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Get to grips with TensorFlow. Become an AI, Machine Learning, and Deep Learning expert.

What you will learn

Practical implementation with comprehensive examples of canonical machine learning, and supervised and unsupervised machine learning

Deep learning and image-classification examples, and time series predictive model examples

Effectively use TensorFlow in your production system, including framing a task in each task example

Fundamentals of machine learning

Description

Have you been looking for a course that teaches you effective machine learning in TensorFlow? 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. 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.


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TensorFlow experts earn up to $204,000 USD a year, with the average salary hovering around $148,000 USD according to 2023 statistics. By passing this certificate, which is officially recognized by Google, you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow developer! If you pass the exam, you will also be part of Google’s TensorFlow Developer Network where recruiters are able to find you. The goal of this course is to teach you all the skills necessary for you to go and pass the exam and get your TensorFlow Certification from Google so you can display it on your resume, LinkedIn, Github and other social media platforms to truly make you stand out. 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.

English
language

Content

Machine Learning ZERO to HERO – Hands-on with Tensorflow

Introduction to Machine Learning with Tensorflow
Understanding Machine Learning
How do Machines Learns
Uses of Machine Learning
Examples with tensorflow by Google
Setting up the Workstation
Understanding program languages
Understanding and Functions of Jupyter
Learning of Jupyter installation
Understanding what Anaconda cloud is
Installation of Anaconda for Windows
Installation of Anaconda in Linux
Using the Jupyter notebook
Getting started with Anaconda
Determining options for Cloudberry
Introduction to Third Party Libraries
Numpy-Array
Numpy-Array Continue
Arrays
Arrays Continue
Indexing
Indexing Continue
Universal Functions
Introoduction to Pandas
Pandas Series
Pandas Series Continue
Import Randin
Import Randin Continue
Paratmeters
Indexing and Database
Missing Data
Missing Data-Groupby
Missing Data-Groupby Continue
Concat-Merge-Join
Operations
Import-Export
Python Visualisation
Mat Plotting
Multiple Plot Subsections
API Functionality
Title of the Plot
Change Size of Articles
Two Different Crops
Mat Plotting Label
Marker Color
Create a New Dataframe
Change the Style
Index and Value
Seaborn-Statistical Data Visualization
Seaborn library
Jointplot
Pairplot
Barplot
Boxplot
Stripplot
Matrix
Matrix Continue
Grid
Grid Continue
Style
Python Libraries Conclusion
Introduction To Conda Envirement
Scikit Learn
Scikit Learn Continue
Datasets
California Dataset
Data Visualization
Datavisualization Continue
Downloading a Test Data
Population Parameter
Processing
Null Values with Median Value
Replace Missing Values
Label Enconder
Import Labelencoder
Custom Transformation
Transformer Custom Transformer
Housing with Custom Colums
Numeric Hosing Data
Liner Regression
Fine Tuning Model
Fine Tuning Model Continue
Quick-Recap
Tensorflow
Tensorflow-Hello-World
Basic Ops
Basic Ops Continue
More on Basic Ops
Eager-Mode
Concept
Linear-Regression
Linear-Model
Matrix Multiplication Function
Practice for a Simple Linear Model
Cost Function
Creative Optimizer
RR Input and Output Value
Logistic-Regression
Global Variabales Initializer
Run Optimizer
Create a Range
Introduction to Neural Networks
Basic-Concepts
Activative Functions
Activative Functions Input to Output
Classification Functions
Tensorflow-Playground
Mnist-Dataset
Mnist-Dataset Continue
More on Mnist-Dataset
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