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Machine Learning and Predictive Analysis

Name: Machine Learning and Predictive Analysis
Rating: 5.00 (2 reviews)

Accelerate your Career

Learn applied machine learning and predictive analytics. Build real-world models for forecasting, classification, and decision-making using Python.

3 Modules

with Certifications

5:40 Hours

of Recorded Content

5.0 Ratings

by 783 Learners

English

Language

Paid Course

Get this Course @ ₹4,999

Name

Email ID

Phone Number

783 enrolled in this course

Course Specifications

Sections

Lessons

100

Capacity

Unlimited

Duration

5:40 Hours

Students

783

Access Duration

90 Days

Created Date

2 Jun 2026

Updated Date

6 Oct 2026

Machine Learning and Predictive Analysis

Machine Learning and Predictive Analysis is a specialist-level course designed to equip learners with practical skills in building intelligent predictive systems using industry-standard machine learning frameworks. Starting with Scikit-Learn fundamentals and data preprocessing techniques, the course progresses through regression, classification, clustering, ensemble methods, and predictive analytics applications. Students will work on real-world use cases including disease prediction, customer segmentation, loan prediction, and forecasting models before advancing to AI applications powered by Natural Language Processing and Deep Learning. By the end of the course, learners will be capable of designing and deploying robust machine learning solutions for business and research applications.

MASTERING THE DATA SCIENCE LIFECYCLE

Comprehensive Syllabus Outline

A comprehensive curriculum designed to take you from beginner to professional.

M1

Module 1: Python Programming & Essentials for ML

ML MC

Python Basics

Python Data Structures

Class and Object

Python Programming Fundamentals

Pandas Library

Project 1 - Sale Data Analysis using Pandas

Working with Numpy Arrays

Data visualization with matplotlib and Seaborn

Project - II - Exploratory Data Analysis with Pandas, Matplotlib, Seaborn

Sklearn Library (Scaling, Encoding)

SKlearn Library (Null, Outlier Handling)

Sklearn Library (train test, Features Selection) (Duration hidden)

M2

Module 2: Core Machine Learning

ML Algorithms (Linear Models)

ML Algorithms (SVM, Decision Trees)

ML Algorithms (Ensemble, Naive Bayes, KNN)

Hyperparameter Tuning & Model Evaluation

Project – Credit Card Fraud Detection

Project – Calories Burnt Prediction

Project – Music Dataset Clustering (PCA)

M3

Module 3: Advanced Predictive Analysis & NLP

Deep Learning Introduction

Project – Churn Prediction (ANN)

CNN, RNN, LSTM

Time Series Prediction (Part 1 )

Time Series Prediction (Part 2)

Project – CO₂ Emission Prediction

Project – Stock Price Prediction (Neural Network)

NLP Introduction

Project – Fake News Prediction (NLP)

Project – Twitter Sentiment Analysis (NLP + LSTM)

Capstone Project

Predictive Analytics Portfolio

Synthesize your learning by building a suite of 7 industry-standard AI models that address real-world business challenges.