Practical Machine Learning with TensorFlow 2.0 and Scikit-Learn

Practical Machine Learning with TensorFlow 2.0 and Scikit-Learn

Get to grips with TensorFlow 2.0 and scikit-learn

Bestseller
Created By: Samuel Holt
15.87 9.52

About This Course

Have you been looking for a course that teaches you effective machine learning in scikit-learn and TensorFlow 2.0? Or have you always wanted an efficient and skilled working knowledge of how to solve problems that can't be explicitly programmed through the latest machine learning techniques?

If you're familiar with pandas and NumPy, this course will give you up-to-date and detailed knowledge of all practical machine learning methods, which you can use to tackle most tasks that cannot easily be explicitly programmed; you'll also be able to use algorithms that learn and make predictions or decisions based on data.

The theory will be underpinned with plenty of practical examples, and code example walk-throughs in Jupyter notebooks. The course aims to make you highly efficient at constructing algorithms and models that perform with the highest possible accuracy based on the success output or hypothesis you've defined for a given task.

By the end of this course, you will be able to comfortably solve an array of industry-based machine learning problems by training, optimizing, and deploying models into production. Being able to do this effectively will allow you to create successful prediction and decisions for the task in hand (for example, creating an algorithm to read a labeled dataset of handwritten digits).

The code bundle for this course is available at https://github.com/PacktPublishing/Practical-Machine-Learning-with-TensorFlow-2.0-and-Scikit-Learn

Other Information

  • Certificate will provided in this course on Completion
  • Full lifetime access
  • Available on Mobile & Laptop

What Students Will Learn In Your Course?

  • Fundamentals of machine learning (and introducing the benefits of scikit-learn)
  • Practical implementation with comprehensive examples of canonical machine learning, and supervised and unsupervised machine learning in scikit-learn
  • How to identify a problem, select the right model, and optimize it to get the best desired outcome: insights into data
  • TensorFlow 2.0 for deep learning with neural networks
  • Deep learning and image-classification examples, and time series predictive model examples
  • Reinforcement learning, and how to implement various types with examples
  • Effectively use scikit-learn and TensorFlow in your production system, including framing a task in each task example

Are There Any Course Requirements Or Prerequisites?

Prior Python programming knowledge is mandatory for this course.

Who Are Your Target Students?

This course is for developers who are familiar with pandas and NumPy concepts and are keen to develop their machine learning methodologies and practices effectively using scikit-learn and TensorFlow 2.0.
 

Course Content

  • 62 lectures
  • 10:27:29
  • Course Overview
    00:03:52
  • Overview of the Anaconda Distribution
    00:05:34
  • Installing the Anaconda Distribution for Scikit-Learn
    00:06:15
  • Installing TensorFlow 2.0 from the Anaconda Distribution
    00:04:20
  • Install Scikit-Learn and Tensorflow 2.0 Manually Through pip
    00:03:07
  • What Is Machine Learning?
    00:09:31
  • First Scikit-Learn Model
    00:07:37
  • Overfitting and Regularization
    00:10:00
  • Probability and Statistics Review
    00:15:00
  • Probability Distribution and Metrics
    00:14:50
  • Supervised Learning and KNN
    00:10:09
  • Logistic Regression
    00:13:31
  • Nave Bayes
    00:09:43
  • Support Vector Machines
    00:11:49
  • Decision Trees
    00:15:31
  • Ensemble Methods
    00:20:59
  • K-means and Hierarchical Clustering
    00:12:08
  • Multi-Dimensional Scaling and t-SNE Manifolds
    00:12:01
  • Density Estimation
    00:08:46
  • Restricted Boltzmann Machine
    00:12:52
  • Connectivity and Density Clustering
    00:12:55
  • Gaussian Mixture Models
    00:07:32
  • Variational Bayesian Gaussian Mixture Models
    00:08:46
  • Decomposing Signals into Components
    00:08:55
  • Signal Decomposition with Factor & Independent ComponentAnalysis
    00:10:01
  • Novelty Detection
    00:06:51
  • Outlier Detection
    00:07:47
  • Locally Linear Embedded Manifolds
    00:11:38
  • TensorFlow 2.0 Overview
    00:13:13
  • TensorFlow 2.0‚As Gradient Tape
    00:08:48
  • Working with Neural Networks and Keras
    00:13:25
  • Keras Customization
    00:08:55
  • Custom Networks in Keras
    00:06:55
  • Core Neural Network Concepts
    00:13:25
  • Regression and Transfer Learning
    00:07:50
  • TensorFlow Estimators and TensorBoard
    00:09:37
  • Introduction to ConvNets
    00:08:30
  • ConvNets In Keras
    00:07:28
  • Image Classification with Data Augmentation
    00:07:44
  • Convolutional Autoencoders
    00:07:38
  • Denoising and Variational Autoencoders
    00:07:22
  • Custom Generative Adversarial Networks
    00:08:25
  • Semantic Segmentation
    00:06:32
  • Neural Style Transfer
    00:09:49
  • Using Word Embeddings
    00:09:49
  • Text Pipeline with Tokenization for Classification
    00:11:22
  • Sequential Data with Recurrent Neural Networks
    00:12:05
  • Best Practices with Recurrent Neural Networks
    00:06:24
  • Time Series Forecasting
    00:09:47
  • Forecasting with CNNs and RNNs
    00:07:42
  • NLP Language Models
    00:11:39
  • Generating Text from an LSTM
    00:09:39
  • Sequence to Sequence Models
    00:07:55
  • MT Seq2Seq with Attention
    00:09:30
  • NLP Transformers
    00:11:39
  • Training Transformers and NLP In Practice
    00:12:06
  • Basics of Reinforcement Learning
    00:13:45
  • Training a Deep Q-Network with TF-Agents
    00:13:38
  • TF-agents In Depth
    00:13:00
  • Value and Policy Based Methods
    00:13:13
  • Exploration Techniques and Uncertainty In RL
    00:13:16
  • Imitation Learning and AlphaZero
    00:13:24
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Packt Publication

  • 4.48 (6)
  • 13 Reviews
  • 6 Students
  • 935 Courses