Categories
Machine Learning

Complete 2020 Data Science & Machine Learning Bootcamp Download

Welcome to the Complete Data Science and Machine Learning Bootcamp, the only course you need to learn Python and get into data science.

At over 40+ hours, this Python course is without a doubt the most comprehensive data science and machine learning course available online. Even if you have zero programming experience, this course will take you from beginner to mastery. Here’s why:

  • The course is a taught by the lead instructor at the App Brewery, London’s leading in-person programming bootcamp.
  • In the course, you’ll be learning the latest tools and technologies that are used by data scientists at Google, Amazon, or Netflix.
  • This course doesn’t cut any corners, there are beautiful animated explanation videos and real-world projects to build.
  • The curriculum was developed over a period of three years together with industry professionals, researchers and student testing and feedback.
  • To date, we’ve taught over 200,000 students how to code and many have gone on to change their lives by getting jobs in the industry or starting their own tech startup.
  • You’ll save yourself over $12,000 by enrolling, but get access to the same teaching materials and learn from the same instructor and curriculum as our in-person programming bootcamp.

We’ll take you step-by-step through video tutorials and teach you everything you need to know to succeed as a data scientist and machine learning professional.

The course includes over 35 hours of HD video tutorials and builds your programming knowledge while solving real-world problems.

In the curriculum, we cover a large number of important data science and machine learning topics, such as:

  • Data Cleaning and Pre-Processing
  • Data Exploration and Visualisation
  • Linear Regression
  • Multivariable Regression
  • Optimisation Algorithms and Gradient Descent
  • Naive Bayes Classification
  • Descriptive Statistics and Probability Theory
  • Neural Networks and Deep Learning
  • Model Evaluation and Analysis
  • Serving a Tensorflow Model

Throughout the course, we cover all the tools used by data scientists and machine learning experts, including:

  • Python 3
  • Tensorflow
  • Pandas
  • Numpy
  • Scikit Learn
  • Keras
  • Matplotlib
  • Seaborn
  • SciPy
  • SymPy

By the end of this course, you will be fluently programming in Python and be ready to tackle any data science projectWe’ll be covering all of these Python programming concepts:

  • Data Types and Variables
  • String Manipulation
  • Functions
  • Objects
  • Lists, Tuples and Dictionaries
  • Loops and Iterators
  • Conditionals and Control Flow
  • Generator Functions
  • Context Managers and Name Scoping
  • Error Handling

By working through real-world projects you get to understand the entire workflow of a data scientist which is incredibly valuable to a potential employer.

Sign up today, and look forward to:

  • 178+ HD Video Lectures
  • 30+ Code Challenges and Exercises
  • Fully Fledged Data Science and Machine Learning Projects
  • Programming Resources and Cheatsheets
  • Our best selling 12 Rules to Learn to Code eBook
  • $12,000+ data science & machine learning bootcamp course materials and curriculum

Don’t just take my word for it, check out what existing students have to say about my courses:

“One of the best courses I have taken. Everything is explained well, concepts are not glossed over. There is reinforcement in the challenges that helps solidify understanding. I’m only half way through but I feel like it is some of the best money I’ve ever spent.” -Robert Vance

“I’ve spent £27,000 on University….. Save some money and buy any course available by Philipp! Great stuff guys.” -Terry Woodward

“This course is amazingly immersive and quite all-inclusive from end-to-end to develop an app! Also gives practicality to apply the lesson straight away and full of fun with bunch of sense of humor, so it’s not boring to follow throughout the whole course. Keep up the good work guys!” – Marvin Septianus

“Great going so far. Like the idea of the quizzes to challenge us as we go along. Explanations are clear and easy to follow” -Lenox James

“Very good explained course. The tasks and challenges are fun to do learn an do! Would recommend it a thousand times.” -Andres Ariza

“I enjoy the step by step method they introduce the topics. Anyone with an interest in programming would be able to follow and program” -Isaac Barnor

“I am learning so much with this course; certainly beats reading older Android Ebooks that are so far out of date; Phillippe is so easy any understandable to learn from. Great Course have recommended to a few people.” -Dale Barnes

“This course has been amazing. Thanks for all the info. I’ll definitely try to put this in use. :)” -Devanshika Ghosh

“Great Narration and explanations. Very interactive lectures which make me keep looking forward to the next tutorial” -Bimal Becks

“English is not my native language but in this video, Phillip has great pronunciation so I don’t have problem even without subtitles :)” -Dreamerx85

“Clear, precise and easy to follow instructions & explanations!” -Andreea Andrei

“An incredible course in a succinct, well-thought-out, easy to understand package. I wish I had purchased this course first.” -Ian

REMEMBER… I’m so confident that you’ll love this course that we’re offering a FULL money back guarantee for 30 days! So it’s a complete no-brainer, sign up today with ZERO risks and EVERYTHING to gain.

So what are you waiting for? Click the buy now button and join the world’s best data science and machine learning course.

Who this course is for:

  • If you want to learn to code through building fun and useful projects, then take this course.
  • If you want to solve real-life problems using data.
  • If you want to learn how to build machine learning algorithms such as deep learning and neural networks.
  • If you are a seasoned programmer, take this course to get up to speed quickly with the workflow of a data scientist.
  • If you want to take ONE COURSE and learn everything you need to know about data science and machine learning then take this course.

Udemy Course Link: Click Here

Download Link:

[Torrent Download]: Click Here

Categories
Machine Learning

[Udemy] Cutting-Edge AI: Deep Reinforcement Learning in Python Download

Welcome to Cutting-Edge AI!

This is technically Deep Learning in Python part 11 of my deep learning series, and my 3rd reinforcement learning course.

Deep Reinforcement Learning is actually the combination of 2 topics: Reinforcement Learning and Deep Learning (Neural Networks).

While both of these have been around for quite some time, it’s only been recently that Deep Learning has really taken off, and along with it, Reinforcement Learning.

The maturation of deep learning has propelled advances in reinforcement learning, which has been around since the 1980s, although some aspects of it, such as the Bellman equation, have been for much longer.

Recently, these advances have allowed us to showcase just how powerful reinforcement learning can be.

We’ve seen how AlphaZero can master the game of Go using only self-play.

This is just a few years after the original AlphaGo already beat a world champion in Go.

We’ve seen real-world robots learn how to walk, and even recover after being kicked over, despite only being trained using simulation.

Simulation is nice because it doesn’t require actual hardware, which is expensive. If your agent falls down, no real damage is done.

We’ve seen real-world robots learn hand dexterity, which is no small feat.

Walking is one thing, but that involves coarse movements. Hand dexterity is complex – you have many degrees of freedom and many of the forces involved are extremely subtle.

Imagine using your foot to do something you usually do with your hand, and you immediately understand why this would be difficult.

Last but not least – video games.

Even just considering the past few months, we’ve seen some amazing developments. AIs are now beating professional players in CS:GO and Dota 2.

So what makes this course different from the first two?

Now that we know deep learning works with reinforcement learning, the question becomes: how do we improve these algorithms?

This course is going to show you a few different ways: including the powerful A2C (Advantage Actor-Critic) algorithm, the DDPG (Deep Deterministic Policy Gradient) algorithm, and evolution strategies.

Evolution strategies is a new and fresh take on reinforcement learning, that kind of throws away all the old theory in favor of a more “black box” approach, inspired by biological evolution.

What’s also great about this new course is the variety of environments we get to look at.

First, we’re going to look at the classic Atari environments. These are important because they show that reinforcement learning agents can learn based on images alone.

Second, we’re going to look at MuJoCo, which is a physics simulator. This is the first step to building a robot that can navigate the real-world and understand physics – we first have to show it can work with simulated physics.

Finally, we’re going to look at Flappy Bird, everyone’s favorite mobile game just a few years ago.

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

Suggested prerequisites:

  • Calculus
  • Probability
  • Object-oriented programming
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations
  • Linear regression
  • Gradient descent
  • Know how to build a convolutional neural network (CNN) in TensorFlow
  • Markov Decision Proccesses (MDPs)

TIPS (for getting through the course):

  • Watch it at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don’t, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Realize that most exercises will take you days or weeks to complete.
  • Write code yourself, don’t just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture “What order should I take your courses in?” (available in the Appendix of any of my courses, including the free Numpy course)

Who this course is for:

  • Students and professionals who want to apply Reinforcement Learning to their work and projects
  • Anyone who want

Udemy Courses Link: Click Here

Download Link:

[Torrent Download]: Click Here
[Google Drive Download]: Click Here