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Practical Machine Learning by Example in Python

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A Deep Dive into Building Machine Learning and Deep Learning models

What does this course teach you?

Course Structure and Development Environment
Course Structure and Development Environment Course Quick Tips Introduction to Jupyter Notebook Jupyter notebook: Text Cells Jupyter notebook: Code Cells Jupyter notebook: Math Markup and Magic Commands Introduction to Notebooks Sharing Colab Notebooks Artificial Intelligence, Machine Learning, and Deep Learning What you learned in this section
Python Quick Start
About this section Basic Syntax String formatting Literal string interpolation Experiment with string formatting Type conversion Flow control Lists Dot product Dictionaries Defining functions Classes File I/O and Modules Plot several math functions Prompting for passwords What you learned in this section
Example: Logistic Regression
The problem Machine Learning Development Process Data analysis Analyze Iris flower data set The model The forward function Loss and cost functions Gradient descent Experiment with gradient descent Backpropagation Model training Making predictions Training a model Optional: Wine Classification Test vs. train accuracy Speeding up training Improving the model What you learned in this section
Foundations: NumPy
What is NumPy and why it is needed? Creating data with NumPy Basic operations Experiment with NumPy Introduction to Linear Regression Linear Regression Example Experiment with Linear Regression More Complex Models Statistics and linear algebra Visualizing data Images Visualizing data Reshaping data What you learned in this section
Foundations: Tensorflow
About this section Model example Model layers Activation functions Training example Train a basic model Loss functions Optimizers Experiment with optimizers Prediction example Saving and restoring models The Three Body Problem What you learned in this section
Example: Image recognition
The problem Data analysis Analyze MNIST data set Model selection Data preparation CNN Model Layers Model definition Model training Training a model Making predictions Error analysis Prediction and error analysis Hyperparameter tuning Hyperparameter tuning example Model improvement Common questions Optional: Real images Optional: Other image types What you learned in this section
Foundations: Pandas
What is Pandas and why is it useful? Loading and inspecting data example Indexing and selecting data example Experiment with Pandas Sorting and transforming data example Aggregations example Visualizing data Visualizing data with Pandas What you learned in this section
Example: Recommendations
The problem Data analysis Analyze MovieLens data set Model selection Data preparation Prepare data Embedding layers Model definition Model training Training the model Predictions Making predictions Error analysis Making recommendations and error analysis Common questions What you learned in this section
Example: Sentiment Analysis
The Problem Data Analysis Analyze Sentiment Data Set Supervised Learning Data Preparation Prepare Data Model Definition Model Training Training the Model Transfer Learning with BERT Transfer Learning Example Fine Tuning and Prediction Transfer Learning with BERT What you learned in this section
Example: Fraud detection
The problem Data analysis Analyze credit card data set Unsupervised learning Data preparation Model definition Model training Training the model Making predictions Prediction and error analysis Common questions Improving the model What you learned in this section

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