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
In this course, you’re going to master the fundamentals of machine learning in Python with TensorFlow. You will learn about analyzing data with Linear Regression, cleaning data with Feature Engineering, making predictions with Logistic Regression, understanding, training, and evaluating Deep Neural Networks, and applying Machine Learning to real-world problems.
After completing the course, you will be able to design and train a deep neural network to recognize handwriting and apply this knowledge in many other machine learning tasks.
Are you a CTO, tech leader, or software developer wondering what the Machine Learning hype is all about? Would you like to start experimenting with Neural Networks in your organization? Then this is the course for you! Introduction To Machine Learning With TensorFlow will help you:
Check out this webinar, which is based on content from the course. The webinar demonstrates how to build a Python app that uses a deep neural network to recognize handwritten digits and transcribe them to text.
‘A fantastic primer for Machine Learning, Python, and TensorFlow!’
This course uses the Python language and the TensorFlow library and 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.
In the course, you will learn:
– linear regression
– underfitting and overfitting
– partitioning data sets
– binning, scaling, and scrubbing data
– creating one-hot encoding features
– building feature crosses
– logistic regression
– true positives, true negatives, false positives, and false negatives
– accuracy, precision, and recall
– ROC curve and AUC value
– dealing with prediction bias
– L1 and L2 regularization
– neural networks
– hidden layers and activation functions
– backpropagation and interative learning
– one-to-many networks
– softmax layers
And much more!
Are you interested?
– Code Example: Set Up Python In Visual Studio
– Single Linear Regression
– Code Example: Analyze Data With Single Linear Regression
– Under And Overfitting
– Partitioning Data
– Code Example: Use A Validation And A Test Set
– Feature Engineering
– Code Example: Correlate And Engineer Features
– Multiple Linear Regression
– Code Example: Analyze Data With Multiple Linear Regression
– Feature Crosses
– Code Example: Train Your Model Using Feature Crosses
– Introduction To Logistic Regression
– Evaluating Logistic Models (free preview)
– ROC, AUC, And Prediction Bias
– Code Example: Analyze Data With Logistic Regression (free preview)
– Code Example: Regularize A Machine Learning Model
– Introduction To Neural Networks
– Training A Neural Network With Backpropagation
– Iteratively Train Neural Networks
– Code Example: Analyze Data With A Neural Network
– Multiclass Neural Networks
– Code Example: Analyze Handwriting With A Multiclass Neural Network
– The course starts when you enroll
– You can take as long as you like to complete the course
– You have Lifelong Access to all content
– You are a Python developer (any level)
– Or: you are a C# developer wanting to learn Python
– You are interested in Machine Learning
– Fluent in English
– An internet-connected computer
– Visual Studio running on Windows (can be in a Virtual Machine)
– A good knowledge of Python, or a willingness to learn
– Understand the fundamentals of machine learning
– Build machine learning models in Python with TensorFlow
– Create neural networks to solve machine learning problems
– Master data processing and feature engineering
Hour Response Time
You will receive Lifelong Access to all course content.
You can access our Facebook Community Group 24/7 and chat with the instructor team and your fellow students.
You can ask unlimited questions to the instructor team. This course offers response times of Under 24 Hours to all your inquiries.
I am a blogger, investor, serial entrepreneur, and the author of 11 successful IT courses in the Udemy marketplace. My career spans over two decades during which I’ve been a Founder twice and CTO three times, and I have launched two lean startups in The Netherlands.
I became a Microsoft Certified Trainer in 2005 and started training classes in .NET development, web design, and Microsoft back-office servers. Today I use my extensive knowledge of IT to help CTO’s, architects, and other tech professionals with their leadership, communication, and technical skills.
I hold MCSA and MCSD certifications from Microsoft and am a certified Microsoft Trainer and Scrum master. I also speak fluent English, Dutch, and German.