Master Artificial Intelligence 2022 : Build 6 AI Projects
Saturday, April 30, 2022
Master Artificial Intelligence 2022 : Build 6 AI Projects - Learn Artificial Intelligence with Python. Create Advanced Artificial Intelligence (AI) Applications with Python
Preview this Course
What you'll learn
- Machine Learning
- Deep Learning
- Natural Language Processing
- Artificial Neural Network
- Convolutional Neural Network
- Recurrent Neural Network
- Quizzes and exercises
- Understand the theory
- Build and train AI model
- Apply AI in business applications
- Build, train, deploy AI models in business
- Understand the concept of Explainable AI
Requirements
- For taking up this course you need to be enthusiastic and self confident.
- You need to have good knowledge of programming and basic mathematical skills.
- For taking up this course you need to be enthusiastic and self confident.
- A willingness to learn and practice.
Description
Are you ready to master Artificial Intelligence skills?
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow,
they will have a great impact on our quality of life.
Artificial intelligence (AI) is one of the top tech fields to be in right now!
Financial institutions, legal institutions, media companies, and insurance companies are all figuring out ways to use artificial intelligence (ai) to their advantage. From fraud detection to writing news stories with natural language processing(NLP) and reviewing law briefs, AI’s reach is extensive.
If you want to build super-powerful applications in artificial intelligence(ai).
Then, you are at the right place.
This course will provide you with in-depth knowledge on a very hot topic i.e., Artificial Intelligence(AI).
The purpose of this course is to provide you with knowledge of key aspects of modern AI without any intimidating mathematics and in a practical, easy, and fun way. The course provides students with practical hands-on experience using real-world datasets.
This course will cover the following topics:-
1. Natural Language Processing (NLP).
2. Artificial Neural Network (ANN).
3. Convolutional Neural Network (CNN).
4. Recurrent Neural Network. (RCN)
5. Machine Learning (ML).
6. Deep Learning (DL).
This course will take you through the basics to an advanced level in all the mentioned four topics.
After taking this course, you will be confident enough to work independently on any projects on these topics.
There are lots and lots of exercises for you to practice In this Python Data Science Course and also a 5 Bonus Data Science Project "Sentiment Analysis", "Drug Prescription", "Detecting Pneumonia from X-rays", "Stock Market Prediction", "Fruits Recognition" and "Face emotion Recognition".
In this Sentiment Analysis project, you will learn how to Extract and Scrap Data from Social Media Websites and Extract out Beneficial Information from these Data for Driving Huge Business Insights.
In this Drug Prescription project, you will learn how to Deal with Data having Textual Features, you will also learn NLP Techniques to transform and Process the Data to find out Important Insights.
In this Detecting Pneumonia from X-rays project, you will learn how to solve Image Classification Tasks using Deep Neural Networks such as ResNet which is a High Level CNN Architectures.
In this Stock Market Prediction project, you will learn to analyze and the Stock Market Prices using Time Series Forecasting, Advanced Deep Learning Models and different Statistical features.
In this Fruits Recognition project, you will learn how to solve a complicated Image Classification Task with Multiple Classes using various Deep Learning Architectures and Compare the Result.
In this Face Expression Recognizer project, you will learn to use Computer Vision Techniques to detect Human Emotions such as Angry, Sad, Happy, Disgust, Fear etc. to build a Facial Emotion Detector.
Instructor Support - Quick Instructor Support for any queries.
I'm looking forward to see you in the course!
You will have access to all the resources used in this course.
Who this course is for:
- This course is for everyone who wants to master AI skills.
- This course is for students who have already good knowledge of machine learning and have good programming skills.
- Job-seekers, Software Engineers and Data Scientists who want to level up their career
- Students and professionals who want to improve the training capabilities
- Machine Learning Engineer
- Just Passion for Learning
- Everyone who wants to improve their Python programming skills
- Anyone interested in Artificial Intelligence, Machine Learning or Deep Learning
Deep Learning Bootcamp with 5 Capstone Projects
Deep Learning Bootcamp with 5 Capstone Projects
Learn about Deep Learning - ANN, CNN, RNN, LSTMs along with Real Time Capstone Projects
Preview this Course
What you'll learn
- Learn about Artificial Neural Networks.
- Learn about the different Layers present in a Neural Networks.
- Learn about different Activation Functions used in a Neural Network.
- Learn to Hyper tune the Neural Networks to Improve Performance.
- Implement Artificial Neural Networks to solve real world Problems.
- Learn about Convolutional Neural Networks.
- Learn about different Layers of a Convolutional Neural Networks.
- Learn about Dropout and Callbacks in Neural Networks.
- Learn about the Recurrent Neural Networks.
- Implement the LSTMs to solve Sequential Problems.
- Use Real World Examples
Requirements
- For taking up this course you need to be enthusiastic and self confident.
- You need to have good knowledge of programming and basic mathematical skills.
- Determination and Desire to Learn new things.
- No prior knowledge is needed.
- Start from the basics and gradually build your knowledge in the subject.
Description
Are you ready to master Deep Learning skills?
Deep Learning is a technology using which we can solve highly computational problems such as Image Processing, Image Classification, Image Segmentation, Image tagging, sound classification, video analysis, etc.
Deep Learning is becoming a buzzword these days, and If you want to learn Deep Learning then It is very important for you that you should have a proper plan regarding that.
Before Learning Deep Learning you must have learned Machine Learning and must possess good knowledge of the Python programming language.
If you want to build super-powerful applications in Deep Learning. Then, you are at the right place.
This course will provide you with in-depth knowledge on a very hot topic i.e., Deep Learning.
The purpose of this course is to provide you with knowledge of key aspects of Deep Learning without any intimidating mathematics and in a practical, easy, and fun way. The course provides students with practical hands-on experience using real-world datasets.
This course will cover the following topics:-
1. Deep Learning (DL).
2. Artificial Neural Network (ANN).
3. Convolutional Neural Network (CNN).
4. Recurrent Neural Network. (RCN)
5. Learn to Implement the LSTMs.
This course will take you through the basics to an advanced level in all the mentioned four topics.
After taking this course, you will be confident enough to work independently on any projects on these topics.
There are lots and lots of exercises for you to practice In this Deep Learning Course and also a 5 Bonus Deep Learning Project "Stock Market Prediction", "Fruits Identification System", "Face Expression Recognizer", "Detecting Pneumonia from Chest X-rays", and "Optimizing Crop Production".
In this Optimizing Crop Production, you will learn about Precision Farming using Data Science Technologies such as Clustering Analysis and Classification Analysis. You will be able to Recommend the best Crops to Farmers to Increase their Productivity.
In this Detecting Pneumonia from X-rays project, you will learn how to solve Image Classification Tasks using Deep Neural Networks such as ResNet which is a High-Level CNN Architectures.
In this Stock Market Prediction project, you will learn to analyze, and the Stock Market Prices using Time Series Forecasting, Advanced Deep Learning Models, and different Statistical features.
In this Fruits Recognition project, you will learn how to solve a complicated Image Classification Task with Multiple Classes using various Deep Learning Architectures and Compare the Result.
In this Face Expression Recognizer project, you will learn to use Computer Vision Techniques to detect Human Emotions such as Angry, Sad, Happy, Disgust, Fear, etc. to build a Facial Emotion Detector.
Instructor Support - Quick Instructor Support for any queries.
I'm looking forward to see you in the course!
You will have access to all the resources used in this course.
Who this course is for:
- This course is for everyone who wants to master Deep Learning skills.
- This course is for students who have already good knowledge of machine learning and have good programming skills.
- Any people who want to create added value to their business by using powerful Deep Learning tools
- Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets
- Software engineers who are curious about data science and about the Deep Learning buzz and want to get a better understanding of it
AWS SageMaker, AI and Machine Learning Specialty Exam
Free Coupon Discount - Complete Guide to AWS Certified Machine Learning (MLS-C01) - Specialty and Practice Test | Created by Chandra Lingam
Students also bought
- Machine Learning A-Z™: Hands-On Python & R In Data Science
- Python for Data Science and Machine Learning Bootcamp
- Machine Learning, Data Science and Deep Learning with Python
- Machine Learning with Javascript
- A Beginner's Guide To Machine Learning with Unity
Preview this Udemy Course GET COUPON CODE
Description
Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep
*** UPDATE APR-2020 Bring Your Own Algorithm - We take a behind the scene look at the SageMaker Training and Hosting Infrastructure for your own algorithms. With Labs ***
*** UPDATE FEB-2020 Subtitles and Closed Caption Available - I spent several hours cleaning and editing manually for an accurate subtitle ***
*** UPDATE JAN-2020 Timed Practice Test and additional lectures for Exam Preparation added
For Practice Test, look for the section: 2020 Practice Exam - AWS Certified Machine Learning Specialty
For exam overview, gap analysis and preparation strategy, look for 2020 - Overview - AWS Machine Learning Specialty Exam
***
*** UPDATE DEC-2019 Third update for this month!!! AWS Certified Machine Learning Specialty Exam Overview and Preparation Strategies lectures added to the course! Timed Practice Exam is coming soon!
Also added, two new lectures that gives an overview of all SageMaker Built-in Algorithms, Frameworks and Bring-Your-Own Algorithm Supports
Look for lectures starting with 2020
***
*** UPDATE DEC-2019. In the Neural Network and Deep Learning section, we will look at the core concepts behind neural networks, why deep learning is popular these days, different network architectures and hands-on labs to build models using Keras, TensorFlow, Apache MxNet: 2020 Deep Learning and Neural Networks
***
*** UPDATE DEC-2019. New reference architecture section with hands-on lab that demonstrates how to build a data lake solution using AWS Services and the best practices: 2020 AWS S3 Data Lake Architecture. This topic covers essential services and how they work together for a cohesive solution. Covers critical topics like S3, Athena, Glue, Kinesis, Security, Optimization, Monitoring and more.
***
*** UPDATE NOV-2019. AWS Artificial Intelligence material is now live!
Within a few minutes, you will learn about algorithms for sophisticated facial recognition systems, sentiment analysis, conversational interfaces with speech and text and much more.
***
*** UPDATE OCT-2019. New XGBoost Lectures, Labs, do-it-yourself exercises, quizzes, Autoscaling, high availability, Monitoring, security, and lots of good stuff
*** UPDATE MAY-2019. 1. Model endpoint integration with hands-on-labs for (Direct Client, Microservice, API Gateway). 2. Hyperparameter Tuning - Learn how to automatically tune hyperparameters ***
*** UPDATE MARCH-12-2019. I came to know that new accounts are not able to use AWSML Service. AWS is asking new users to use SageMaker Service.
I have restructured the course to start with SageMaker Lectures First. Machine Learning Service Lectures are still available in the later parts of the course. Newly updated sections start with 2019 prefix.
All source code for SageMaker Course is now available on Github
The new house keeping lectures cover all the steps for setting up code from GitHub.
***
*** SageMaker Lectures - DeepAR - Time Series Forecasting, XGBoost - Gradient Boosted Tree algorithm in-depth with hands-on. XGBoost has won several competitions and is a very popular Regression and Classification Algorithm, Factorization Machine based Recommender Systems and PCA for dimensionality reduction ***
Benefits
There are several courses on Machine Learning and AI. What is unique about this course?
Here are the top reasons:
1. Cloud-based machine learning keeps you focused on the current best practices.
2. In this course, you will learn the most useful algorithms. Don’t waste your time sifting through mountains of techniques that are in the wild
4. Cloud-based service is straightforward to integrate with your application and has support for a wide variety of programming languages.
5. Whether you have small data or big data, the elastic nature of the AWS cloud allows you to handle them all.
6. There is also No upfront cost or commitment – Pay only for what you need and use
Hands-on Labs
In this course, you will learn with hands-on labs and work on exciting and challenging problems
What exactly will you learn in this course?
Here are the things that you will learn in this course:
AWS SageMaker
* You will learn how to deploy a Notebook instance on the AWS Cloud.
* You will gain insight into algorithms provided by SageMaker service
* Learn how to train, optimize and deploy your models
AI Services
In the AI Services section of this course,
* You will learn about a set of pre-trained services that you can directly integrate with your application.
* Within a few minutes, you can build image and video analysis applications – like face recognition
* You can develop solutions for natural language processing, like finding sentiment, text translation, and conversational chatbots.
Integration
* Learning algorithms is one part of the story - You need to know how to integrate the trained models in your application.
* You will learn how to host your models, scale on-demand, handle failures
* Provide a clean interface for the applications using Lambda and API Gateway
Data Lake
* Data management is one of the most complex and time-consuming activities when working on machine learning projects.
* With AWS, you have a variety of powerful tools for ingesting, cataloging, transforming, securing, visualization of your data assets.
* We will build a data lake solution in this course.
Machine Learning Certification
* If you are planning to get AWS Machine Learning Specialty Certification, you will find all the resources that you need to pass the exam in this course.
* Timed Practice Exam and Quizzes
Source Code
* The source code for this course available on Git and that ensures you always get the latest code
Ideal Student
* The ideal student for this course is willing to learn, participate in the course Q&A forum when you need help, and you need to be comfortable coding in Python.
Author
My name is Chandra Lingam, and I am the instructor for this course.
I have over 50,000 thousand students
I spend a considerable amount of time keeping myself up-to-date and teach cloud technologies from the basics.
I have the following AWS Certifications: Solutions Architect, Developer, SysOps, Solutions Architect Professional, Machine Learning Specialty.
I am looking forward to meeting you.
Thank you!
Who this course is for:
This course is designed for anyone who is interested in AWS cloud based machine learning and data science
AWS Certified Machine Learning - Specialty Preparation
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
TensorFlow 2.0 Practical Advanced
Thursday, April 28, 2022
Free Coupon Discount - TensorFlow 2.0 Practical Advanced, Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 5 advanced practical projects | Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard
Students also bought
- Recommender Systems and Deep Learning in Python
- Machine Learning and AI: Support Vector Machines in Python
- Natural Language Processing with Deep Learning in Python
- Artificial Intelligence: Reinforcement Learning in Python
- Data Science: Deep Learning in Python
Preview this Udemy Course GET COUPON CODE
Description
Google has recently released TensorFlow 2.0 which is Google’s most powerful open source platform to build and deploy AI models in practice. Tensorflow 2.0 release is a huge win for AI developers and enthusiast since it enabled the development of super advanced AI techniques in a much easier and faster way.
The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Advanced Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab. This course will cover advanced, state-of-the–art AI models implementation in TensorFlow 2.0 such as DeepDream, AutoEncoders, Generative Adversarial Networks (GANs), Transfer Learning using TensorFlow Hub, Long Short Term Memory (LSTM) Recurrent Neural Networks and many more. The applications of these advanced AI models are endless including new realistic human photographs generation, text translation, image de-noising, image compression, text-to-image translation, image segmentation, and image captioning.
The global AI and machine learning technology sectors are expected to grow from $1.4B to $8.8B by 2022 and it is predicted that AI tech sector will create around 2.3 million jobs by 2020. The technology is progressing at a massive scale and being adopted in almost every sector. The course provides students with practical hands-on experience in training Advanced Artificial Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:
Develop, train, and test State-of-the art DeepDream algorithm to create AI-based art masterpieces!
Implement revolutionary Generative Adversarial Networks known as GANs to generate brand new images.
Develop Long Short Term Memory (LSTM) networks to generate new Shakespeare-style text!
Deploy AI models in practice using TensorFlow 2.0 Serving.
Apply Auto-Encoders to perform image compression and de-noising.
Apply transfer learning to transfer knowledge from pre-trained networks to classify new images using TensorFlow 2.0 Hub.
The course is targeted towards students wanting to gain a fundamental understanding of how to build, train, test and deploy advanced models in Tensorflow 2.0. Basic knowledge of programming and Artificial Neural Networks is recommended. Students who enroll in this course will master Advanced AI and Deep Learning techniques 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
AI Developers
AI Researchers
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
100+ Exercises - Advanced Python Programming - 2022
100+ Exercises - Advanced Python Programming - 2022 - Improve your Python programming skills and solve over 100 advanced Python problems!
Preview this Course
Machine Learning with Imbalanced Data
Tuesday, April 26, 2022
Machine Learning with Imbalanced Data - Learn multiple techniques to tackle data imbalance and improve the performance of your machine learning models.
- Hot & New
- Created by Soledad Galli
- English [Auto]
What you'll learn
- Under-sampling methods at random
- Under-sampling methods which focus on observations that are harder to classify
- Under-sampling methods that ignore potentially noisy observations
- Over-sampling methods to increase the number of minority observations
- Ways of creating syntethic data to increase the examples of the minority class
- SMOTE and its variants
- Use ensemble methods with sampling techniques to improve model performance
- The most suitable evaluation metrics to use with imbalanced datasets
Description
Welcome to Machine Learning with Imbalanced Datasets. In this course, you will learn multiple techniques which you can use with imbalanced datasets to improve the performance of your machine learning models.
If you are working with imbalanced datasets right now and want to improve the performance of your models, or you simply want to learn more about how to tackle data imbalance, this course will show you how.
We'll take you step-by-step through engaging video tutorials and teach you everything you need to know about working with imbalanced datasets. Throughout this comprehensive course, we cover almost every available methodology to work with imbalanced datasets, discussing their logic, their implementation in Python, their advantages and shortcomings, and the considerations to have when using the technique. Specifically, you will learn:
Under-sampling methods at random or focused on highlighting certain sample populations
Over-sampling methods at random and those which create new examples based of existing observations
Ensemble methods that leverage the power of multiple weak learners in conjunction with sampling techniques to boost model performance
Cost sensitive methods which penalize wrong decisions more severely for minority classes
The appropriate metrics to evaluate model performance on imbalanced datasets
By the end of the course, you will be able to decide which technique is suitable for your dataset, and / or apply and compare the improvement in performance returned by the different methods on multiple datasets.
This comprehensive machine learning course includes over 50 lectures spanning about 8 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.
In addition, the code is updated regularly to keep up with new trends and new Python library releases.
So what are you waiting for? Enroll today, learn how to work with imbalanced datasets and build better machine learning models.
Who this course is for:
Data Scientists and Machine Learning engineers working with imbalanced datasets
Practical Deep Learning with Tensorflow 2.x and Keras
Practical Deep Learning with Tensorflow 2.x and Keras
Learn to apply Tensorflow to YOUR problems. Follow a complete pipeline including pre-processing and training for ML.
Preview this Course
What you'll learn
- Be able to run deep learning models with Keras on Tensorflow 2 backend
- Run Deep Neural Networks on a real-world scientific protein dataset
- Understand how to feed own data to deep learning models (i.e. handling the notorious shape mismatch issue)
- Understand Deep Learning, CNN, dropout, functional API with minimal of math
- Understand and use Keras' functional API to create models with multiple inputs and outputs
- Learn how to do Transfer Learning practically
- Stunning SUPPORT. I answer questions on the same day.
Requirements
- You should be able to use Python (if, while, lists. Everything else will be covered in the course)
- NO prior knowledge of machine learning is assumed
Description
**UPDATED: Now using Tensorflow 2. Please post in Q&A if you have any trouble. I'm here to help**
**UPDATED 11-2021: Added a section on Practical Transfer Learning**
TensorFlow is by far, the most popular library for deep learning. Backed by Google, it is a solid investment of your time and efforts if you want to succeed in the area of machine learning and AI. The issue most people face is that getting started with Tensorflow guides usually delve too deeply into unnecessary mathematics.
That is where this course comes in. While some theory is important, a lot of it is just not needed when you're just getting started!
This course is for you if you are new to Machine Learning but want to learn it without all the complicated math. This course is also for you if you have had a machine learning course but could never figure out how to use it to solve your own problems.
In this course, we will start from very scratch. This is a very applied course, so we will immediately start coding even without installation! You will see a brief bit of absolutely essential theory and then we will get into the environment setup and explain almost all concepts through code. You will be using Keras and Tensorflow 2.x -- one of the easiest and most powerful machine learning tools out there.
You will start with a basic model of how machines learn and then move on to higher models such as:
Convolutional Neural Networks
Residual Connections
Inception Module
Functional API of Keras / Tensorflow 2.x
Transfer Learning
In this course, we explain concepts using not only toy datasets but also a real-world dataset from the bioinformatics domain. While you may not be interested in this particular domain, you would still learn a lot of important concepts that are involved in taking data from the real world and feeding it to ML models. This is the aspect of ML that is missing from almost all courses available on the internet today! Doing this would mean that you would be able to solve problems of your own industry after finishing this course.
All with only a few lines of code. All the examples used in the course come with a starter code that will get you started and remove the grunt effort. The course also includes finished codes for the examples run in the videos so that you can see the end product should you ever get stuck. Do checkout the preview lectures on this page to get a better feel of the teaching style used in this course and how it can help you learn quickly.
I provide unmatched support. All questions are answered within 24 hours. Try me and see ... =]
Who this course is for:
- Anyone who wants to learn machine learning (this course is a soft introduction)
- Anyone who knows machine learning and wants to learn deep learning (this course focuses on deep learning)
- Anyone who knows deep learning but needs help applying their knowledge in practice (this is a very applied course)
- Anyone who is comfortable with deep learning models but has trouble processing examples beyond the toy examples covered in typical courses (this course has a real-world case study and not just toy examples)
- Anyone who is a researcher or educator working in machine learning and wants to move from theory to practice
100+ Exercises - Python Programming - Data Science - NumPy
100+ Exercises - Python Programming - Data Science - NumPy - Improve your Python programming and data science skills and solve over 100 exercises in NumPy!
- Created by Paweł Krakowiak
- English
What you'll learn
- solve over 100 exercises in NumPy
- deal with real programming problems in data science
- work with documentation and Stack Overflow
- guaranteed instructor support
Requirements
- completed course '200+ Exercises - Programming in Python - from A to Z'
- completed course '210+ Exercises - Python Standard Libraries - from A to Z'
- completed course '150+ Exercises - Object Oriented Programming in Python - OOP'
- basic knowledge of NumPy library
Description
------------------------------------------------------------------------------
RECOMMENDED LEARNING PATH
------------------------------------------------------------------------------
PYTHON DEVELOPER:
200+ Exercises - Programming in Python - from A to Z
210+ Exercises - Python Standard Libraries - from A to Z
150+ Exercises - Object Oriented Programming in Python - OOP
150+ Exercises - Data Structures in Python - Hands-On
100+ Exercises - Advanced Python Programming
100+ Exercises - Unit tests in Python - unittest framework
100+ Exercises - Python Programming - Data Science - NumPy
100+ Exercises - Python Programming - Data Science - Pandas
100+ Exercises - Python - Data Science - scikit-learn
250+ Exercises - Data Science Bootcamp in Python
SQL DEVELOPER:
SQL Bootcamp - Hands-On Exercises - SQLite - Part I
SQL Bootcamp - Hands-On Exercises - SQLite - Part II
------------------------------------------------------------------------------
COURSE DESCRIPTION
------------------------------------------------------------------------------
100+ Exercises - Python Programming - Data Science - NumPy
Welcome to the course 100+ Exercises - Python Programming - Data Science - NumPy, where you can test your Python programming skills in data science, specifically in NumPy.
Some topics you will find in the exercises:
working with numpy arrays
generating numpy arrays
generating numpy arrays with random values
iterating through arrays
dealing with missing values
working with matrices
reading/writing files
joining arrays
reshaping arrays
computing basic array statistics
sorting arrays
filtering arrays
image as an array
linear algebra
matrix multiplication
determinant of the matrix
eigenvalues and eignevectors
inverse matrix
shuffling arrays
working with polynomials
working with dates
working with strings in array
solving systems of equations
The course is designed for people who have basic knowledge in Python and NumPy package. It consists of 100 exercises with solutions.
This is a great test for people who are learning the Python language and data science and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course.
If you're wondering if it's worth taking a step towards Python, don't hesitate any longer and take the challenge today.
Who this course is for:
- everyone who wants to learn by doing
- everyone who wants to improve their Python programming skills
- everyone who wants to improve their data science skills
- everyone who wants to prepare for an interview
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
Artificial Intelligence Expert Course + Live Class
Monday, April 25, 2022
Artificial Intelligence Expert Course + Live Class
A breathtaking course in 2022 that teaches new-age Artificial Intelligence (AI) Technologies and Tools
Preview this Course
What you'll learn
- Secretive Machine Learning Artificial Intelligence tools
- Secretive Research based Artificial Intelligence tools
- Secretive Digital Marketing Artificial Intelligence tools
- Secretive Business related Artificial Intelligence tools
- Secretive Notetaking Artificial Intelligence tools
- Secretive Video Creation Artificial Intelligence tools
- Secretive Analytics based Artificial Intelligence tools
- Secretive Graphic Design Artificial Intelligence tools
- Secretive Deep Learning Artificial Intelligence tools
- Secretive Voice Cloning Artificial Intelligence tools
- Secretive Mind Cloning Artificial Intelligence tools
- Secretive Virtual Reality or VR Artificial Intelligence tools
- Secretive Customer Support Artificial Intelligence tools
- Secretive Misc. Artificial Intelligence tools of 2022
- Secretive and Futuristic Programming Artificial Intelligence tools
- Secretive Entertainment or Game type Artificial Intelligence tools
- Secretive Augmented Reality or AR Artificial Intelligence tools
- Artificial General Intelligence (AGI) - Incredible Research Findings
- Learn how to prepare your business for the Artificial Intelligence Revolution
- Artificial Intelligence (AI) Trends for Social Media
- Learn about the concept of whether Robots can teach or not.
- Learn how to find your ideal career in the field of Artificial Intelligence (AI).
- Concepts of Computer Vision: Fundamentals
Requirements
- Several Artificial Intelligence coding tools will be taught in the course. Most of the tools will be free and also paid alternatives will be covered. You can choose the best tools according to your requirement with the correct features during usage.
Description
Welcome to experience a mind-blowing "Artificial Intelligence Expert Course" in 2022.
Artificial Intelligence Expert Course: Platinum Edition - The course has now launched.
Artificial Intelligence (AI) seems to be a unique technology for making a machine, a robot fully autonomous. AI is an analysis of how the machine is thinking, studying, determining, and functioning when it is trying to solve problems.
These kinds of problems are present in all fields, the most emerging ones, and even beyond. The aim of Artificial Intelligence is to enhance machine functions relating to human knowledge, such as reasoning, learning, and problems along with the ability to manipulate things. For example, virtual assistants or chatbots offer expert advice. Smart robots or robot advisors will provide instant research or findings in the fields of finance, insurance, law, the media, and journalism, and medical diagnosis and support will be provided by AI software on the health front. Other advantages include increasing productivity dramatically in research and development programs by reducing time to the market, enhancing the transport and supply chain networks and improving governance by improved decision-making processes etc.
Artificial Intelligence technology is aiding the following prominent fields for 2022 and many others:
Website Creation - Artificial Intelligence tools can help to create websites and landing pages in merely minutes.
App Creation - Artificial Intelligence tools can help to automate the whole concept of creating apps.
Natural Language Processing (NLP) − This process is simulating the actual interaction with the computer that understands natural language spoken by humans.
Expert Systems − Learners and users will be provided with guided information and advice on the computer or software.
Vision Systems − Artificial Intelligence (AI) systems in 2022 can understand, explain and describe the computer's visual input to the ultimate core.
Spoken Word or Speech Recognition − Many Artificial Intelligence (AI) based speech recognition systems are capable of listening, voicing and recognizing the user input - i.e. when a person speaks with them. Alexa, Siri and Google's assistant, are examples of this function.
Machine Learning - Artificial Intelligence (AI) can help users to create and experiment with machine learning models and build data science applications in a flash.
Video Creation - Artificial Intelligence (AI) can help to automate video production and cut high costs of hiring resources and teams as everything is taken care of in the cloud.
Python and Coding tools - Artificial Intelligence (AI) can help to automate writing lakhs of lines of code in python and even Java or PHP in minutes without the need for a human coder or even someone who will debug the code. Everything is automated in the concept of AI-powered programming.
This mind-blowing exhaustive course focusing on "Artificial Intelligence Expert Course: Platinum Edition" taught by Digital Marketing Legend Srinidhi Ranganathan and Civil MasterMind Saranya Srinidhi will change your perspective on the concept of Artificial Intelligence forever to rely on AI tools of the future for your needs. We will cover 100's of Artificial Intelligence (AI) tools that are highly popular in the following fields that include: Machine Learning, Deep Learning, Digital Marketing, Research, Analytics, Voice-cloning, Mind-Cloning, Video Creation, Virtual Reality (VR) based Artificial Intelligence tools etc. As you learn these AI tools you will understand that Artificial Intelligence has so much evolved in the last 2 to 5 years. You will also see the growth of the emergence of Artificial Intelligence (AI) tools across industries that have exploded and its impact on business and society that is emerging at a quick pace. In this incredible journey, you will also encounter businesses that are pushing the limits of automation, search and social media. As you slowly realize that with the brain of a computer, AI would potentially automate control in such sectors as self-employed vehicles and non-manned drones, you will be left spellbound.
"Artificial Intelligence (AI) will not only reduce costs by automating processes but also skyrocket revenues by helping startups and corporates introduce new product and service categories at the fastest speed ever imagined with limited resources."
But, will the jobs die as automation takes over? Will AI will surpass human intelligence in 2022, itself? The answer to these questions is a "No".
The development and eventual growth in AI apps helping in productivity will provide workers with a range of opportunities to improve their skills and concentrate on creative aspects. Stating additional forecasts, disruptive market trends are highly likely to occur in the AI Expansion period (i.e. 2025-2030), the opportunity for employment would require a high degree of personalization, innovation or ability tasks which will still require a person to perform them (even though the AI robotic tools have actually speeded up the process). These occupations or technologies are difficult to imagine at this stage, yet these occupations will rapidly increase as new specialization is required when the demand would kick in. The time is coming soon for a global Artificial Intelligence (AI) revolution to strongly emerge.
Okay. Let's start learning and go on an adventure in Artificial Intelligence. Enrol Now and I will see you inside. Let's rock this world with learning secretive tools. Don't waste any more time.
Special Note: Several Artificial Intelligence (AI) coding tools will be taught in the course. Most of the tools will be free and also paid alternatives will be covered. You can choose the best tools according to your requirements with the correct features during usage.
IMPORTANT:
Regarding the Live Group Q&A Session:
Students who have completed every lesson in the course will also get access to a free live online group Q&A session of 1 hour with Digital Marketing Legend "Srinidhi Ranganathan". You can clarify all your doubts there. Srinidhi will also showcase your career paths in Futuristic AI, and provide you with a full walkthrough of what you learned along with tips on how to improvise or implement the stuff taught.
Who this course is for:
- Beginner students who want to learn new-age Artificial Intelligence tools to take their career ahead to new heights of success
- Digital Marketing enthusiasts who wish to learn Artificial Intelligence technology tools
- Startup founders who wish to learn Artificial Intelligence tools in 2022
- Passionate students and learners who want to put a ding in the universe after learning Artificial Intelligence tools and implement those tools in their business and job
- Artificial Intelligence trainers who want to discover new technologies that are world changing in every aspect
- Research specialists, Machine Learning enthusiasts and Data Science Students who want to learn incredible tools and technologies in the field of Artificial Intelligence
- Virtual Reality or gaming enthusiasts who want to see something new to experiment on Artificial Intelligence technology
- Anyone who is interested to learn Artificial Intelligence tools that will help them all the time in their business, career or college
Computer Vision Masterclass
Sunday, April 24, 2022
Free Udemy Coupon - Computer Vision Masterclass, Learn in practice everything you need to know about Computer Vision! Build projects step by step using Python!
- Hot & New
- Created by Jones Granatyr, Ligency Team, Gabriel Alves
- English [Auto]
What you'll learn
- Understand the basic intuition about Cascade and HOG classifiers to detect faces
- Implement face detection using OpenCV and Dlib library
- Learn how to detect other objects using OpenCV, such as cars, clocks, eyes, and full body of people
- Compare the results of three face detectors: Haarcascade, HOG (Histogram of Oriented Gradients) and CNN (Convolutional Neural Networks)
- Detect faces using images and the webcam
- Understand the basic intuition about LBPH algorithm to recognize faces
- Implement face recognition using OpenCV and Dlib library
- Recognize faces using images and the webcam
- Understand the basic intuition about KCF and CSRT algorithms to perform object tracking
- Learn how to track objects in videos using OpenCV library
- Learn everything you need to know about the theory behind neural networks, such as: perceptron, activation functions, weight update, backpropagation, gradient descent and a lot more
- Implement dense neural networks to classify images
- Learn how to extract pixels and features from images in order to build neural networks
- Learn the theory behind convolutional neural networks and implement them using Python and TensorFlow
- Implement transfer learning and fine tuning to get incredible results when classifying images
- Use convolutional neural networks to classify the following emotions in images and videos: happy, anger, disgust, fear, surprise and neutral
- Compress images using linear and convolutional autoencoders
- Detect objects in images in videos using YOLO, one of the most powerful algorithms today
- Recognize gestures and actions in videos using OpenCV
- Learn how to create hallucinogenic images with Deep Dream
- Learn how to revive famous artists with style transfer
- Create images that don't exist in the real world with GANs (Generative Adversarial Networks)
- Implement image segmentation do extract useful information from images and videos
Description
Computer Vision is a subarea of Artificial Intelligence focused on creating systems that can process, analyze and identify visual data in a similar way to the human eye. There are many commercial applications in various departments, such as: security, marketing, decision making and production. Smartphones use Computer Vision to unlock devices using face recognition, self-driving cars use it to detect pedestrians and keep a safe distance from other cars, as well as security cameras use it to identify whether there are people in the environment for the alarm to be triggered.
In this course you will learn everything you need to know in order to get in this world. You will learn the step-by-step implementation of the 14 (fourteen) main computer vision techniques. If you have never heard about computer vision, at the end of this course you will have a practical overview of all areas. Below you can see some of the content you will implement:
Detect faces in images and videos using OpenCV and Dlib libraries
Learn how to train the LBPH algorithm to recognize faces, also using OpenCV and Dlib libraries
Track objects in videos using KCF and CSRT algorithms
Learn the whole theory behind artificial neural networks and implement them to classify images
Implement convolutional neural networks to classify images
Use transfer learning and fine tuning to improve the results of convolutional neural networks
Detect emotions in images and videos using neural networks
Compress images using autoencoders and TensorFlow
Detect objects using YOLO, one of the most powerful techniques for this task
Recognize gestures and actions in videos using OpenCV
Create hallucinogenic images using the Deep Dream technique
Combine style of images using style transfer
Create images that don't exist in the real world with GANs (Generative Adversarial Networks)
Extract useful information from images using image segmentation
You are going to learn the basic intuition about the algorithms and implement some project step by step using Python language and Google Colab
Who this course is for:
Beginners who are starting to learn Computer Vision
Undergraduate students who are studying subjects related to Artificial Intelligence
People who want to solve their own problems using Computer Vision
Students who want to work in companies developing Computer Vision projects
People who want to know all areas inside Computer Vision, as well as know the problems that these techniques are able to solve
Anyone interested in Artificial Intelligence or Computer Vision
Data scientists who want to grow their portfolio
Professionals who want to understand how to apply Computer Vision to real projects
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
Machine Learning, Data Science and Deep Learning with Python
Saturday, April 23, 2022
Online Courses Udemy - Machine Learning, Data Science and Deep Learning with Python, Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
- BESTSELLER
- 4.5 (16,595 ratings)
- Created by Sundog Education by Frank Kane, Frank Kane
- English, Italian [Auto-generated], 1 more
PREVIEW THIS COURSE - GET COUPON CODE
What you'll learn
- Build artificial neural networks with Tensorflow and Keras
- Classify images, data, and sentiments using deep learning
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Data Visualization with MatPlotLib and Seaborn
- Implement machine learning at massive scale with Apache Spark's MLLib
- Understand reinforcement learning - and how to build a Pac-Man bot
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
- Use train/test and K-Fold cross validation to choose and tune your models
- Build a movie recommender system using item-based and user-based collaborative filtering
- Clean your input data to remove outliers
- Design and evaluate A/B tests using T-Tests and P-Values
Requirements
- You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software.
- Some prior coding or scripting experience is required.
- At least high school level math skills will be required.
Description
New! Updated for Summer 2019 for the latest software versions, and over 5 hours of new & updated content!
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too!
If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 13 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.
Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!
The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We'll cover the machine learning, AI, and data mining techniques real employers are looking for, including:
Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras
Data Visualization in Python with MatPlotLib and Seaborn
Transfer Learning
Sentiment analysis
Image recognition and classification
Regression analysis
K-Means Clustering
Principal Component Analysis
Train/Test and cross validation
Bayesian Methods
Decision Trees and Random Forests
Multiple Regression
Multi-Level Models
Support Vector Machines
Reinforcement Learning
Collaborative Filtering
K-Nearest Neighbor
Bias/Variance Tradeoff
Ensemble Learning
Term Frequency / Inverse Document Frequency
Experimental Design and A/B Tests
...and much more! There's also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to "big data" analyzed on a computing cluster. And you'll also get access to this course's Facebook Group, where you can stay in touch with your classmates.
If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs.
If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!
"I started doing your course in 2015... Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing." - Kanad Basu, PhD
Introduction to AI, Machine Learning and Python basics
Introduction to AI, Machine Learning and Python basics - Learn to understand Artificial Intelligence and Machine Learning algorithms, and learn the basics of Python Programming
What you'll learn
- Learn to understand between Machine Learning, Deep learning and Artificial Intelligence
- Learn where AI and Machine learning algorithms are used today
- Learn basics of Python programming
- Build simplest Machine Learning models in Excel
- Predict and build Machine Learning models in Python
- Create your own Neural Networks to Classify Images and Analyze Texts
Description
Artificial Intelligence has already become an indispensable part of our everyday life, whether when we browse the Internet, shop online, watch videos and images on social networks, and even when we drive a car or use our smartphones. AI is widely used in medicine, sales forecasting, space industry and construction.
Since we are surrounded by AI technologies everywhere, we need to understand how these technologies work. And for such understanding at a basic level, it is not necessary to have a technical or IT education.
***
In this course, you will learn about the fundamental concepts of Artificial Intelligence and Machine learning. You will get acquainted with their main types, algorithms and models that are used to solve completely different problems. We will even create models together to solve specific practical examples in Excel - for those who do not want to program anything. And for those who want to get acquainted with Python , a programming language that solves more than 53% of all machine learning tasks today, in this course you will find lectures to familiarize yourself with the basics of programming in this language.
**
This course may become a kind of springboard for your career development in the field of AI and Machine learning. Having mastered this short course, you will be able to choose the particular area in which you would like to develop and work further. It is worth mentioning that today, AI and Machine Learning specialists are among the highest paid and sought after on the market (according to various estimates, there are about 300,000 AI experts on the global market today, while the demand for them is several million).
**
So why not reinforce your resume with a certificate from Udemy, the largest international educational platform , that you have completed this course on Artificial Intelligence and Machine Learning, and the basics of Python programming .
***
After completing this course, you will be able to communicate freely on topics related to Artificial Intelligence, Machine and Deep Learning, and Neural Networks. You will be able to analyze and visualize data, use algorithms to solve problems from different areas.
***
This course will be regularly supplemented with new lectures and after enrolling in it you will have full access to all materials without any restrictions. Spend a few hours studying this course to get new or improve existing skills and broaden your horizons using the acquired knowledge.
See you inside the course!
***
Who this course is for:
- Beginner learners of AI and Machine learning
- Beginner Python enthusiasts interested in Machine learning
Complete Machine Learning & Data Science Bootcamp 2022
Thursday, April 21, 2022
Free Coupon Discount - Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more! | Created by Andrei Neagoie, Daniel Bourke
Students also bought
- Machine Learning A-Z™: Hands-On Python & R In Data Science
- Data Science A-Z™: Real-Life Data Science Exercises Included
- Machine Learning, Data Science and Deep Learning with Python
- Statistics for Data Science and Business Analysis
- Data Science 2020 : Complete Data Science & Machine Learning
Preview this Udemy Course GET COUPON CODE
Description
This is a brand new Machine Learning and Data Science course just launched January 2020 and updated this month with the latest trends and skills! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 270,000+ engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. 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 and 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.
100% Off Udemy Coupon . Free Udemy Courses . Online Classes
250+ Exercises - Data Science Bootcamp in Python - 2022
Monday, April 18, 2022
250+ Exercises - Data Science Bootcamp in Python - 2022
Improve your Python programming skills and solve over 250 data science exercises!
Preview this Course
What you'll learn
- solve over 250 exercises in data science in Python
- deal with real programming problems
- deal with real problems in data science
- work with libraries numpy, pandas, seaborn, plotly, scikit-learn, opencv, tensorflow
- work with documentation
- guaranteed instructor support
Requirements
completed course '200+ Exercises - Programming in Python - from A to Z'
completed course '210+ Exercises - Python Standard Libraries - from A to Z'
completed course '150+ Exercises - Object Oriented Programming in Python - OOP'
completed course '100+ Exercises - Python Programming - Data Science - NumPy'
completed course '100+ Exercises - Python Programming - Data Science - Pandas'
completed course '100+ Exercises - Python - Data Science - scikit-learn'
Description
------------------------------------------------------------------------------
RECOMMENDED LEARNING PATH
------------------------------------------------------------------------------
PYTHON DEVELOPER:
200+ Exercises - Programming in Python - from A to Z
210+ Exercises - Python Standard Libraries - from A to Z
150+ Exercises - Object Oriented Programming in Python - OOP
150+ Exercises - Data Structures in Python - Hands-On
100+ Exercises - Advanced Python Programming
100+ Exercises - Unit tests in Python - unittest framework
100+ Exercises - Python Programming - Data Science - NumPy
100+ Exercises - Python Programming - Data Science - Pandas
100+ Exercises - Python - Data Science - scikit-learn
250+ Exercises - Data Science Bootcamp in Python
110+ Exercises - Python + SQL (sqlite3) - SQLite Databases
250+ Questions - Job Interview - Python Developer
SQL DEVELOPER:
SQL Bootcamp - Hands-On Exercises - SQLite - Part I
SQL Bootcamp - Hands-On Exercises - SQLite - Part II
110+ Exercises - Python + SQL (sqlite3) - SQLite Databases
200+ Questions - Job Interview - SQL Developer
JOB INTERVIEW SERIES:
250+ Questions - Job Interview - Python Developer
200+ Questions - Job Interview - SQL Developer
200+ Questions - Job Interview - Software Developer - Git
200+ Questions - Job Interview - Data Scientist
------------------------------------------------------------------------------
COURSE DESCRIPTION
------------------------------------------------------------------------------
The course consists of 250 exercises (exercises + solutions) in data science with Python.
Packages that you will use in the exercises:
numpy
pandas
seaborn
plotly
scikit-learn
opencv
tensorflow
Some topics you will find in the exercises:
working with numpy arrays
working with matrices
random numbers
normal distribution
image as a numpy array
working with polynomials
working with dates
dealing with missing values
working with pandas Series and DataFrames
reading/writing files
working with stock market data
creating visualizations using seaborn and plotly
preparing data to the machine learning models
feature extraction
splitting data into train and test sets
solving systems of equations
building regression and classification models
working with neural networks - TensorFlow and Keras
working with computer vision - OpenCV
This is a great test for people who are learning the Python language and are looking for new challenges. The course is designed for people who already have basic knowledge in Python and knowledge about data science libraries. Exercises are also a good test before the interview. Many popular topics were covered in this course.
Don't hesitate and take the challenge today!
Who this course is for:
- everyone who wants to learn by doing
- everyone who wants to improve their programming skills in Python
- people who are preparing for interviews
- people interested in data science
- data scientists
- data analytics
- machine learning engineers
Theoretical Machine Learning From Scratch - Linear Models
Saturday, April 16, 2022
Theoretical Machine Learning From Scratch - Linear Models
Learn the theory and math behind Linear and Logistic regression and also learn to code them from scratch
Preview this Course
What you'll learn
- Understand the math behind linear models particularly linear and logistic regression
- Uncover the black box understand the inner workings of linear and logistic regression
- Understand gradient descent in a great detail and apply it to solving problems
- Learn to apply the linear models to machine learning problems and use cases
- Code everything from scratch without using any ready made machine learning library
Requirements
- Basic to intermediate programming skills(program flow, conditional statements, looping, object oriented approach)
- Taking derivative and partial derivatives using calculus
- Some basic probability and statistics
- Basic linear algebra(matrix multiplication)
Description
This course will be your guide to learning how to use the power of theory, math and python to create linear regression and logistic regression, two of most popular and useful machine learning models from scratch.
This course is designed for folks with some programming experience or experienced developers looking to make the jump to data science and machine learning, I'll teach you how to dive deep into the math behind the linear models in an easy and understandable way. Once, you have understood the inner workings of the linear models and uncovered the black box, you are ready to code everything from the ground up without using any fancy ready made machine learning libraries and yes you will be taught that too! The course is beneficial for understanding the machine learning concepts deeply rather than just using some library to get results, it will guide you in the right direction for learning many other machine learning and deep learning algorithms, as this course covers all the basics required, you will be well on your way to becoming an expert Data Scientist!
Since this course goes deep into the math and has coding from scratch, a basic to intermediate knowledge of coding is a must, also good idea of derivatives(calculus), linear algebra(matrix multiplication) and basic probability is required to get the full out of this course.
Enroll today to go beyond!
Who this course is for:
- This course is meant for people who want to go beyond the basic understanding of machine learning paradigms and dive deeper into the math and theory
Subscribe to:
Posts (Atom)