An Introduction to Machine Learning | The Complete Guide
Data Preprocessing for Machine Learning | Apply All the Steps in Python
Learn Simple Linear Regression in the Hard Way(with Python Code)
Multiple Linear Regression in Python (The Ultimate Guide)
Polynomial Regression in Two Minutes (with Python Code)
Support Vector Regression Made Easy(with Python Code)
Decision Tree Regression Made Easy (with Python Code)
Random Forest Regression in 4 Steps(with Python Code)
4 Best Metrics for Evaluating Regression Model Performance
A Beginners Guide to Logistic Regression(with Example Python Code)
K-Nearest Neighbor in 4 Steps(Code with Python & R)
Support Vector Machine(SVM) Made Easy with Python
Kernel SVM for Dummies(with Python Code)
Naive Bayes Classification Just in 3 Steps(with Python Code)
Decision Tree Classification for Dummies(with Python Code)
Random forest Classification
Evaluating Classification Model performance
A Simple Explanation of K-means Clustering in Python
Hierarchical Clustering
Association Rule Learning | Apriori
Eclat Intuition
Reinforcement Learning
Upper Confidence Bound (UCB) Algortihm: Solving the Multi-Armed Bandit Problem
Thompson Sampling Intuition
Natural Language Processing
Deep Learning
Artificial Neural Networks
Principal Component Analysis
Linear Discriminant Analysis (LDA)
Kernel PCA
Model Selection & Boosting
K-fold Cross Validation in Python | Master this State of the Art Model Evaluation Technique
Convolution Neural Network
Dimensionality Reduction

Deep Learning | Machine Learning

Deep Learning: Deep Learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in Artificial Intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Deep Learning is the most exciting and powerful branch of Machine Learning.

Deep Learning models can be used for a variety of complex tasks:

  • Artificial Neural Networks for Regression and Classification

  • Convolutional Neural Networks for Computer Vision

  • Recurrent Neural Networks for Time Series Analysis

  • Self Organizing Maps for Feature Extraction

  • Deep Boltzmann Machines for Recommendation Systems

  • AutoEncoders for Recommendation Systems

In this part, you will understand and learn how to implement the following Deep Learning models:

  1. Artificial Neural Networks for a Business Problem

  2. Convolutional Neural Networks for a Computer Vision task

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