Are you ready to dive into the exciting world of Machine Learning and Deep Learning? You’ve come to the right place! This post provides a well-organized guide to some amazing Jupyter Notebooks that I compiled while learning these concepts from various online resources. Let’s get started!

Understanding Your Learning Path

Our exploration will take us through diverse topics critical for mastering Machine Learning. From NumPy basics to advanced neural networks, each section will provide valuable insights and practical knowledge.

1. NumPy Basics

• NumPy Basics

2. Data Preprocessing

• Feature Selection: Imputing missing values, Encoding, Binarizing.
• Feature Scaling: Min-Max Scaling, Normalizing, Standardizing.
• Feature Extraction: CountVectorizer, DictVectorizer, TfidfVectorizer.

3. Regression Techniques

• Linear and Multiple Regression
  • Theory and Derivation
  • Linear Regression from Scratch
  • Assumptions in Linear Regression
  • Linear Regression using Scikit-learn
• Backward Elimination: Method and P-values.
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression
• Robust Regression using Theil-Sen Regression
• Pipelines in Scikit-Learn

4. Classification Techniques

• Logistic Regression
  • Logistic Regression and Gradient Descent
  • Deriving Logistic Regression
  • Logistic Regression using Gradient Descent
  • Logistic Regression using Sci-kit Learn
• Regularization
  • Regularization
  • Regularization on Logistic Regression
• K Nearest Neighbors
• Support Vector Machines
• Naive Bayes
• Decision Trees

5. Clustering Techniques

• KMeans
• Minibatch KMeans
• Hierarchical Clustering
• Application of Clustering – Image Quantization
• Application of Clustering – Outlier Detection

6. Model Evaluation

• Cross Validation and its types
• Confusion Matrix, Precision, Recall
• R Squared
• ROC Curve, AUC
• Silhouette Distance

7. Associate Rule Mining

• Apriori Algorithm
• Eclat Model

8. Reinforcement Learning

• Upper Confidence Bound Algorithm
• Thompson Sampling

9. Natural Language Processing

• Sentiment Analysis

10. Neural Networks

• What are Activation Functions
• Vanilla Neural Network
• Backpropagation Derivation
• Backpropagation in Python
• Convolutional Neural Networks
• LSTM Neural Networks

Troubleshooting Ideas

While exploring these Jupyter Notebooks, you may encounter some common issues:

• Notebook Not Loading: Make sure that you have a stable internet connection and that the URL is typed correctly.
• Kernel Crashes: Try restarting the kernel from the Jupyter menu or check for any conflicting packages.
• Missing Libraries: Install any missing libraries by running pip or conda commands in your terminal, for example: pip install numpy pandas scikit-learn.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By leveraging these Jupyter Notebooks, you can comprehensively grasp the intricate world of machine learning and deep learning. Each section builds upon the last, ensuring a seamless learning path.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.