Ensemble Machine Learning in Python: Random Forest, AdaBoost

 Ensemble Machine Learning in Python: Random Forest, AdaBoost - Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python

Preview this Course

GET COUPON CODE

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now "machine learning first", and companies like NVIDIA and Amazon have followed suit, and this is what's going to drive innovation in the coming years.

Machine learning is embedded into all sorts of different products, and it's used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

This course is all about ensemble methods.

We've already learned some classic machine learning models like k-nearest neighbor and decision tree. We've studied their limitations and drawbacks.

But what if we could combine these models to eliminate those limitations and produce a much more powerful classifier or regressor?

In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of.

In particular, we will study the Random Forest and AdaBoost algorithms in detail.

To motivate our discussion, we will learn about an important topic in statistical learning, the bias-variance trade-off. We will then study the bootstrap technique and bagging as methods for reducing both bias and variance simultaneously.

We'll do plenty of experiments and use these algorithms on real datasets so you can see first-hand how powerful they are.

Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

Calculus (derivatives)

Probability

Object-oriented programming

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations

Simple machine learning models like linear regression and decision trees

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree

No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch

Not afraid of university-level math - get important details about algorithms that other courses leave out

Who this course is for:

• Understand the types of models that win machine learning contests (Netflix prize, Kaggle)
• Students studying machine learning
• Professionals who want to apply data science and machine learning to their work
• Entrepreneurs who want to apply data science and machine learning to optimize their business
• Students in computer science who want to learn more about data science and machine learning
• Those who know some basic machine learning models but want to know how today's most powerful models (Random Forest, AdaBoost, and other ensemble methods) are built

Python A-Z™: Python For Data Science With Real Exercises!

 

Python A-Z™: Python For Data Science With Real Exercises! - Programming In Python For Data Analytics And Data Science. Learn Statistical Analysis, Data Mining And Visualization

Created by Kirill Eremenko, Ligency I Team, Ligency Team

Preview this Course

GET COUPON CODE

Learn Python Programming by doing!

There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different!

This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.

After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.

In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course!

I can't wait to see you in class,

What you'll learn

• Learn to program in Python at a good level
• Learn how to code in Jupiter Notebooks
• Learn the core principles of programming
• Learn how to create variables
• Learn about integer, float, logical, string and other types in Python
• Learn how to create a while() loop and a for() loop in Python
• Learn how to install packages in Python
• Understand the Law of Large Numbers

Deep Learning Prerequisites: Linear Regression in Python

 Deep Learning Prerequisites: Linear Regression in Python - Data science, machine learning, and artificial intelligence in Python for students and professionals

Bestseller | Created by Lazy Programmer Inc.

Preview this Course

GET COUPON CODE

This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python.

What you'll learn

• Derive and solve a linear regression model, and apply it appropriately to data science problems
• Program your own version of a linear regression model in Python

ChatGPT: Build Solutions and Apps with ChatGPT and OpenAI Course

 

ChatGPT: Build Solutions and Apps with ChatGPT and OpenAI Course

Integrate ChatGPT into your no-code apps and solutions | Create 6+ ChatGPT integrations | Build 4+ ChatGPT Applications

This course has one objective: start building applications and solutions with ChatGPT. We do that by first learning more about OpenAI and ChatGPT, and how to use its APIs in our platforms. We then create a serverless function that can take ChatGPT prompts and return responses – the first step to integrate your ChatGPT into your application. After that, we learn to integrate the ChatGPT API into many platforms, like Outlook, Power Apps, Power Automate, Teams, Bubbe, Airtable, and more. Finally, we take all we’ve learned, and create full applications and solution examples, like TravelPlan, a web application that helps users create itineraries through the power of ChatGPT.

Best Seller Course: ChatGPT chatbot for Salesforce Admin and Developers Chat GPT

What you’ll learn

• Create full business applications and solutions with the OpenAI API and ChatGPT AI
• The inner workings of OpenAI, chatGPT, its capabilities, advantages, disadvantage, and more
• Integrate ChatGPT into several business platforms, like Outlook, Teams, Excel, Power Automate, and more
• Integrate ChatGPT into several no-code development applications, like Power Apps, Bubble, Airtable, and mofre
• Create a ChatGPT app that replies to your emails in a professional and polite manner
• Create a ChatGPT app from scratch that automatically creates real cover letters based on job ads and resumes
• Create a ChatGPT app from scratch that plans itineraries based on location, trip length, and interests
• Create a ChatGPT chatbot with Teams
• Create a ChatGPT app that generates stock photos to be used in pitch presentations and other slide decks
• Integrate ChatGPT API into Azure Functions, enabling you to put ChatGPT anywhere

Recommended Course: ChatGPT: Complete ChatGPT Course For Work 2023 (Ethically)!

Preview this Course  -  GET COUPON CODE

The Data Science Course 2022: Complete Data Science Bootcamp

 

The Data Science Course 2022: Complete Data Science Bootcamp

Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning

Preview this Course  -  GET COUPON CODE

Description

The Problem

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace.     

However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.    

And how can you do that?  

Universities have been slow at creating specialized data science programs. (not to mention that the ones that exist are very expensive and time consuming)   

Most online courses focus on a specific topic and it is difficult to understand how the skill they teach fit in the complete picture  

The Solution   

Data science is a multidisciplinary field. It encompasses a wide range of topics.   

Understanding of the data science field and the type of analysis carried out  

Mathematics  

Statistics   

Python   

Applying advanced statistical techniques in Python   

Data Visualization  

Machine Learning  

Deep Learning  

Each of these topics builds on the previous ones. And you risk getting lost along the way if you don’t acquire these skills in the right order. For example, one would struggle in the application of Machine Learning techniques before understanding the underlying Mathematics. Or, it can be overwhelming to study regression analysis in Python before knowing what a regression is.   

So, in an effort to create the most effective, time-efficient, and structured data science training available online, we created The Data Science Course 2022.   

We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place.  

Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs (not to mention the amount of time you will save).   

The Skills

   1. Intro to Data and Data Science

Big data, business intelligence, business analytics, machine learning and artificial intelligence. We know these buzzwords belong to the field of data science but what do they all mean?     

Why learn it? As a candidate data scientist, you must understand the ins and outs of each of these areas and recognise the appropriate approach to solving a problem. This ‘Intro to data and data science’ will give you a comprehensive look at all these buzzwords and where they fit in the realm of data science.  

   2. Mathematics 

Learning the tools is the first step to doing data science. You must first see the big picture to then examine the parts in detail.   

We take a detailed look specifically at calculus and linear algebra as they are the subfields data science relies on.   

Why learn it?  

Calculus and linear algebra are essential for programming in data science. If you want to understand advanced machine learning algorithms, then you need these skills in your arsenal.

   3. Statistics 

You need to think like a scientist before you can become a scientist. Statistics trains your mind to frame problems as hypotheses and gives you techniques to test these hypotheses, just like a scientist.  

Why learn it?  

This course doesn’t just give you the tools you need but teaches you how to use them. Statistics trains you to think like a scientist.

   4. Python

Python is a relatively new programming language and, unlike R, it is a general-purpose programming language. You can do anything with it! Web applications, computer games and data science are among many of its capabilities. That’s why, in a short space of time, it has managed to disrupt many disciplines. Extremely powerful libraries have been developed to enable data manipulation, transformation, and visualisation. Where Python really shines however, is when it deals with machine and deep learning.

Why learn it?   

When it comes to developing, implementing, and deploying machine learning models through powerful frameworks such as scikit-learn, TensorFlow, etc, Python is a must have programming language.  

   5. Tableau

Data scientists don’t just need to deal with data and solve data driven problems. They also need to convince company executives of the right decisions to make. These executives may not be well versed in data science, so the data scientist must but be able to present and visualise the data’s story in a way they will understand. That’s where Tableau comes in – and we will help you become an expert story teller using the leading visualisation software in business intelligence and data science.

Why learn it?   

A data scientist relies on business intelligence tools like Tableau to communicate complex results to non-technical decision makers.  

   6. Advanced Statistics 

Regressions, clustering, and factor analysis are all disciplines that were invented before machine learning. However, now these statistical methods are all performed through machine learning to provide predictions with unparalleled accuracy. This section will look at these techniques in detail.  

Why learn it?  

Data science is all about predictive modelling and you can become an expert in these methods through this ‘advance statistics’ section.  

   7. Machine Learning 

The final part of the program and what every section has been leading up to is deep learning. Being able to employ machine and deep learning in their work is what often separates a data scientist from a data analyst. This section covers all common machine learning techniques and deep learning methods with TensorFlow.  

Why learn it?   

Machine learning is everywhere. Companies like Facebook, Google, and Amazon have been using machines that can learn on their own for years. Now is the time for you to control the machines.  

***What you get***

A $1250 data science training program   

Active Q&A support  

All the knowledge to get hired as a data scientist  

A community of data science learners  

A certificate of completion   

Access to future updates  

Solve real-life business cases that will get you the job   

You will become a data scientist from scratch   We are happy to offer an unconditional 30-day money back in full guarantee. No risk for you. The content of the course is excellent, and this is a no-brainer for us, as we are certain you will love it.

Why wait? Every day is a missed opportunity.

Click the “Buy Now” button and become a part of our data scientist program today.  

 

Who this course is for:

• You should take this course if you want to become a Data Scientist or if you want to learn about the field
• This course is for you if you want a great career
• The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills
• Show less
• Featu

What you'll learn

• The course provides the entire toolbox you need to become a data scientist
• Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
• Impress interviewers by showing an understanding of the data science field
• Learn how to pre-process data
• Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
• Start coding in Python and learn how to use it for statistical analysis
• Perform linear and logistic regressions in Python
• Carry out cluster and factor analysis
• Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
• Apply your skills to real-life business cases
• Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
• Unfold the power of deep neural networks
• Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance
• Warm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situations
• Show less

Statistics For Data Science and Machine Learning with Python

 

Statistics For Data Science and Machine Learning with Python

Practical Statistics with Python for Data Science & Machine Learning Statistical Modeling Using Sci-kit Learn and Scipy

• New
• Created by Taher Assaf

Preview this Course  -  GET COUPON CODE

This course is ideal for you if you want to gain knowledge in statistical methods required for Data Science and machine learning!

Learning Statistics is an essential part of becoming a professional data scientist. Most data science learners study python for data science and ignore or postpone studying statistics. One reason for that is the lack of resources and courses that teach statistics for data science and machine learning.

Statistics is a huge field of science, but the good news for data science learners is that not all statistics are required for data science and machine learning. However, this fact makes it more difficult for learners to study statistics because they are not sure where to start and what are the most relevant topics of statistics for data science.

What you'll learn

• You will learn to use data exploratory analysis in data science.
• You will learn the most common data types such as continuous and categorical data.
• You will learn the central tendency measures and the dispersion measures in statistics.
• You will learn the concepts of population data vs sample data.
• You will learn what random sampling means and how it affects data analysis.
• You will learn about outliers and sampling errors and how they are related to data analysis.
• You will learn how to visualize data distribution using boxplots, violin plots, histograms, and density plots.
• You will learn how to visualize categorical data using bar plots and pie charts.
• You will learn how to calculate correlation and covariance between features in the dataset.
• You will learn how to visualize a correlation matrix using heat maps.
• You will learn the most common probability distributions such as normal distribution and binomial distribution.
• You will learn how to perform normality tests to check for deviation from normality.
• You will learn how to test skewed distributions in real-world data.
• You will learn how to standardize and normalize data to have the same scale.
• You will learn how to transform skewed data to be normally distributed using different transformation methods such as log, square root, and power transformation
• You will learn how to calculate confidence intervals for statistical estimates such as model accuracy.
• You will learn bootstrapping in statistics and how it is used in machine learning.
• You will learn how to evaluate machine learning models.
• You will practically understand the concepts of bias and variance in data modeling.
• You will understand what we mean by underfitting and overfitting in machine leaning and statistical modeling.
• You will learn the most common evaluation metrics for regression models in machine learning.
• You will learn the evaluation metrics for classification models.
• You will learn how to validate predictive machine learning such as regression and classification models.
• You will learn how to use different validation techniques for machine learning such as hold-out validation and cross-validation techniques.

Tensorflow 2.0: Deep Learning and Artificial Intelligence

 Tensorflow 2.0: Deep Learning and Artificial Intelligence - Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!

Preview this Course  -  GET COUPON CODE

What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.

Tensorflow is Google's library for deep learning and artificial intelligence.

Deep Learning has been responsible for some amazing achievements recently, such as:

Generating beautiful, photo-realistic images of people and things that never existed (GANs)

Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)

Self-driving cars (Computer Vision)

Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)

Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)

Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.

In other words, if you want to do deep learning, you gotta know Tensorflow.

This course is for beginner-level students all the way up to expert-level students. How can this be?

If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.

Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).

Current projects include:

Natural Language Processing (NLP)

Recommender Systems

Transfer Learning for Computer Vision

Generative Adversarial Networks (GANs)

Deep Reinforcement Learning Stock Trading Bot

Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.

This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).

Advanced Tensorflow topics include:

Deploying a model with Tensorflow Serving (Tensorflow in the cloud)

Deploying a model with Tensorflow Lite (mobile and embedded applications)

Distributed Tensorflow training with Distribution Strategies

Writing your own custom Tensorflow model

Converting Tensorflow 1.x code to Tensorflow 2.0

Constants, Variables, and Tensors

Eager execution

Gradient tape

Instructor's Note: This course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. If you are looking for a more theory-dense course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.

Thanks for reading, and I’ll see you in class!

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree

No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch

Not afraid of university-level math - get important details about algorithms that other courses leave out

Who this course is for:

• Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0
• Show less

What you'll learn

• Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
• Predict Stock Returns
• Time Series Forecasting
• Computer Vision
• How to build a Deep Reinforcement Learning Stock Trading Bot
• GANs (Generative Adversarial Networks)
• Recommender Systems
• Image Recognition
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Use Tensorflow Serving to serve your model using a RESTful API
• Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
• Use Tensorflow's Distribution Strategies to parallelize learning
• Low-level Tensorflow, gradient tape, and how to build your own custom models
• Natural Language Processing (NLP) with Deep Learning
• Demonstrate Moore's Law using Code
• Transfer Learning to create state-of-the-art image classifiers
• Show less

Statistics & Probability for Data Science – 25+ Projects

 

Statistics & Probability for Data Science – 25+ Projects - Gain control over your data with Math & Stats foundation required for Data Science, Machine Learning & Deep learning

Created by Manifold AI Learning ® | 20 hours on-demand video course

Preview this Course - GET COUPON CODE

The Growing availability of data has made way for Data Science and Machine Learning to become in-demand professions. We define Statistics for Data Science – Predictive Analytics as exposure to Statistics which is essential for anyone seeking a career in Data Science and Machine learning. In this course, you will get the required college math , statistics and its practical implementation from Data Analytics which are necessary to better understand what goes in the black box libraries(sklearn) that you would encounter in the Data Science Journey.

With this course, as a learner, you will be exposed to various Statistics and Machine Learning topics that will apply to real-world problems. The Ultimate goal of taking this structured approach is to integrate everything we learn and demonstrate practical insights in using Machine learning and Statistical Libraries beyond a black-box understanding.

What you’ll learn

• Learn Underlying Mathematics to build an intuitive understanding & relating it to Machine Learning and Data Science
• Hands-On Code Implementation with Python for each mathematical topic to deepen the knowledge
• Master the Advanced level in an Interactive learning approach to Strengthen your knowledge on Difficult & Important Topics
• Understand the Importance of Probability & Distributions, and choose the right function for your data.

Complete Machine Learning & Data Science Bootcamp 2023

 Complete Machine Learning & Data Science Bootcamp 2023 - Learn Data Science, Data Analysis, Machine Learning (Artificial Intelligence) and Python with Tensorflow, Pandas & more!

Preview this Course  -  GET COUPON CODE

This is a top selling Machine Learning and Data Science course just updated this month with the latest trends and skills for 2023! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 900,000+ 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, Meta, + other top tech companies. You will go from zero to mastery!

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:

Daniel Bourke:

A self-taught Machine Learning Engineer who lives on the internet with an uncurable desire to take long walks and fill up blank pages.

My experience in machine learning comes from working at one of Australia's fastest-growing artificial intelligence agencies, Max Kelsen.

I've worked on machine learning and data problems across a wide range of industries including healthcare, eCommerce, finance, retail and more.

Two of my favourite projects include building a machine learning model to extract information from doctors notes for one of Australia's leading medical research facilities, as well as building a natural language model to assess insurance claims for one of Australia's largest insurance groups.

Due to the performance of the natural language model (a model which reads insurance claims and decides which party is at fault), the insurance company were able to reduce their daily assessment load by up to 2,500 claims.

My long-term goal is to combine my knowledge of machine learning and my background in nutrition to work towards answering the question "what should I eat?".

Aside from building machine learning models on my own, I love writing about and making videos on the process. My articles and videos on machine learning on Medium, personal blog and YouTube have collectively received over 5-million views.

I love nothing more than a complicated topic explained in an entertaining and educative matter. I know what it's like to try and learn a new topic, online and on your own. So I pour my soul into making sure my creations are accessible as possible.

My modus operandi (a fancy term for my way of doing things) is learning to create and creating to learn. If you know the Japanese word for this concept, please let me know.

Questions are always welcome.

--------

Andrei Neagoie:

Andrei 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.

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 Meta 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

OpenCV with Python (Computer Vision)

 OpenCV with Python (Computer Vision) - Using Python Learn Computer Vision Course on OpenCV in Python from Basic to Advance

Preview this Course  -  GET COUPON CODE

Welcome to the OpenCV course. If you are interested in the field of Computer Vision or Deep Learning? Then this course is for you.

Nowadays, Computer Vision is used in Automation in every domain such as self-driving cars, warehouses, security, object tracking, feature matching, and many more.

Moreover, in this course, we are covering the basic to advance level core concepts for image and video processing. We have taken a practical approach to explain the core concept of image and video processing. This course is best for students who want to start their career as Computer Vision Engineer.

What you'll learn

• Images and Video read in Python using OpenCV.
• Resizing, Cropping of images and videos in Python using OpenCV.
• Draw shapes on images such as circle, rectangle, ellipse, line.
• Adding text message on images.
• Hide a portion of images using XOR, NOT and AND operation.
• Image manipulation such as smoothing, blurring.
• Read different type of images with (single channel, 3 channel, 4 channel images).
• Image splitting
• Saving Images and Videos files using OpenCV on our PC.
• Color conversation of images (BGR to gray), (BGR to RGB).
• Reading images from different package using PILLOW.
• Most important: contour and its properties for analyzing the shape of the object in an image.
• Detect line on the image with canny algorithm.
• Feature detection on images and matching between the images.
• Common problem student face while working on OpenCV.

Machine Learning in Python for Professionals

 Machine Learning in Python for Professionals - Learn advance machine learning concepts and build next generation AI systems

New, Created by Eduonix Learning Solutions

Preview this Course

GET COUPON CODE

Do you want to learn advanced Python algorithms used by professional developers?

We have created a complete and updated advanced program in machine learning who want to build complex machine learning solutions. This course covers advanced Python algorithms, which will help you learn how Python allows its users to create their own Data Structures enables to have full control over the functionality of the models.

What you'll learn

• Learn professional machine learning and data science tools
• Learn the foundation algorithms for supervised and unsupervised learning
• Learn to build recommendation systems
• Learn reinforcement learning from ground up

Clustering & Classification With Machine Learning In Python

 Clustering & Classification With Machine Learning In Python - Harness The Power Of Machine Learning For Unsupervised & Supervised Learning In Python

Created by Minerva Singh

Preview this Course

GET COUPON CODE

HERE IS WHY YOU SHOULD TAKE THIS COURSE:

This course your complete guide to both supervised & unsupervised learning using Python. This means, this course covers all the main aspects of practical data science and if you take this course, you can do away with taking other courses or buying books on Python based data science.

 In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal..

By becoming proficient in unsupervised & supervised learning in Python, you can give your company a competitive edge and boost your career to the next level.

LEARN FROM AN EXPERT DATA SCIENTIST WITH +5 YEARS OF EXPERIENCE:

My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I also just recently finished a PhD at Cambridge University.

I have several years of experience in analyzing real life data from different sources  using data science techniques and producing publications for international peer reviewed journals.

Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic .

This course will give you a robust grounding in the main aspects of machine learning- clustering & classification. 

Unlike other Python instructors, I dig deep into the machine learning features of Python and gives you a one-of-a-kind grounding in Python Data Science!

You will go all the way from carrying out data reading & cleaning  to machine learning to finally implementing simple deep learning based models using Python

THE COURSE COMPOSES OF 7 SECTIONS TO HELP YOU MASTER PYTHON MACHINE LEARNING:

• A full introduction to Python Data Science and powerful Python driven framework for data science, Anaconda • Getting started with Jupyter notebooks for implementing data science techniques in Python  • Data Structures and Reading in Pandas, including CSV, Excel and HTML data • How to Pre-Process and “Wrangle” your Python data by removing NAs/No data, handling conditional data, grouping by attributes, etc. 

• Machine Learning, Supervised Learning, Unsupervised Learning in Python

• Artificial neural networks (ANN) and Deep Learning. You’ll even discover how to use artificial neural networks and deep learning structures for classification! 

With such a rigorous grounding in so many topics, you will be an unbeatable data scientist by the end of the course.

NO PRIOR PYTHON OR STATISTICS OR MACHINE LEARNING KNOWLEDGE IS REQUIRED:

You’ll start by absorbing the most valuable Python Data Science basics and techniques.

I use easy-to-understand, hands-on methods to simplify and address even the most difficult concepts in Python.

My course will help you implement the methods using real data obtained from different sources.

After taking this course, you’ll easily use packages like Numpy, Pandas, and Matplotlib to work with real data in Python..

You’ll even understand concepts like unsupervised learning, dimension reduction and supervised learning.. I will even introduce you to deep learning and neural networks using the powerful H2o framework! 

Most importantly, you will learn to implement these techniques practically using Python. You will have access to all the data and scripts used in this course. Remember, I am always around to support my students!

JOIN MY COURSE NOW!

 

What you'll learn

• Harness The Power Of Anaconda/iPython For Practical Data Science
• Read In Data Into The Python Environment From Different Sources
• Carry Out Basic Data Pre-processing & Wrangling In Python
• Implement Unsupervised/Clustering Techniques Such As k-means Clustering
• Implement Dimensional Reduction Techniques (PCA) & Feature Selection
• Implement Supervised Learning Techniques/Classification Such As Random Forests In Python
• Neural Network & Deep Learning Based Classification