Coursera courses

Google [link]

• Google Data Analytics Professional Certificate [link]

  1. Foundations: Data, Data, Everywhere [link]

  2. Ask Questions to Make Data-Driven Decisions [link]

  3. Prepare Data for Exploration [link]

  4. Process Data from Dirty to Clean [link]

  5. Analyze Data to Answer Questions [link]

  6. Share Data Through the Art of Visualization [link]

  7. Data Analysis with R Programming [link]

  8. Google Data Analytics Capstone: Complete a Case Study [link]

• Google Advanced Data Analytics Professional Certificate [link]

• Google Business Intelligence Professional Certificate [link]

Google Cloud [link]

• Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate [link]

  1. Google Cloud Big Data and Machine Learning Fundamentals [link]

  2. How Google does Machine Learning [link]

  3. Launching into Machine Learning [link]

  4. TensorFlow on Google Cloud [link]

  5. Feature Engineering [link]

  6. Machine Learning in the Enterprise [link]

  7. Production Machine Learning Systems [link]

  8. Machine Learning Operations (MLOps): Getting Started [link]

  9. ML Pipelines on Google Cloud [link]

• Preparing for Google Cloud Certification: Cloud Data Engineer Professional Certificate [link]

  1. Google Cloud Big Data and Machine Learning Fundamentals [link]

  2. Modernizing Data Lakes and Data Warehouses with Google Cloud [link]

  3. Building Batch Data Pipelines on Google Cloud [link]

  4. Building Resilient Streaming Analytics Systems on Google Cloud [link]

  5. Smart Analytics, Machine Learning, and AI on GCP [link]

  6. Preparing for the Google Cloud Professional Data Engineer Exam [link]

• Advanced Machine Learning on Google Cloud Specialization [link]

  1. Production Machine Learning Systems [link]

  2. Computer Vision Fundamentals with Google Cloud [link]

  3. Sequence Models for Time Series and Natural Language Processing [link]

  4. Recommendation Systems with TensorFlow on GCP [link]

• Digital Transformation Using AI/ML with Google Cloud Specialization [link]

  1. Business Transformation with Google Cloud [link]

  2. Infrastructure and Application Modernization with Google Cloud [link]

  3. Managing Machine Learning Projects with Google Cloud [link]

• Data Engineering, Big Data, and Machine Learning on GCP Specialization [link]

  1. Google Cloud Big Data and Machine Learning Fundamentals [link]

  2. Modernizing Data Lakes and Data Warehouses with Google Cloud [link]

  3. Building Batch Data Pipelines on Google Cloud [link]

  4. Building Resilient Streaming Analytics Systems on Google Cloud [link]

  5. Smart Analytics, Machine Learning, and AI on Google Cloud [link]

• Serverless Data Processing with Dataflow Specialization [link]

  1. Serverless Data Processing with Dataflow: Foundations [link]

  2. Serverless Data Processing with Dataflow: Develop Pipelines [link]

  3. Serverless Data Processing with Dataflow: Operations [link]

• From Data to Insights with Google Cloud Specialization [link]

  1. Exploring ​and ​Preparing ​your ​Data with BigQuery [link]

  2. Creating New BigQuery Datasets and Visualizing Insights [link]

  3. Achieving Advanced Insights with BigQuery [link]

  4. Applying Machine Learning to your Data with Google Cloud [link]

• Creating Business Value with Data and Looker Specialization [link]

  1. Innovating with Data and Google Cloud [link]

  2. Analyzing and Visualizing Data in Looker [link]

  3. Developing Data Models with LookML [link]

DeepLearning.AI [link]

• Mathematics for Machine Learning and Data Science Specialization [link]

  1. Linear Algebra for Machine Learning and Data Science [link]

  2. Calculus for Machine Learning and Data Science [link]

  3. Probability & Statistics for Machine Learning & Data Science [link]

• Machine Learning Specialization [link]

  1. Supervised Machine Learning: Regression and Classification [link]

  2. Advanced Learning Algorithms [link]

  3. Unsupervised Learning, Recommenders, Reinforcement Learning [link]

• Deep Learning Specialization [link]

  1. Neural Networks and Deep Learning [link]

  2. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization [link]

  3. Structuring Machine Learning Projects [link]

  4. Convolutional Neural Networks [link]

  5. Sequence Models [link]

• TensorFlow Developer Professional Certificate [link]

  1. Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning [link]

  2. Convolutional Neural Networks in TensorFlow [link]

  3. Natural Language Processing in TensorFlow [link]

  4. Sequences, Time Series and Prediction [link]

• TensorFlow: Advanced Techniques Specialization [link]

  1. Custom Models, Layers, and Loss Functions with TensorFlow [link]

  2. Custom and Distributed Training with TensorFlow [link]

  3. Advanced Computer Vision with TensorFlow [link]

  4. Generative Deep Learning with TensorFlow [link]

• TensorFlow: Data and Deployment Specialization [link]

  1. Browser-based Models with TensorFlow.js [link]

  2. Device-based Models with TensorFlow Lite [link]

  3. Data Pipelines with TensorFlow Data Services [link]

  4. Advanced Deployment Scenarios with TensorFlow [link]

• Machine Learning Engineering for Production MLOps) Specialization [link]

  1. Introduction to Machine Learning in Production [link]

  2. Machine Learning Data Lifecycle in Production [link]

  3. Machine Learning Modeling Pipelines in Production [link]

  4. Deploying Machine Learning Models in Production [link]

• Natural Language Processing Specialization [link]

  1. Natural Language Processing with Classification and Vector Spaces [link]

  2. Natural Language Processing with Probabilistic Models [link]

  3. Natural Language Processing with Sequence Models [link]

  4. Natural Language Processing with Attention Models [link]

• Generative Adversarial Networks GANs) Specialization [link]

  1. Build Basic Generative Adversarial Networks GANs) [link]

  2. Build Better Generative Adversarial Networks GANs) [link]

  3. Apply Generative Adversarial Networks GANs) [link]

• AI for Medicine Specialization [link]

  1. AI for Medical Diagnosis [link]

  2. AI for Medical Prognosis [link]

  3. AI For Medical Treatment [link]

• Practical Data Science on the AWS Cloud Specialization [link]

IBM [link]

• IBM Introduction to Machine Learning Specialization [link]

  1. Exploratory Data Analysis for Machine Learning [link]

  2. Supervised Machine Learning: Regression [link]

  3. Supervised Machine Learning: Classification [link]

  4. Unsupervised Machine Learning [link]

• Introduction to Data Science Specialization [link]

  1. What is Data Science? [link]

  2. Tools for Data Science [link]

  3. Data Science Methodology [link]

  4. Databases and SQL for Data Science with Python [link]

• Advanced Data Science with IBM Specialization [link]

• Applied Data Science Specialization [link]

• Applied Data Science with R Specialization [link]

• BI Foundations with SQL, ETL and Data Warehousing Specialization [link]

• Data Analysis and Visualization Foundations Specialization [link]

• Data Engineering Foundations Specialization [link]

• Data Science Fundamentals with Python and SQL Specialization [link]

  1. Tools for Data Science [link]

  2. Python for Data Science, AI & Development [link]

  3. Python Project for Data Science [link]

  4. Statistics for Data Science with Python [link]

  5. Databases and SQL for Data Science with Python [link]

• IBM AI Foundations for Business Specialization [link]

• IBM Introduction to Machine Learning Specialization [link]

  1. Exploratory Data Analysis for Machine Learning [link]

  2. Supervised Machine Learning: Regression [link]

  3. Supervised Machine Learning: Classification [link]

  4. Unsupervised Machine Learning [link]

• Introduction to Data Science Specialization [link]

  1. What is Data Science? [link]

  2. Tools for Data Science [link]

  3. Data Science Methodology [link]

  4. Databases and SQL for Data Science with Python [link]

• IBM AI Engineering Professional Certificate [link]

• IBM Applied AI Professional Certificate [link]

• IBM Data Analyst Professional Certificate [link]

• IBM Data Analytics with Excel and R Professional Certificate [link]

• IBM Data Engineering Professional Certificate [link]

• IBM Data Science Professional Certificate contains Intro DS Specialization) [link]

  1. What is Data Science? [link]

  2. Tools for Data Science [link]

  3. Data Science Methodology [link]

  4. Python for Data Science, AI & Development [link]

  5. Python Project for Data Science [link]

  6. Databases and SQL for Data Science with Python [link]

  7. Data Analysis with Python [link]

  8. Data Visualization with Python [link]

  9. Machine Learning with Python [link]

  10. Applied Data Science Capstone [link]

• IBM Data Warehouse Engineer Professional Certificate contains BI Foundations) [link]

• IBM Machine Learning Professional Certificate contains Intro ML Specialization) [link]

  1. Exploratory Data Analysis for Machine Learning [link]

  2. Supervised Machine Learning: Regression [link]

  3. Supervised Machine Learning: Classification [link]

  4. Unsupervised Machine Learning [link]

  5. Deep Learning and Reinforcement Learning [link]

  6. Specialized Models: Time Series and Survival Analysis [link]

Imperial College London [link]

• TensorFlow 2 for Deep Learning Specialization [link]

  1. Getting started with TensorFlow 2 [link]

  2. Customising your models with TensorFlow 2 [link]

  3. Probabilistic Deep Learning with TensorFlow 2 [link]

University of Michigan [link]

• Applied Data Science with Python Specialization [link]

  1. Introduction to Data Science in Python [link]

  2. Applied Plotting, Charting & Data Representation in Python [link]

  3. Applied Machine Learning in Python [link]

  4. Applied Text Mining in Python [link]

  5. Applied Social Network Analysis in Python [link]

University of Washington [link]

• Machine Learning Specialization [link]

  1. Machine Learning Foundations: A Case Study Approach [link]

  2. Machine Learning: Regression [link]

  3. Machine Learning: Classification [link]

  4. Machine Learning: Clustering & Retrieval [link]

Johns Hopkins University [link]

• Data Science Specialization [link]

• Data Science: Foundations using R Specialization [link]

• Data Literacy Specialization [link]

  1. Data – What It Is, What We Can Do With It [link]

  2. Measurement – Turning Concepts into Data [link]

  3. Quantifying Relationships with Regression Models [link]

  4. What are the Chances? Probability and Uncertainty in Statistics [link]

  5. Data Literacy Capstone – Evaluating Research [link]

University of California, Davis [link]

• Learn SQL Basics for Data Science Specialization [link]

  1. SQL for Data Science [link]

  2. Data Wrangling, Analysis and AB Testing with SQL [link]

  3. Distributed Computing with Spark SQL [link]

  4. SQL for Data Science Capstone Project [link]

• Health Information Literacy for Data Analytics Specialization [link]

  1. Healthcare Data Literacy [link]

  2. Healthcare Data Models [link]

  3. Healthcare Data Quality and Governance [link]

  4. Analytical Solutions to Common Healthcare Problems [link]

University at Buffalo [link], The State University of New York [link]

• Data-Driven Decision Making DDDM) Specialization [link]

  1. Data-Driven Process Improvement

  2. Data Analysis and Visualization

  3. Applied Analytics and Data for Decision Making

University of Illinois at Urbana-Champaign [link]

• Introduction to Business Analytics and Information Economics Specialization [link]

  1. Business Analytics Executive Overview

  2. Infonomics I: Business Information Economics and Data Monetization

  3. Infonomics II: Business Information Management and Measurement

  4. Introduction to Business Analytics and Information Economics Capstone