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Learning content

Python Programming in Machine Learning
1: Introduction to Machine Learning
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    • 1.1 What is Machine Learning?
       
    • 1.2 Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
       
    • 1.3 Python and Machine Learning Libraries (NumPy, Pandas, Scikit-Learn)
       
    • 1.4 Jupyter Notebooks for Machine Learning
       
2: Data Preprocessing
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    • 2.1 Data Cleaning and Handling Missing Data
       
    • 2.2 Feature Scaling and Normalization
       
    • 2.3 Data Encoding (One-Hot Encoding, Label Encoding)
       
    • 2.4 Feature Engineering
       
3: Supervised Learning
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    • 3.1 Linear Regression
       
    • 3.2 Logistic Regression
       
    • 3.3 Decision Trees and Random Forests
       
    • 3.4 Support Vector Machines (SVM)
       
    • 3.5 k-Nearest Neighbors (k-NN)
       
    • 3.6 Naive Bayes
       
    • 3.7 Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, ROC, AUC)
       
4: Unsupervised Learning
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    • 4.1 Clustering (K-Means, Hierarchical Clustering, DBSCAN)
       
    • 4.2 Dimensionality Reduction (Principal Component Analysis - PCA)
       
    • 4.3 Anomaly Detection
       
    • 4.4 Association Rule Learning (Apriori)
       
5: Neural Networks and Deep Learning
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    • 5.1 Introduction to Artificial Neural Networks (ANNs)
       
    • 5.2 Feedforward Neural Networks
       
    • 5.3 Activation Functions (Sigmoid, ReLU, etc.)
       
    • 5.4 Backpropagation and Gradient Descent
       
    • 5.5 Convolutional Neural Networks (CNNs)
       
    • 5.6 Recurrent Neural Networks (RNNs)
       
    • 5.7 Transfer Learning
       
    • 5.8 Introduction to TensorFlow and Keras
       
6: Model Evaluation and Hyperparameter Tuning
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    • 6.1 Cross-Validation
       
    • 6.2 Grid Search and Random Search for Hyperparameter Tuning
       
    • 6.3 Model Selection and Comparison
       
    • 6.4 Bias-Variance Tradeoff
       
    • 6.5 Overfitting and Underfitting
       
7: Natural Language Processing (NLP)
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    • 7.1 Text Preprocessing
       
    • 7.2 Bag of Words and TF-IDF
       
    • 7.3 Word Embeddings (Word2Vec, GloVe)
       
    • 7.4 Sentiment Analysis
       
8: Reinforcement Learning
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    • 8.1 Introduction to Reinforcement Learning
       
    • 8.2 Markov Decision Processes (MDPs)
       
    • 8.3 Q-Learning
       
    • 8.4 Deep Q-Networks (DQNs)
       
9: Deployment and Scaling
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    • 9.1 Model Deployment (Flask, Django, Docker)
       
    • 9.2 Cloud-Based Deployment (AWS, Google Cloud, Azure)
       
    • 9.3 Model Monitoring and Maintenance
       
10: Case Studies and Projects
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    • 10.1Real-world Machine Learning projects and case studies
       
    • 10.2Hands-on implementation and problem-solving
       
12: Future Trends and Advanced Topics
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    • 12.1Generative Adversarial Networks (GANs)
       
    • 12.2Autoencoders
       
    • 12.3Reinforcement Learning in Robotics
       
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