Machine Learning


  1. Machine Learning Introduction
    • What is Machine Learning
    • Machine Learning vs. Traditional Programming
    • How does Machine Learning work?
    • Machine Learning Algorithms and where they are used?
    • How to choose Machine Learning Algorithm
    • Challenges and limitations of Machine Learning
    • Application of Machine Learning
    • Why is Machine Learning important?
  2. Deep Learning Tutorial
    • What is Deep Learning?
    • Deep Learning Process
    • Classification of Neural Networks 
    • Types of Deep Learning Networks
    • Feed-forward neural networks
    • Recurrent neural networks(RNNs)
    • Convolution neural networks(CNN)
    • Reinforcement Learning 
    • Examples of Deep Learning applications
    • Why is Deep Learning important
    • imitations of Deep Learning
  3. Machine Learning vs Deep Learning
  4. Supervised Machine Learning
    • What is Supervised Machine Learning
    • How supervised Learning works
    • Types of Supervised Machine Learning Algorithms 
    • Supervised vs. unsupervised Machine Learning techniques 
    • challenges in supervised Machine Learning
    • advantages of supervised learning
    • disadvantages of supervised learning 
    • best practices for supervised learning
  5. Unsupervised Machine Learning
    • What is unsupervised learning
    • Examples of unsupervised machine learning
    • Why Unsupervised Learning?
    • Types of unsupervised learning
    • clustering
    • clustering types
    • association
    • supervised vs Unsupervised Machine Learning
    • applications of unsupervised Machine Learning
    • disadvantages of unsupervised learning
  6. Supervised vs Unsupervised Learning 


  1. Back Propagation Neural Network
    • What is Artificial Neural Networks?
    • What is Backpropagation?
    • How Backpropagation Works
    • Why We Need Backpropagation?
    • What is a Feed-Forward Network?
    • Types of Backpropagation Networks
    • History of Backpropagation
    • Backpropagation Key Points
    • Best practice Backpropagation
    • Disadvantages of using Backpropagation
  2. Reinforcement Learning
    • What is Reinforcement Learning?
    • Important terms used in Deep Reinforcement
    • Learning method
    • How Reinforcement Learning works
    • Reinforcement Learning Algorithms
    • Characteristics of Reinforcement Learning
    • Types of Reinforcement Learning
    • Learning Models of Reinforcement
    • Reinforcement Learning vs. Supervised Learning
    • Applications of Reinforcement Learning
    • Why use Reinforcement Learning?
    • When Not to Use Reinforcement Learning
    • Challenges of Reinforcement Learning
  3. Deep Learning Libraries
  4. Fuzzy Logic Tutorial
    • What Is Fuzzy Logic?
    • History of Fuzzy Logic
    • Characteristics of Fuzzy Logic
    • When not to use fuzzy logic
    • Fuzzy Logic Architecture
    • Fuzzy Logic vs. Probability
    • Crisp vs. Fuzzy
    • Classical Set vs. Fuzzy set Theory
    • Fuzzy Logic Examples
    • Application Areas of Fuzzy Logic
    • Advantages of Fuzzy Logic System
    • Disadvantages of Fuzzy Logic Systems
  5. Confusion Matrix in Machine Learning
    • What is Confusion matrix?
    • Four outcomes of the confusion matrix
    • Example of Confusion matrix:
    • How to Calculate a Confusion Matrix
    • Other Important Terms using a Confusion matrix
    • Why you need Confusion matrix?


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