Machine Learning With TensorFlow
This course will teach you how to build machine learning models in Python with TensorFlow, using linear regression, logistic classification, and neural networks
Are you ready for the robot revolution?
Experts predict that robots will wipe out 50% of all jobs. We’ll feel the first shocks in the next 10 years, when self-driving cars are poised to put roughly 10 million people out of a job.
If you want to keep your job, you will have to make sure that you’re employed in sexy high-tech niche areas that remain relevant for years. You need to make sure it’s you who is building the AI’s, and not you being replaced by them.
So let’s focus on machine learning, which is all about teaching robots how to think and learn. It’s a super-popular field right now, and there are loads of cool libraries available to get you started. The most prominent coding environment you'll encounter in the machine learning field is the Python language and Google's TensorFlow library.
Would you like me to bring you up to speed?
I have created this machine learning course specially for you. This course will teach you how to become fluent in building machine learning models in Python with TensorFlow.
The course covers Linear Regression, Feature Engineering, Logistic Regression, understanding and evaluating Classifiers, building Neural Networks, optimizing Gradient Descent Learning and performing Data Processing.
We will cover machine learning techniques like regression, classification, building histograms, binning and scrubbing data, creating sparse one-hot feature columns, building features crosses, evaluating models by calculating accuracy, precision, recall, ROC and AUC, building neural networks with hidden layers, adding softmax layers, and much more.
In the course, I cover each topic in detail. I start with a lecture on the theory and background of the issue, and then follow up with a code example that demonstrates how to put the topic in practice with Python code and the TensorFlow library.
Class Curriculum
-
StartSection Introduction
-
StartCode Exercise: Set Up Python In Visual Studio
-
StartSingle Linear Regression
-
StartCode Exercise: Analyze Data With Simple Linear Regression
-
StartUnder- And Overfitting
-
StartPartitioning Data
-
StartCode Exercise: Use A Validation And A Test Set
-
StartFeature Engineering
-
StartCode Exercise: Correlate And Engineer Features
-
StartMultiple Linear Regression
-
StartCode Exercise: Analyze Data With Multiple Linear Regression
-
StartFeature Crosses
-
StartCode Exercise: Train Your Model Using Feature Crosses
-
StartSection Recap
-
StartYou've Earned A New Badge
-
StartYour Skill Progress
-
StartSection Introduction
-
StartIntroduction To Logistic Regression
-
PreviewEvaluating Logistic Models
-
StartROC, AUC, And Prediction Bias
-
PreviewCode Exercise: Analyze Data With Logistic Regression
-
StartRegularization
-
StartCode Exercise: Regularize A Machine Learning Model
-
StartSection Recap
-
StartYou've Earned A New Badge
-
StartYour Skill Progress