English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 59m | 412 MB
Transcend your machine learning experience by leveraging it with the cutting edge library – TensorFlow
With this course you’ll learn to take your data analysis and Python programming skills to the next level via Machine Learning using TensorFlow. This course focuses on key Machine Learning techniques and algorithms and you’ll apply them practically using TensorFlow models in a hands-on approach. Each section covers a specific Machine Learning task and you will implement it on your system with TensorFlow models. For example, you will learn Logistic Regression and will then implement it with TensorFlow for your analysis tasks. You’ll implement techniques such as Classification and Clustering effectively using TensorFlow. Similarly, this course takes you through different ML tasks/algorithms and teaches you to implement them in your applications/systems.
This hands-on course covers important aspects of Machine Learning with TensorFlow using practical examples. Throughout the course, you’ll learn how different algorithms work and follow step-by-step instructions to implement them using different example projects.
What You Will Learn
- Leverage the power of TensorFlow and machine learning
- Use TensorFlow for regression machine learning problems
- Solve classification machine learning tasks with TensorFlow
- Solve clustering machine learning problems with TensorFlow
- Use TensorFlow to build an artificial neural network from scratch
- Build a convolutional neural network for an image dataset in TensorFlow
Table of Contents
Getting Started with TensorFlow
1 Installing TensorFlow Environment
2 TensorFlow Basic Syntax
3 TensorFlow Graphs
4 The Course Overview
5 Variables and Placeholders
Apply Regression Techniques in TensorFlow
6 Housing Price Prediction Model with Estimator API
7 Regression from Scratch for 1 Million Data Points – Part 1
8 Regression from Scratch for 1 Million Data Points – Part 2
9 What is Machine Learning
Implementing Classification Techniques Using TensorFlow
10 Performing Classification Techniques on Pima Indians Diabetes Dataset – Part 1
11 Performing Classification Techniques on Pima Indians Diabetes Dataset – Part 2
12 Performing Classification Techniques on Pima Indians Diabetes Dataset – Part 3
13 Predicting Class of Income on Census Data – Part 1
14 Predicting Class of Income on Census Data – Part 2
15 Predicting Class of Income on Census Data – Part 3
Implement Clustering Techniques in TensorFlow
16 Apply K-Means Clustering on the Blob Dataset Part – 1
17 Apply K-Means Clustering on the Blob Dataset Part – 2
18 Introduction to K-Means Clustering
Create Your Own Artificial Neural Network
19 Part 1 – Data Preprocessing
20 Part 2 – Let’s Create the ANN
21 Part 3 – Making Predictions and Evaluating Models
22 What is Deep Learning
Build Convolutional Neural Network Using Image Dataset
23 Part 1 – Import MNIST Data from TensorFlow
24 Part 2 – Create Placeholders and Layers
25 Part 3 – Optimize and Run Sessions
26 What Is a Convolutional Neural Network
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