Coursera

4.53

Describe the use of data visualizations to talk about data and the results of data analysisIdentify Tableau as a data visualization tool and understand its usesExplain what data driven stories are including reference to their...

4.76

Describe the R programming language and its programming environmentExplain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectorsDescribe the options for generating visualizations in R...

4.64

Analyze data within a database using SQL and Python. Create a relational database on Cloud and work with tables. Compare and contrast DDL to DML. WriteSQL statements including SELECT, INSERT, UPDATE, and DELETE.

4.79

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and...

4.65

Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud. Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud. Employ BigQuery to...

4.7

Handle real-world image dataPlot loss and accuracyExplore strategies to prevent overfitting, including augmentation and dropoutLearn transfer learning and how learned features can be extracted from models

4.72

In this course, you will: - Learn about GANs and their applications - Understand the intuition behind the fundamental components of GANs - Explore and implement multiple GAN architectures - Build conditional GANs capable of...

4.6

Describe what a methodology is and why data scientists need a methodology. Describe the six stages in the Cross Industry Process for Data Mining (CRISP-DM) methodology including Business Understanding and Data Understanding. Describe some of...

4.67

Give examples of Machine Learning in various industries. Outline the steps machine learning uses to solve problems. Provide examples of various techniques used in machine learning. Describe the Python libraries for Machine Learning.

4.78

Differentiate between a capstone, case study, and a portfolioIdentify the key features and attributes of a completed case studyApply the practices and procedures associated with the data analysis process to a given set of dataDiscuss...

4.72

Use dynamic programming, hidden Markov models, and word embeddings to implement autocorrect, autocomplete & identify part-of-speech tags for words.

4.67

Describe Python data acquisition and analysis techniques. Analyze Python data using a dataset. Identify three Python libraries and describe their uses. Read data using Python's Pandas package.

4.58

Build natural language processing systems using TensorFlowProcess text, including tokenization and representing sentences as vectorsApply RNNs, GRUs, and LSTMs in TensorFlowTrain LSTMs on existing text to create original poetry and more

4.51

Demonstrate your Skills in Python - the language of choice for Data Science and Data AnalysisApply Python fundamentals, Python data structures, and working with data in PythonPlay the role of a Data Scientist / Data...

4.66

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc. ) as well as demonstrate how these models can solve co...

4.65

In this course on Linear Algebra we look at what linear algebra is and how it relates to vectors and matrices. Then we look through what vectors and matrices are and how to work with...

4.51

Understand techniques such as lambdas and manipulating csv filesDescribe common Python functionality and features used for data scienceQuery DataFrame structures for cleaning and processingExplain distributions, sampling, and t-tests

4.66

Solve time series and forecasting problems in TensorFlowPrepare data for time series learning using best practicesExplore how RNNs and ConvNets can be used for predictionsBuild a sunspot prediction model using real-world data

4.53

Apply techniques to manage modeling resources and best serve batch and real-time inference requests. Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.

4.74

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be applied directly to...