https://github.com/grand-27-master/data-science-course

One-stop repo for learning data science along with roadmap!
https://github.com/grand-27-master/data-science-course

data-analysis data-science machine-learning python statistics

Last synced: about 2 months ago
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One-stop repo for learning data science along with roadmap!

• Host: GitHub
• URL: https://github.com/grand-27-master/data-science-course
• Owner: grand-27-master
• Created: 2021-08-11T06:43:18.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2021-09-03T10:09:24.000Z (almost 5 years ago)
• Last Synced: 2025-02-24T09:42:56.130Z (over 1 year ago)
• Topics: data-analysis, data-science, machine-learning, python, statistics
• Language: Python
• Homepage:
• Size: 32.9 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Roadmap to Become a Machine Learning Expert

I'll recommend learning both Python & R, but if you are only learning one of them, then I would suggest going with Python.

- **Learn Basic Python Syntax:** Basic Arithmetic Operations, Control & Conditional Structures, Looping, User Input, Strings, Integers, Typecasting

- **Learn In-built Data Structures:** List, Set, Tuple, Dictionary, Function, Lambda Function, Iterators & Generators, Exception Handling & Imports Libraries

- **OOPS:** Classes, Object, Method, Inheritance, Polymorphism, Data Abstraction, Encapsulation

- **Libraries with Python:** Numpy, Pandas, Scipy, Scikit-Learn, Matplotlib, Seaborn**Link:** [Learn Python in 20 Days for Free!](https://www.kaggle.com/questions-and-answers/262250)

### **2. Learn Basic Statistics**

- **Probability Distributions:** Continuous and Discrete

- **Basic Probability:** Independent and Dependent Events, Marginal Probability, Conditional Probability, Joint Probability

- **Measures of Central Tendency:** Mean, Median, Mode

- **Variance, Standard Deviation & Standard Error**

### **3. Learn Exploratory Data Analysis (EDA)**

- Identification of variables and data types

- Analyzing the basic metrics

- Non-Graphical & Graphical Univariate Analysis

- Bivariate Analysis

- Variable transformations, Missing value/Outlier Treatment

- Correlation Analysis/Dimensionality Reduction

### **4. Learn Supervised & Unsupervised Model**

### **Supervised Models:**

- Linear/Polynomial/Logistic regression

- Classification trees

- Ensemble models like Bagging and Random Forest

- Supervised Vector Machines

### **Unsupervised Models:**

- Clustering

- Association Rule Learning

### **5. Learn Deep Learning Models**

- **Supervised:** ANN/CNN/RNN

- **Unsupervised:** SOMs/Boltzmann Machines/AutoEncoders

### **6. Understand Big Data Technologies**

- Big Data Overview and Eco-System

- Hadoop/NoSQL/Data Lakes

- TensorFlow/Docker/Kubernetes