles on Kdnuggets covers around 80 datasets sources of the datasets. Enjoy machine learning.

MACHINE LANGUAGE

The instructions in binary form, which can be directly understood by the computer (CPU) without translating them, is called a machine language or machine code.

Machine language is also known as first generation of programming language. Machine language is the fundamental language of the computer and the program instructions in this language is in the binary form (that is 0's and 1's).

This language is different for different computers.

It is not easy to learn the machine language.

ADVANTAGE OF MACHINE LANGUAGE

The only advantage of machine language is that the program of machine language runs very fast because no translation program is required for the CPU.

DISADVANTAGE OF MACHINE LANGUAGE

Here are some of the main disadvantages of machine languages:

• Machine Dependent - the internal design of every computer is different from every other type of computer, machine language also differs from one computer to another. Hence, after becoming proficient in the machine language of one type of computer, if a company decides to change to another type, then its programmer will have to learn a new machine language and would have to rewrite all existing program.

• Difficult to Modify - it is difficult to correct or modify this language. Checking machine instructions to locate errors is very difficult and time consuming.

• Difficult to Program - a computer executes machine language program directly and efficiently, it is difficult to program in machine language. A machine language programming must be knowledgeable about the hardware structure of the computer.

To become a Machine Learning Engineer:

• Python
• numpy, pandas, matplotlib, Scikit-Learn
• TensorFlow or PyTorch
• Jupyter, Colab
• Analysis > Code
• 99%: Foundational algorithms
• 1%: Other algorithms
• Solve problems ← This is key
• Teaching = 2 × Learning
• Have fun!

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems

📚 book

Python Workout 50 Essential Exercises

📓 book

Most Machine Learning articles on Medium are really very bad quality and repetitive. Titles are usually clickbaits. Most start with a story which is utter nonsense and totally not required. In some 5-10% content is useful but most are fully useless. Sorry if I hurt feelings. Agree 👍

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 learn important data science and machine learning concepts

ENJOY LEARNING 👍👍