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Tensor Flow #Recipe1 | Machine Learning Full Course Recipe#4 | All In One Code | All In One Code

Official TensorFlow Site : https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough This AllInOneCode video on "What is Machine Learning" (Machine Learning Blog: https://goo.gl/fe7ykh ) gives an introduction to Machine Learning and its various types. Below are the topics covered in this tutorial: 1. Evolution of Machine Learning 2. What is Machine Learning? 3. Types of Machine Learning 4. Supervised Learning 5. Unsupervised Learning 6. Reinforcement Learning Subscribe to our channel to get video updates. Hit the subscribe button above. Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm #MachineLearning #PythonMachineLearning #PythonMachineTraining How it Works? 1. This is a 5 Week Instructor led Online Course,40 hours of assignment and 20 hours of project work 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will be working on a real time project for which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - - - - About the Course Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you: 1. Master the Basic and Advanced Concepts of Python 2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs 3. Master the Concepts of Sequences and File operations 4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python 5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application 6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn 7. Master the concepts of MapReduce in Hadoop 8. Learn to write Complex MapReduce programs 9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python 10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics 11. Master the concepts of Web scraping in Python 12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience - - - - - - - - - - - - - - - - - - - Why learn Python? Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license. Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain. -------------------------------------------TODAY'S CODE ----------------------------------------------------- #Code For Iris DataSets If You Want Code For Fashion_Mnist Datasets Comment Me from __future__ import absolute_import, division, print_function, unicode_literals import os import matplotlib.pyplot as plt import tensorflow as tf print("TensorFlow version: {}".format(tf.__version__)) print("Eager execution: {}".format(tf.executing_eagerly())) train_dataset_url = "https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv" train_dataset_fp = tf.keras.utils.get_file(fname=os.path.basename(train_dataset_url), origin=train_dataset_url) print("Local copy of the dataset file: {}".format(train_dataset_fp)) !head -n5 {train_dataset_fp} # column order in CSV file column_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'] feature_names = column_names[:-1] label_name = column_names[-1] print("Features: {}".format(feature_names)) print("Label: {}".format(label_name)) class_names = ['Iris setosa', 'Iris versicolor', 'Iris virginica'] batch_size = 32 train_dataset = tf.data.experimental.make_csv_dataset( train_dataset_fp, batch_size, column_names=column_names, label_name=label_name, num_epochs=1) features, labels = next(iter(train_dataset)) print(features) plt.scatter(features['petal_length'], features['sepal_length'], c=labels, cmap='viridis') plt.xlabel("Petal length") plt.ylabel("Sepal length") plt.show()

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