Course Details

Machine Learning with Python

Master Python for Machine Learning with NumPy, Pandas, Matplotlib, and Seaborn. Build skills in statistics, supervised & unsupervised models, regression, preprocessing, tree-based algorithms, and neural networks. Apply everything hands-on in Jupyter to launch your career in data science and AI.

Top Course

  • 50+ Students
  • English

What You’ll Learn?

  • Use Python & Jupyter effectively for ML workflows.

  • Manipulate and analyze data with NumPy and Pandas.

  • Visualize insights with Matplotlib and Seaborn.

  • Apply statistical thinking and perform exploratory data analysis.

  • Build supervised and unsupervised models including regression.

  • Train tree-based models and learn fundamentals of neural networks.

Course content

  • 14 Sections
  • 45 - 90 Lectures
  • 45h - 90h Total Duration
Python (Internals, do's and don'ts) Architecture, Data Structure

  • Install Anaconda

  • Jupyter Notebook Overview

  • Shortcuts in Jupyter

  • Data Types

  • Variable naming rules

  • List, Tuple, Set, Dict

  • Introduction to Files and directories

  • Command prompt paths

  • Read text files with open/with

  • If, elif, else

  • For loop

  • While loop

  • ML Libraries

  • Numpy Hands-on

  • Pandas overview

  • Pandas Hands-on

  • Explore, visualize, extract insights from data

  • Matplotlib Hands-on

  • Seaborn Hands-on

  • Think statistically

  • Measures of Central Tendency

  • Measures of Dispersion

  • IQR Statistics Hands-on

  • Classification & Regression, Fine-tuning your model

  • Supervised Learning

  • Unsupervised Learning

  • Linear Regression

  • Metrics & Hands-on in Linear Regression

  • Logistic Regression

  • Metrics

  • Hands-on

  • Linear Regression

  • Metrics

  • Intro to preprocessing

  • Standardizing Data

  • Exploratory Data Analysis

  • Missing Values

  • Outliers

  • Normalization

  • Feature Scaling & Selection

  • Decision Trees

  • Bagging

  • Random Forest

  • Fundamentals of NN

  • Use data science packages, visualize, build model etc.

This Course Includes:

  • Lifetime Access to Course

  • Internship/Project

  • Certificate of Completion

  • Interactive Quizzes

Join this Course

Course Feedback

The course simplified Python, NumPy, and Pandas concepts, helping me gain strong data analysis and machine learning skills

Ishika

Hands-on projects in regression, tree models, and neural networks made machine learning concepts very easy and engaging to learn.

Aniket Rao

Learning preprocessing, feature selection, and evaluation metrics boosted my confidence to handle real-world machine learning tasks independently

Divya

Practical guidance on Jupyter, data visualization, and supervised learning helped me understand ML workflows thoroughly and apply them well.

Rohith