Machine Learning, Data Science and Deep Learning with Python Course

Source: Deep Learning on Medium

Machine Learning, Data Science and Deep Learning with Python Course

New! Updated for Winter 2019 with extra content on feature engineering, regularization techniques, and tuning neural networks — as well as Tensorflow 2.0 support!

Machine Learning and artificial intelligence (AI) are everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. That’s just the average! And it’s not just about money — it’s interesting work too!
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If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry — and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 14 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t.

You won’t find academic, deeply mathematical coverage of these algorithms in this course — the focus is on practical understanding and application of them.

  • Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s) with TensorFlow and Keras
  • Data Visualization in Python with MatPlotLib and Seaborn
  • Transfer Learning
  • Sentiment analysis
  • Image recognition and classification
  • Regression analysis
  • K-Means Clustering
  • Principal Component Analysis
  • Train/Test and cross-validation
  • Bayesian Methods
  • Decision Trees and Random Forests
  • Multiple Regression
  • Multi-Level Models
  • Support Vector Machines
  • Reinforcement Learning
  • Collaborative Filtering
  • K-Nearest Neighbor
  • Bias/Variance Tradeoff
  • Ensemble Learning
  • Term Frequency / Inverse Document Frequency
  • Experimental Design and A/B Tests
  • Feature Engineering
  • Hyperparameter Tuning

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. We’ll cover the machine learning, AI, and data mining techniques real employers are looking for, including:

If you’re new to Python, don’t worry — the course starts with a crash course. This course shows you how to get set up on Microsoft Windows-based PCs, Linux desktops, and Macs.

Who this course is for: