A key to own a successful career in Data Science

By ridhigrg |Email | Jul 15, 2019 | 7068 Views

Data Science: Deep Learning in Python
The MOST in-depth look at neural network theory, and how to code one with pure Python and Tensorflow

What you'll learn
  • Learn how Deep Learning REALLY works (not just some diagrams and magical black box code)
  • Learn how a neural network is built from basic building blocks (the neuron)
  • Code a neural network from scratch in Python and numpy
  • Code a neural network using Google's TensorFlow
  • Describe different types of neural networks and the different types of problems they are used for
  • Derive the backpropagation rule from first principles
  • Create a neural network with an output that has K > 2 classes using softmax
  • Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward"

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block and build full-on non-linear neural networks right out of the gate using Python and Numpy. 

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Data Science: Supervised Machine Learning in Python
Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn
What you'll learn
  • Understand and implement K-Nearest Neighbors in Python
  • Understand the limitations of KNN
  • User KNN to solve binary and multiclass classification problems
  • Understand and implement Naive Bayes and General Bayes Classifiers in Python
  • Understand the limitations of Bayes Classifiers
  • Understand and implement a Decision Tree in Python
  • Understand and implement the Perceptron in Python
  • Understand the limitations of the Perceptron
  • Understand hyperparameters and how to apply cross-validation
  • Understand the concepts of feature extraction and feature selection
  • Understand the pros and cons of classic machine learning methods and deep learning
  • Use Sci-Kit Learn
  • Implement a machine learning web service

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.

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.

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?

Data Science Career Guide - Interview Preparation
Prepare for your Data Science Interview with this full guide on a career in Data Science including practice questions!

What you'll learn
  • Create a great data science resume!
  • Understand various positions and titles available in the data science ecosystem.
  • Get practice with probability and statistics interview questions.
  • Build an understanding of good experiment design.
  • Get practice with SQL interview questions.

This course is designed to be the ultimate resource for getting a career as a Data Scientist. We'll start off with a general overview of the field and discuss multiple career paths, including Product Analyst, Data Engineering, Data Scientist, and many more. You'll understand the various opportunities available and the best way to pursue each of them. The course touches upon a wide variety of topics, including questions on probability, statistics, machine learning, product metrics, example data sets, A/B testing, market analysis, and much more! 

The course will be full of real questions sourced from employees working at some of the world's top technology companies, including Amazon, Square, Facebook, Google, Microsoft, Airbnb and more!

The course contains real questions with fully detailed explanations and solutions. Not only is the course designed for candidates to achieve a full understanding of possible interview questions, but also for recruiters to learn about what to look for in each question response. For questions requiring coded solutions, fully commented code examples will be shown for both Python and R. This way you can focus on understanding the code in a programming language you're already familiar with, instead of worrying about syntax!

Introduction to Data Science
An overview of modern data science: the practice of obtaining, exploring, modeling, and interpreting data.

What you'll learn
  • Programming with R, Python, and SQL
  • Understand roles and careers in data science
  • Sourcing data
  • Data science in math and statistics
  • Data science and machine learning

Develop your career as a data scientist, as you explore essential skills and principles. 

This course covers the necessary tools and concepts used in the data science industry, including machine learning, statistical inference, working with data at scale and much more. 

First, we'll start by showing you the entire process for data science projects and the different roles and skills that are needed. Then you'll learn the basics of obtaining data through a variety of sources, including web APIs and page scraping. We'll show you how to use tools like R, Python, the command line, and even spreadsheets to explore and manipulate data.

We'll also take a look at powerful techniques for analyzing data. We'll be covering a variety of techniques for planning, performing, and presenting your projects to help you get started in data science and making the most of the data that's all around you. 

Introduction to Machine Learning for Data Science
A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.
What you'll learn
Genuinely understand what Computer Science, Algorithms, Programming, Data, Big Data, Artificial Intelligence, Machine Learning, and Data Science is.
  • To understand how these different domains fit together, how they are different, and how to avoid the marketing fluff.
  • The Impacts of Machine Learning and Data Science is having on society.
  • To really understand computer technology has changed the world, with an appreciation of scale.
  • To know what problems Machine Learning can solve, and how the Machine Learning Process works.
How to avoid problems with Machine Learning, to successfully implement it without losing your mind!

Unlock the secrets of understanding Machine Learning for Data Science!
In this introductory course, the "Backyard Data Scientist" will guide you through the wilderness of Machine Learning for Data Science.  Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the "technosphere around us, why it's important now, and how it will dramatically change our world today and for days to come.

Data Science: Master Machine Learning Without Coding
Learn the Fundamentals Of Data Science & Machine Learning With Rapidminer (No Coding). Dataset & Solutions Included.

What you'll learn
  • Build predictive models using machine learning algorithms without writing a line of software code
  • Machine Learning is the Key to Your High-Earning Future
  • Leading companies understand that Machine Learning is the future, and are investing millions of dollars into Machine Learning Research. 
  • Machine Learning is the subset of Artificial Intelligence (AI) that enables computers to learn and perform tasks they haven't been explicitly programmed to do.

Data Scientists and Machine Learning Engineers who are skilled in Machine Learning are even higher in demand across the entire employment spectrum.  Many diverse industries are searching for innovation in the field, and their need for Machine Learning experts and engineers is rapidly increasing.

There's literally no other course on Udemy that teaches Machine Learning without the need for programming knowledge or coding, using free open source software!

Source: HOB