Categories
Machine Learning, AI & Deep Learning Python Courses

Deep Learning Foundation : Linear Regression And Statistics

What you’ll learn
  • Mathematics behind R-Squared, Linear Regression,VIF and more!
  • Deep understating of Gradient descent and Optimization
  • Program your own version of a linear regression model in Python
  • Derive and solve a linear regression model, and implement it appropriately to data science problems
  • Statistical background of Linear regression and Assumptions
  • Assumptions of linear regression hypothesis testing
  • Writing codes for T-Test, Z-Test and Chi-Squared Test in python
Requirements
  • Jupyter notebook and simple python programming
Description

Hi Everyone welcome to new course which is created to sharpen your linear regression and statistical basics. linear regression is starting point for a data science this course focus is on making your foundation strong for deep learning and machine learning algorithms. In this course I have explained hypothesis testing, Unbiased estimators, Statistical test , Gradient descent. End of the course you will be able to code your own regression algorithm from scratch.

Who this course is for:
  • Python developers curious about data science
  • data science and machine leaning engineers

Take Course

Categories
Data Science Data Sciences Books Machine Learning Machine Learning, AI & Deep Learning Uncategorized

Complete Machine Learning and Data Science: Zero to Mastery

What you’ll learn
  • Become a Data Scientist and get hired
  • Master Machine Learning and use it on the job
  • Deep Learning, Transfer Learning and Neural Networks using the latest Tensorflow 2.0
  • Use modern tools that big tech companies like Google, Apple, Amazon and Facebook use
  • Present Data Science projects to management and stakeholders
  • Learn which Machine Learning model to choose for each type of problem
  • Real-life case studies and projects to understand how things are done in the real world
  • Learn best practices when it comes to Data Science Workflow
  • Implement Machine Learning algorithms
  • Learn how to program in Python using the latest Python 3
  • How to improve your Machine Learning Models
  • Learn to pre-process data, clean data, and analyze large data.
  • Build a portfolio of work to have on your resume
  • Developer Environment setup for Data Science and Machine Learning
  • Supervised and Unsupervised Learning
  • Machine Learning on Time Series data
  • Explore large datasets using data visualization tools like Matplotlib and Seaborn
  • Explore large datasets and wrangle data using Pandas
  • Learn NumPy and how it is used in Machine Learning
  • A portfolio of Data Science and Machine Learning projects to apply for jobs in the industry with all code and notebooks provided
  • Learn to use the popular library Scikit-learn in your projects
  • Learn about Data Engineering and how tools like Hadoop, Spark and Kafka are used in the industry
  • Learn to perform Classification and Regression modelling
  • Learn how to apply Transfer Learning
Requirements
  • No prior experience is needed (not even Math and Statistics). We start from the very basics.
  • A computer (Linux/Windows/Mac) with internet connection.
  • Two paths for those that know programming and those that don’t.
  • All tools used in this course are free for you to use.
Description

Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 200,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. This is a brand new Machine Learning and Data Science course just launched January 2020! Graduates of Andrei’s courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, + other top tech companies.

Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries). This course is focused on efficiency: never spend time on confusing, out of date, incomplete Machine Learning tutorials anymore. We are pretty confident that this is the most comprehensive and modern course you will find on the subject anywhere (bold statement, we know).

This comprehensive and project based course will introduce you to all of the modern skills of a Data Scientist and along the way, we will build many real world projects to add to your portfolio. You will get access to all the code, workbooks and templates (Jupyter Notebooks) on Github, so that you can put them on your portfolio right away! We believe this course solves the biggest challenge to entering the Data Science and Machine Learning field: having all the necessary resources in one place and learning the latest trends and on the job skills that employers want.

The curriculum is going to be very hands on as we walk you from start to finish of becoming a professional Machine Learning and Data Science engineer. The course covers 2 tracks. If you already know programming, you can dive right in and skip the section where we teach you Python from scratch. If you are completely new, we take you from the very beginning and actually teach you Python and how to use it in the real world for our projects. Don’t worry, once we go through the basics like Machine Learning 101 and Python, we then get going into advanced topics like Neural Networks, Deep Learning and Transfer Learning so you can get real life practice and be ready for the real world (We show you fully fledged Data Science and Machine Learning projects and give you programming Resources and Cheatsheets)!

The topics covered in this course are:

– Data Exploration and Visualizations

– Neural Networks and Deep Learning

– Model Evaluation and Analysis

– Python 3

– Tensorflow 2.0

– Numpy

– Scikit-Learn

– Data Science and Machine Learning Projects and Workflows

– Data Visualization in Python with MatPlotLib and Seaborn

– Transfer Learning

– Image recognition and classification

– Train/Test and cross validation

– Supervised Learning: Classification, Regression and Time Series

– Decision Trees and Random Forests

– Ensemble Learning

– Hyperparameter Tuning

– Using Pandas Data Frames to solve complex tasks

– Use Pandas to handle CSV Files

– Deep Learning / Neural Networks with TensorFlow 2.0 and Keras

– Using Kaggle and entering Machine Learning competitions

– How to present your findings and impress your boss

– How to clean and prepare your data for analysis

– K Nearest Neighbours

– Support Vector Machines

– Regression analysis (Linear Regression/Polynomial Regression)

– How Hadoop, Apache Spark, Kafka, and Apache Flink are used

– Setting up your environment with Conda, MiniConda, and Jupyter Notebooks

– Using GPUs with Google Colab

By the end of this course, you will be a complete Data Scientist that can get hired at large companies. We are going to use everything we learn in the course to build professional real world projects like Heart Disease Detection, Bulldozer Price Predictor, Dog Breed Image Classifier, and many more. By the end, you will have a stack of projects you have built that you can show off to others.

Here’s the truth: Most courses teach you Data Science and do just that. They show you how to get started. But the thing is, you don’t know where to go from there or how to build your own projects. Or they show you a lot of code and complex math on the screen, but they don’t really explain things well enough for you to go off on your own and solve real life machine learning problems.

Whether you are new to programming, or want to level up your Data Science skills, or are coming from a different industry, this course is for you. This course is not about making you just code along without understanding the principles so that when you are done with the course you don’t know what to do other than watch another tutorial. No! This course will push you and challenge you to go from an absolute beginner with no Data Science experience, to someone that can go off, forget about Daniel and Andrei, and build their own Data Science and Machine learning workflows.

Machine Learning has applications in Business Marketing and Finance, Healthcare, Cybersecurity, Retail, Transportation and Logistics, Agriculture, Internet of Things, Gaming and Entertainment, Patient Diagnosis, Fraud Detection, Anomaly Detection in Manufacturing, Government, Academia/Research, Recommendation Systems and so much more. The skills learned in this course are going to give you a lot of options for your career.

You hear statements like Artificial Neural Network, or Artificial Intelligence (AI), and by the end of this course, you will finally understand what these mean!

Click “Enroll Now” and join others in our community to get a leg up in the industry, and learn Data Scientist and Machine Learning. We guarantee this is better than any bootcamp or online course out there on the topic. See you inside the course!

Taught By:

Andrei Neagoie is the instructor of the highest rated Development courses on Udemy as well as one of the fastest growing. His graduates have moved on to work for some of the biggest tech companies around the world like Apple, Google, Amazon, JP Morgan, IBM, UNIQLO etc… He has been working as a senior software developer in Silicon Valley and Toronto for many years, and is now taking all that he has learned, to teach programming skills and to help you discover the amazing career opportunities that being a developer allows in life.

Having been a self taught programmer, he understands that there is an overwhelming number of online courses, tutorials and books that are overly verbose and inadequate at teaching proper skills. Most people feel paralyzed and don’t know where to start when learning a complex subject matter, or even worse, most people don’t have $20,000 to spend on a coding bootcamp. Programming skills should be affordable and open to all. An education material should teach real life skills that are current and they should not waste a student’s valuable time.   Having learned important lessons from working for Fortune 500 companies, tech startups, to even founding his own business, he is now dedicating 100% of his time to teaching others valuable software development skills in order to take control of their life and work in an exciting industry with infinite possibilities.

Andrei promises you that there are no other courses out there as comprehensive and as well explained. He believes that in order to learn anything of value, you need to start with the foundation and develop the roots of the tree. Only from there will you be able to learn concepts and specific skills(leaves) that connect to the foundation. Learning becomes exponential when structured in this way.

Taking his experience in educational psychology and coding, Andrei’s courses will take you on an understanding of complex subjects that you never thought would be possible.

See you inside the course!

Who this course is for:
  • Anyone with zero experience (or beginner/junior) who wants to learn Machine Learning, Data Science, Python
  • You are a programmer that wants to extend their skills into Data Science and Machine Learning to make yourself more valuable
  • Anyone who wants to learn these topics from industry experts that don’t only teach, but have actually worked in the field
  • You’re looking for one single course to teach you about Machine Learning and Data Science and get you caught up to speed with the industry
  • You want to learn the fundamentals and be able to truly understand the topics instead of just watching somebody code on your screen for hours without really “getting it”
  • You want to learn to use Deep Learning and Neural Networks with your projects
  • You want to add value to your own business or company you work for, by using powerful Machine Learning tools.

Categories
Data Science Data Sciences Books Machine Learning, AI & Deep Learning Uncategorized

Data Science & Machine Learning : Hands on Data Science 2020

What you’ll learn
  • You will Learn one of the most in-demand skill of 21st-century Data Science
  • Add Data science skills : python, numpy, pandas, Plotly, tableau, machine learning, statistics, probability in your resume
  • Apply linear regression and logistics regression on real dataset.
  • Crash course on python
  • Apply matrix operation with Numpy – Numerical python library
  • Visualize your data with mother of all visualization library available in Python : MatplotLIb
  • Perform Data analysis, wrangling and cleaning with pandas library
  • Get hands on with interactive visualization library Plotly
  • Getting start with data visualization tool, Tableau
  • Data Pre-processing technique – Missing data, Normalization, one hot encoding,
  • Importing data in Python from different sources, Files
  • Web Scraping to download web page and extract data
  • Data scaling and transformation
  • Exploratory Data analysis
  • Feature engineering process in Machine Learning system design
  • Machine learning theory
  • Apache spark installation : pyspark
  • Getting started with spark session
  • Mathey required for machine learning : Statistics, probability
  • Setup Data Science Virtual machine on Microsoft Azure Cloud
Requirements
  • Basic of Python programming
  • High school mathematics
Description

Welcome to Complete Ultimate course guide on Data Science and Machine learning with Python.

Have you ever thought about

How amazon gives you product recommendation,

How Netflix and YouTube decides which movie or video you should watch next,

Google translate translate one language to another,

How Google knows what is there in your photo,

How  Android speech Recognition or Apple siri understand your speech signal with such high accuracy.

If you would like algorithm or technology running behind that,  This is first course to get started in this direction.

==============================================

This course has more than 100 – 5 star rating.

What previous students have said: 

“This is a truly great course! It covers far more than it’s written in its name: many data science libraries, frameworks, techniques, tips, starting from basics to advanced level topics. Thanks a lot!  ”

This course has taught me many things I wanted to know about pandas. It covers everything since the installation steps, so it is very good for anyone willing to learn about data analysis in python /jupyter environment.”

“learning valuable concepts and feeling great.Thanks for this course.”

Good explanation, I have laready used two online tutorials on data -science and this one is more step by step, but it is good”

“i have studied python from other sources as well but here i found it more basic and easy to grab especially for the beginners. I can say its best course till now . it can be improved by including some more examples and real life data but overall i would suggest every beginner to have this course.”

“The instructor is so good, he helps you in all doubts within an average replying time of one hour. The content of the course and the way he delivers is great.”

==================================================

Why Data Science Now?

Data Scientist: The Sexiest Job of the 21st Century – By Harvard Business review

There is huge sortage of data scientist currently software industry is facing.

The average data scientist today earns $130,000 a year  by  glassdoor.

Want to join me for your journey towards becoming Data Scientist, Machine Learning Engineer.

This course has more than 100+ HD –  quality video lectures and is over 13+ hours in content.

This is first introductory course to get started data analysis, Machine learning and towards  AI algorithm implementation

This course will teach you – All Basic python library required for data analysis process.

  • Python  crash course
  • Numerical Python – Numpy
  • Pandas – data analysis
  • Matplotlib for data visualization
  • Plotly and Business intelligence tool Tableau
  • Importing Data in Python from different sources like .csv, .tsv, .json, .html, web rest Facebook API
  • Data Pre-Processing like normalization, train test split, Handling missing data
  • Web Scraping with python BeautifulSoup – extract  value from structured HTML Data
  • Exploratory data analysis on pima Indian diabetes dataset
  • Visualization of Pima Indian diabetes dataset
  • Data transformation and Scaling Data –  Rescale Data, Standardize Data, Binarize Data, normalise data
  • Basic introduction to What is Machine Learning, and  Scikit learn overview Its type, and comparison with traditional system. Supervised learning vs Unsupervised Learning
  • Understanding of regression, classification and clustering
  • Feature selection and feature elimination technique.
  • And Many Machine learning algorithm yet to come.
  • Data Science Prerequisite : Basics of Probability and statistics
  • Setup Data Science and Machine learning lab in Microsoft Azure Cloud

This course is for beginner and some experienced programmer who want to make career in Data Science and  Machine learning, AI.

Prerequisite:

  • basic knowledge in python programming (will be covered in python)
  • High School mathematics

Enroll in this course, take look at brief curriculum of this course and take first step in the wonderful world of Data.

See you in field.

Sincerely,

Ankit Mistry

Who this course is for:
  • Anyone who is interested in Data Science
  • Anyone who wants to learn – How to analyze data
  • Those who want to make a career in Data Analytics, Machine Learning, Data Science

Take Course

Categories
Machine Learning, AI & Deep Learning Uncategorized

Machine Learning Made Easy : Beginner to Advanced using R

What you’ll learn
  • R Programming, Data Handling and Cleaning, Basic Statistics, Classical Machine Learning Algorithms, Model Selection and Validation, Advanced Machine Learning Algorithms, Ensemble Learning.
  • Write your own R scripts and work in R environment.
  • Import, manipulate, clean up, sanitize and export datasets.
  • Understand basic statistics and implement using R.
  • Understand data science life cycle while understanding steps of building, validating, improving and implementing the machine learning models.
  • Do powerful analysis on data, find insights and present them in visual manner.
  • Learn classical algorithms like Linear Regression, Logistic Regression, Decision Trees and advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.
  • Know how each machine learning algorithm works and which one to choose according to the type of problem.
  • Build more than one powerful machine learning model and be able to select the best one and improve it further.
Requirements
  • Familiarity with high school mathematics.
Description

Want to know how Machine Learning algorithms work and how people apply it to solve data science problems? You are looking at right course!

This course has been created, designed and assembled by professional Data Scientists who have worked in this field for nearly a decade. We can help you understand the complex machine learning algorithms while keeping you grounded to the implementation on real business and data science problems.

We will let you feel the water and coach you to become a full swimmer in the realm of data science and Machine Learning. Every tutorial will increase your skill level by challenging your ability to foresee, yet letting you improve upon self.

We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what’s coming next.

Here is how the course is going to work:

  • Part 1 – Introduction to R Programming.
    • This is the part where you will learn basic of R programming and familiarize yourself with R environment.
    • Be able to import, export, explore, clean and prepare the data for advance modelling.
    • Understand the underlying statistics of data and how to report/document the insights.
  • Part 2 – Machine Learning using R
    • Learn, upgrade and become expert on classic machine learning algorithms like Linear Regression, Logistic Regression and Decision Trees.
    • Learn which algorithm to choose for specific problem, build multiple model, learn how to choose the best model and be able to improve upon it.
    • Move on to advance machine learning algorithms like SVM, Artificial Neural Networks, Reinforced Learning, Random Forests and Boosting and clustering algorithms like K-means.

Features:

  • Fully packed with LAB Sessions. One to learn from and one for you to do it yourself.
  • Course includes R code, Datasets and other supporting material at the beginning of each section for you to download and use on your own.
  • Quiz after each section to test your learning.

Bonus:

  • This course is packed with 5 projects on real data related to different domains to prepare you for wide variety of business problems.
  • These projects will serve as your step by step guide to solve different business and data science problems.
Who this course is for:
  • Anyone interested in Data Science and Machine Learning.
  • Students who want a head start in Data Science field.
  • Data analysts who want to upgrade their skills in Machine Learning.
  • People who want to add value to their work and business by using Machine Learning.
  • People with basics understanding of classical machine learning algorithms like linear regression or logistic regression, but want to learn more about it.
  • People interested in understanding the application of machine learning algorithms on real business problems.
  • People interested in understanding how a machine learning algorithm works and what’s the math behind it.

Take Course

Categories
Machine Learning, AI & Deep Learning MATLAB Tutorials

MATLAB Complete Stanford University 4 weeks Short Course

Stanford University offers free online programming courses for COVID-19 pandemic

Stanford University one of the prestigious Universities across the globe has announced several online short-term courses for those who want to keep learning during the COVID-19 pandemic. Although there are about 64 online courses offered by Stanford University we selected the 8 online programing courses which might interest programmers all around the world can join. Each course comes with a project that you might be able to put on your resume.

1. CS50: Introduction to Computer Science
 

Duration – 11 weeks
Time Commitment – 10-20 hours per week
 

An entry-level course, Introduction to Computer Science teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, PHP, and JavaScript plus SQL, CSS, and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming.
 

2. CS50’s Introduction to Artificial Intelligence with Python
 

Duration – 7 weeks  
Time Commitment – 10-30 hours per week
 

This course explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.
 

3. CS50’s Introduction to Game Development
 

Duration – 12 weeks
Time Commitment – 6-9 hours per week
 

Via lectures and hands-on projects, the course explores principles of 2D and 3D graphics, animation, sound, and collision detection using frameworks like Unity and LÖVE 2D, as well as languages like Lua and C#. By class’s end, you’ll have programmed several of your own games and gained a thorough understanding of the basics of game design and development.
 

4. CS50’s Web Programming with Python and JavaScript
 

Duration – 12 weeks
Time Commitment – 6-9 hours per week
 

The course includes topics like database design, scalability, security, and user experience. Through hands-on projects, you’ll learn to write and use APIs, create interactive UIs, and leverage cloud services like GitHub and Heroku. By course’s end, you’ll emerge with knowledge and experience in principles, languages, and tools that empower you to design and deploy applications on the Internet.
 

5. CS50’s Mobile App Development with React Native
 

Duration – 13 weeks
Time Commitment – 6-9 hours per week
 

This course enables your transitioning from web development to mobile app development with React Native. The course introduces you to modern JavaScript (including ES6 and ES7) as well as to JSX, a JavaScript extension. Through hands-on projects, you’ll gain experience with React and its paradigms, app architecture, and user interfaces. The course culminates in a final project for which you’ll implement an app entirely of your own design.
 

6. Using Python for Research
 

Duration – 5 weeks
Time Commitment – 4-8 hours per week
 

This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings. Using a combination of a guided introduction and more independent in-depth exploration, you will get to practice your new Python skills with various case studies chosen for their scientific breadth and their coverage of different Python features.

Categories
Machine Learning, AI & Deep Learning

Artificial Intelligence MasterClass Complete PDF Course 2020

About the Tutorial

This tutorial provides introductory knowledge on Artificial Intelligence. It would come to a great help if you are about to select Artificial Intelligence as a course subject. You can briefly know about the areas of AI in which research is prospering

Audience

This tutorial is prepared for the students at beginner level who aspire to learn Artificial Intelligence.

Prerequisites

The basic knowledge of Computer Science is mandatory. The knowledge of Mathematics, Languages, Science, Mechanical or Electrical engineering is a plus.

Categories
Machine Learning, AI & Deep Learning Udemy Courses

Machine Learning Practical Workout | 8 Real-World Projects

Build 8 Practical Projects and Go from Zero to Hero in Deep/Machine Learning, Artificial Neural Networks

What you’ll learn

Machine Learning Practical Workout | 8 Real-World Projects – Course Site

  • Deep Learning Practical Applications
  • Machine Learning Practical Applications
  • How to use ARTIFICIAL NEURAL NETWORKS to predict car sales
  • How to use DEEP NEURAL NETWORKS for image classification
  • Learn how to use LE-NET DEEP NETWORK to classify Traffic Signs
  • How to apply TRANSFER LEARNING for CNN image classification
  • How to use PROPHET TIME SERIES to predict crime
  • Learn how to use PROPHET TIME SERIES to predict market conditions
  • How to develop a NATURAL LANGUAGE PROCESSING MODEL to analyze Reviews
  • How to apply NATURAL LANGUAGE PROCESSING to develop spam folder
  • Learn how to use USER-BASED COLLABORATIVE FILTERING to develop a recommender system

Requirements

  • Deep Learning and Machine Learning basics
  • PC with an Internet connection

Description

“Deep Learning and Machine Learning are one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects.
Machine learning is the study of algorithms that teach computers to learn from experience. Through experience (i.e.: more training data), computers can continuously improve their performance. Deep Learning is a subset of Machine learning that utilizes multi-layer Artificial Neural Networks. The more hidden layers added to the network, the more “deep” the network will be, the more complex nonlinear relationships that can be modeled.
The purpose of this course is to provide students with knowledge of key aspects of deep and machine learning techniques in a practical, easy and fun way. The course provides students with practical hands-on experience in training deep and machine learning models using real-world datasets.

This course covers several techniques in a practical manner, the projects include but not limited to:

(1) Train Deep Learning techniques to perform image classification tasks.

(2) Develop prediction models to forecast future events such as future commodity prices using state of the art Facebook Prophet Time series.

(3) Develop Natural Language Processing Models to analyze customer reviews and identify spam/ham messages.

(4) Develop recommender systems such as Amazon and Netflix movie recommender systems.

therefore, the course has no prerequisites and is open to any student with basic programming knowledge. Students who enroll in this course will master deep and machine learning models and can directly apply these skills to solve real-world challenging problems.”

Who this course is for:

  • Data Scientists who want to apply their knowledge on Real-World Case Studies
  • Deep Learning practitioners who want to get more Practical Assignments
  • Machine Learning Enthusiasts who look to add more projects to their Portfolio
  • Content From: https://www.udemy.com/course/deep-learning-machine-learning-practical/
Categories
Machine Learning, AI & Deep Learning PHP Scripts | Source Code Python Books Python Courses

Python Face Detection System with Source Code

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Categories
Machine Learning, AI & Deep Learning TensorFlow

Complete Tensorflow 2 and Keras Deep Learning Bootcamp

Learn to use Python for Deep Learning with Google’s latest Tensorflow 2 library and Keras!

What you’ll learn

Complete Tensorflow 2 and Keras Deep Learning Bootcamp Course Site

  • Learn to use TensorFlow 2.0 for Deep Learning
  • Leverage the Keras API to quickly build models that run on Tensorflow 2
  • Perform Image Classification with Convolutional Neural Networks
  • Use Deep Learning for medical imaging
  • Forecast Time Series Data with Recurrent Neural Networks
  • Use Generative Adversarial Networks (GANs) to generate images
  • Use deep learning for style transfer
  • Generate text with RNNs and Natural Language Processing
  • Serve Tensorflow Models through an API
  • Use GPUs for accelerated deep learning

Requirements

  • Know how to code in Python
  • Some math basics such as derivatives

Description

This course will guide you through how to use Google’s latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google’s TensorFlow 2 framework in a way that is easy to understand.We’ll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0’s official API) to quickly and easily build models. In this course, we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!

We also have plenty of exercises to test your new skills along the way!

This course covers a variety of topics, including

  • NumPy Crash Course
  • Pandas Data Analysis Crash Course
  • Data Visualization Crash Course
  • Neural Network Basics
  • TensorFlow Basics
  • Keras Syntax Basics
  • Artificial Neural Networks
  • Densely Connected Networks
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • AutoEncoders
  • GANs – Generative Adversarial Networks
  • Deploying TensorFlow into Production
  • and much more!

1. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines.

TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to publication. TensorFlow 2.0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performance

Who this course is for:

  • Python developers interested in learning about TensorFlow 2 for deep learning and artificial intelligence
Categories
Machine Learning, AI & Deep Learning Wordpress Courses

Artificial Intelligence Website Creation 2018 (No Coding)

Artificial Intelligence Tools Taught to Create Websites At Blazing Speed With No Experience

What you’ll learn

  • Build websites and landing pages with implementing Artificial Intelligence Technology
  • Create a chatbot for your site in the fastest time possible using Artificial Intelligence.
  • Make an Apple TV app with no coding and learn the website portal used to create the same with Artificial Intelligence

Requirements

  • There is no pre-requisite needed and you do not need to know any coding to create sites.

Description

This game-changing course will cover artificial intelligence tools in website. Chatbot design and analytics which will help you to create website in minutes.

I will teach you to easily create websites in the fastest time possible and customize your site look and feel according to your requirement in a simple drag-and-drop timeline by talking to chatbots.

Why learn this artificial intelligence game-changing course and how is this a differentiator?

This course can change your life as a web developer or marketer. With no coding experience. You can create amazing looking websites and pave the path for unlimited designs and interchange content and play god using artificial intelligence tech.

This course will save you a ton of time when it comes to creating websites without using any expensive website design tool. Without using complex tools like wordpress etc. You do not even need to outsource websites to other agencies ever again as you can do it yourself now in minutes.

The question is “Are you ready to get into action and embrace the power to leverage artificial intelligence in website creation?”. 

If yes, plunge into action right away by signing up NOW. All the best to become an Artificial Intelligence Web-Creator..

#ArtificialIntelligence ##ArtificialIntelligenceWebsiteCreation ##ArtificialIntelligenceWebDevelopment ##ArtificialIntelligencewebsitemaking #MakeWebsiteswithArtificialIntelligenceWho this course is for:

  • Anyone who want to create websites at incredible speed with Artificial Intelligence
  • Entrepreneurs who have passion to learn website creation with no coding involved in the process.
  • Developers who want to be aware of artificial intelligence technologies to create webpages and sites

Size: 302.62 MB

If you are facing any problem regarding downloading course then do refer Complete Downloading Guide & Torrent Deadline.