Top Courses Coursera's 10 Most Popular Data Science Courses of 2018

Posted in 'Top Courses' started by Carolyn, Dec 17, 2018.

  1. Carolyn

    Carolyn Founder at MoocLab Staff Member

    Gender:
    Female
    Location:
    Suffolk, UK
    According to Coursera, Data Science is one of their top 3 most popular fields of learning. The MOOC platform has recently shared their 10 most popular Data Science courses of 2018, and this holiday season is the perfect time to build your skills with some of these top courses available now on Coursera.

    #1 Machine Learning
    Offered by Stanford University
    Approx. 55 hours to complete

    This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. Go to Course ...


    #2 Neural Networks and Deep Learning
    Offered by deeplearning.ai
    Approx. 17 hours to complete

    If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.

    In this course, you will learn the foundations of deep learning. When you finish this class, you will:
    - Understand the major technology trends driving Deep Learning
    - Be able to build, train and apply fully connected deep neural networks
    - Know how to implement efficient (vectorized) neural networks
    - Understand the key parameters in a neural network's architecture
    Go to Course ...


    #3 Convolutional Neural Networks
    Offered by deeplearning.ai
    Approx. 20 hours to complete

    This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: - Understand how to build a convolutional neural network, including recent variations such as residual networks. - Know how to apply convolutional networks to visual detection and recognition tasks. - Know to use neural style transfer to generate art. - Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. Go to Course ...


    #4 Sequence Models
    Offered by deeplearning.ai
    Approx. 17 hours to complete

    This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis.
    Go to Course ...


    #5 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
    Offered by deeplearning.ai
    Approx. 14 hours to complete

    This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. Go to Course ...


    #6 Introduction to Data Science in Python
    Offered by University of Michigan
    Approx. 18 hours to complete

    This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. Go to Course ...


    #7 Structuring Machine Learning Projects
    Offered by deeplearning.ai
    Approx. 7 hours to complete

    You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning Go to Course ...


    #8 The Data Scientist’s Toolbox
    Offered by Johns Hopkins University
    Approx. 8 hours to complete

    In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio. Go to Course ...


    #9 R Programming
    Offered by Johns Hopkins University
    Approx. 20 hours to complete

    In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples. Go to Course ...


    #10 SQL for Data Science
    Offered by University of California, Davis
    Approx. 19 hours to complete

    This course is designed to give you a primer in the fundamentals of SQL and working with data so that you can begin analyzing it for data science purposes. You will begin to ask the right questions and come up with good answers to deliver valuable insights for your organization. This course starts with the basics and assumes you do not have any knowledge or skills in SQL. It will build on that foundation and gradually have you write both simple and complex queries to help you select data from tables. You'll start to work with different types of data like strings and numbers and discuss methods to filter and pare down your results. Go to Course ...
     
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