Tn this course, you’ll learn how to clean, analyze, and visualize data using Python, explore datasets to uncover meaningful patterns, and apply machine learning algorithms to build predictive models.
Price
Duration
Days
Class
Certificate
The Data Analysis with Python and Machine Learning course is designed to help you analyze large datasets, uncover patterns, and build intelligent predictive models using Python. You’ll learn how to clean and manipulate data, perform advanced analysis, visualize insights, and apply machine learning techniques to solve real-world business and technological problems.
By the end of this course, you’ll be able to analyze complex datasets using Python, build machine learning models, generate meaningful insights from data, and apply data-driven solutions to business and real-world challenges.
Register NowCourse overview
Python installation (Anaconda, Jupyter)
Variables & data types
Arithmetic Operators
Conditional statements
For Loops
While Loops
Functions
Lists, Tuples
String manipulation
List Comprehension
Dictionary & Sets
Introduction to NumPy
Array Creation
Indexing and Slicing
Mathematical Operations
Statistical Function
Series and Data Frame
Importing Datasets
Data Cleaning
Handling Missing Values
Filtering & Sorting
Group By & Aggregation Practice series
Matplotlib Basics
Line & Bar Charts
Histogram & Pie charts
Seaborn
Introduction to ML
Supervised & Unsupervised Learning
ML Workflow
Train-test split cleaning
Feature selection
Introduction to Scikit-Learn
Model Training Basics
Linear Regression Theory
Multiple Linear Regression Practical
House price prediction project
Logistic Regression
Decision Trees & KNN
Confusion Matrix
Churn Prediction Project
K-Means Clustering
Hierachical Clustering
PCA
Customer Segmentation Project
Readiness
Capstone Project Briefing
Dataset Selection
Project Implementation (Guided Session)
Project Presentations
Our training programs are designed to equip students with practical, industry-relevant skills through hands-on learning and real-world projects.
Fully Virtual
30th March, 2026
7PM - 9PM
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