Machine Learning and Data Science using Python

Learn Python in Malayalam

  • 4 ratings, 33 students enrolled

Course Overview

This is a full Data Science and Machine Learning course that any beginner (not having computer science background) can follow to learn Data science and Machine Learning. It covers the following topics. 1. Python Programming from Basics 2. Jupyter notebook, Numpy, Pandas, Matplotlib 3. Machine learning with Scikit Learn. 4. A Complete Data Science and Machine Learning Project. During the course, We will work with many real-world samples.

What are the requirements?

  • A Computer or Laptop with Internet connection.

What am I going to get from this course?

  • Will guide You to become a confident Data Scientist and Machine Learning Engineer with clearcut knowledge in all the fundamentals.
  • Make You confident to handle Projects in Machine Learnig and Data Science.

Who this course is for?

  • Anyone with average knowledge of using Computer and Internet and having a little Logic and Mathematical skills.

About the Author

An Experienced Computer Engineer, with enough experience in Python, Java, and Android Developments. Also an Online Tutor, presently having students from countries like US, UK, and Germany. Now practicing as a Data Scientist using Python Programming.

Course Curriculum


  • For Loops
  • For Loop Samples
  • Strings, Int and Input
  • If, Else and Elif
  • While Loops
  • While Infinite Loops
  • While Loop Sample
  • Functions
  • Global and Local Variables
  • Variables
  • Data Types and Operators
  • String Operations, Special Operators
  • List in Python
  • Tuple and Set
  • Dictionary
  • Class and Objects
  • File Handling
  • Exception Handling
  • Working with JSON
  • Conclusion


  • Introduction
  • Jupyter Notebook
  • Numpy Module
  • Numpy Arrays
  • Pandas Introduction
  • Data Frame Creation and Views
  • Data Frame Operations
  • Creating Data Frames
  • Read Write CSV Files
  • Read Write EXCEL Files
  • Handling Missing Data - Filna
  • Interpolate and Dropna
  • Replace Functions
  • Group By
  • Concat and Merge
  • Matplotlib Introduction
  • Format Strings in Plot Function
  • Labels, Legend and Grid
  • Bar Charts
  • Histograms
  • Pie Chart and Save Plot Images
  • Conclusion


  • Introduction
  • Linear Regression
  • Linear Regression Multivariate
  • How Gradient Descent
  • Gradient Descent Implementation
  • Save and Load Model
  • Dummy Variables
  • One Hot Encoding
  • Train Test Split
  • Logistic Regression with Logit Function
  • Logistic Regression Binary Classification
  • Logistic Regression Multiclass
  • Confusion Matrix
  • How Decision Tree
  • Decision Tree Implementation
  • Random Forest
  • Support Vector Machine
  • SVM Classifier (SVC)
  • K- Fold Cross Validation
  • K- Fold and Parameter Tuning
  • K- Means Clustering
  • K- Means Implementation
  • Conclusion


  • Introduction
  • Data Cleaning
  • Feature Engineering
  • Outlier Removal
  • Outlier Removal Contnd
  • Model Building
  • Model Export
  • Pycharm and VSC Editors
  • Python Flask Server
  • Flask Server Codes
  • Flask Server Running
  • Website UI
  • Conclusion

Course Conclusion

  • Course conclusion



Fairoos Ok
Computer Engineer

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