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Machine Learning Books that beginners should focus on
- How to download free datasets
- What tools and machine learning libraries you need
- Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data
- Preparing data for analysis, including k-fold Validation
- Regression analysis to create trend lines
- Clustering, including k-means and k-nearest Neighbors
- The basics of Neural Networks
- Bias/Variance to improve your machine learning model
- Decision Trees to decode classification
- How to build your first Machine Learning Model to predict house values using Python
- The fundamentals of machine learning.
- Each of the buzzwords defined!
- 20 real-world applications of machine learning.
- How to predict when a customer is about to churn (and prevent it from happening).
- How to "upsell" to your customers and close more sales.
- How to deal with missing data or poor data.
- Where to find free datasets and libraries.
- Exactly which machine learning libraries you need.
- Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the TensorFlow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets
- Apply practical code examples without acquiring excessive machine learning theory or algorithm details