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Crash course in Python
Crash course in Statisitcs
Introduction to Machine Learning
Setting up the tools
Supervised Vs Unsupervised Learning
Machine Learning With Python
Home
Basics
Python Ecosystem
Methods for Machine Learning
Data Loading for ML Projects
Understanding Data with Statistics
Understanding Data with Visualization
Preparing Data
Data Feature Selection
ML Algorithms - Classification
Introduction
Logistic Regression
Support Vector Machine (SVM)
Decision Tree
Naïve Bayes
Random Forest
ML Algorithms - Regression
Random Forest
Linear Regression
ML Algorithms - Clustering
Overview
K-means Algorithm
Mean Shift Algorithm
Hierarchical Clustering
ML Algorithms - KNN Algorithm
Finding Nearest Neighbors
Performance Metrics
Automatic Workflows
Improving Performance of ML Models
Improving Performance of ML Model (Contd…)
ML With Python - Resources
Machine Learning With Python - Quick Guide
Machine Learning with Python - Resources
Machine Learning With Python - Discussion
Regression
KNN Classifier
Naive Bayes Classifier
Support Vector Machine
Tree Based Algorithm
Introduction to Neural Networks
Real World Project
project list and Application:
Emotion analysis
Sentiment analysis
Error detection and prevention
Weather forecasting and prediction
Stock market analysis and forecasting
Speech synthesis
Speech recognition
Customer segmentation
Object recognition
Fraud detection
Fraud prevention
Recommendation of products to customer in online shopping
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