Data Science And Machine Learning Programming Books

Data Science From Scratch Learning PDF Notes

Download data science With Python from scratch learning PDF notes free . With the help of this complete Course you will be able to learn about data science With Python from Scratch (beginning) to Advance .As You Know Data Science is Hot and Growing Field in 21 Century and Most Demanded Field in the World right now.

As you have seen There are lots of data science libraries, frameworks, modules, and toolkits that efficiently implement the most common data science algorithms and techniques.

Whenever you  become a data scientist, you need to Learn  NumPy, with scikit-learn, with pandas, and with a panoply of other libraries.
It’s awesome to start with Data Science.

Notes Data Science From Scratch
Format word
Language English
Size 16MB
Addition To help you to solve data analysis problems 
For Beginners

Data Science Complete Roadmap For Absolute Beginners.


Data Science From Scratch Course Outline 

  • Introduction to Data Science
  • What is Data Science?
  • Importance and applications of Data Science
  • Setting Up Your Environment
  • Installing Python and necessary libraries (e.g., NumPy, Pandas, Matplotlib)
  • Introduction to Jupyter Notebooks for coding
  • Basics of Python for Data Science
  • Variables and Data Types
  • Lists, Tuples, and Dictionaries
  • Control Structures (if-else, loops)
  • Functions and Modules
  • Working with Data in Python
  • Introduction to NumPy
  • Data manipulation with Pandas
  • Data Visualization using Matplotlib and Seaborn
  • Data Cleaning and Preprocessing
  • Handling Missing Data
  • Data Transformation (normalization, encoding categorical variables)
  • Outlier Detection and Treatment
  • Exploratory Data Analysis (EDA)
  • Descriptive Statistics
  • Data Visualization Techniques (histograms, scatter plots)
  • Correlation and Relationships
  • Introduction to Machine Learning
  • What is Machine Learning?
  • Types of Machine Learning
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Semi supervised
  • Supervised Learning Algorithms
  • Linear Regression
  • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines
  • Unsupervised Learning Algorithms
  • K-Means Clustering
  • Hierarchical Clustering
  • PCA (Principal Component Analysis)
  • Model Evaluation and Validation
  • Splitting Data into Training and Testing Sets
  • Metrics for Regression and Classification Models
  • Cross-Validation Techniques
  • Introduction to Data Mining
  • Association Rules
  • Clustering Methods K-Means
  • Anomaly Detection
  • Introduction to Deep Learning.
  • Basics of Neural Networks using libraries like TensorFlow or PyTorch
  • A Simple Data Science Project

You will Learn These Stuff 

Get in touch basics of programming with Python

Some real time Python code Examples with Different Python libraries like NumPy etc.

Get in touch basic Statistics and Mathematics. as you Know Math and Statistics are Heart of Data Science. if we are going to start learning Data Science we need to know about Math and Statistics.

Get in touch Python for Data Analysis.

Then start  Machine Learning from Scratch with Different Principle . 

Discuss some good real time Practice with projects.

These Topic are Covered very well in these notes. even simple Beginner person can start learning from these notes we are sharing this PDF Free of cost with you.



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