New! Updated for Winter 2019 with further content material on function engineering, regularization strategies, and tuning neural networks – in addition to Tensorflow 2.0!
Machine Learning and synthetic intelligence (AI) is in all places; if you wish to understand how corporations like Google, Amazon, and even Udemy extract which means and insights from huge knowledge units, this knowledge science course offers you the basics you want. Data Scientists take pleasure in one of many top-paying jobs, with a median wage of $120,000 in response to Glassdoor and Indeed. That’s simply the typical! And it’s not nearly cash – it’s attention-grabbing work too!
If you’ve received some programming or scripting expertise, this course will train you the strategies utilized by actual knowledge scientists and machine studying practitioners within the tech business – and put together you for a transfer into this sizzling profession path. This complete machine studying tutorial consists of over 100 lectures spanning 14 hours of video, and most matters embrace hands-on Python code examples you need to use for reference and for apply. I’ll draw on my 9 years of expertise at Amazon and IMDb to information you thru what issues, and what doesn’t.
Each idea is launched in plain English, avoiding complicated mathematical notation and jargon. It’s then demonstrated utilizing Python code you may experiment with and construct upon, alongside with notes you may hold for future reference. You gained’t discover tutorial, deeply mathematical protection of those algorithms on this course – the main target is on sensible understanding and software of them. At the tip, you’ll be given a last venture to use what you’ve realized!
The matters on this course come from an evaluation of actual necessities in knowledge scientist job listings from the most important tech employers. We’ll cowl the machine studying, AI, and knowledge mining strategies actual employers are on the lookout for, together with:
- 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 evaluation
- Image recognition and classification
- Regression evaluation
- Okay-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
- Okay-Nearest Neighbor
- Bias/Variance Tradeoff
- Ensemble Learning
- Term Frequency / Inverse Document Frequency
- Experimental Design and A/B Tests
- Feature Engineering
- Hyperparameter Tuning
…and way more! There’s additionally a complete part on machine studying with Apache Spark, which helps you to scale up these strategies to “big data” analyzed on a computing cluster. And you’ll additionally get entry to this course’s Facebook Group, the place you may keep in contact with your classmates.
If you’re new to Python, don’t fear – the course begins with a crash course. If you’ve performed some programming earlier than, it’s best to decide it up shortly. This course reveals you the way to get arrange on Microsoft Windows-based PC’s, Linux desktops, and Macs.
If you’re a programmer trying to change into an thrilling new profession monitor, or an information analyst trying to make the transition into the tech business – this course will train you the essential strategies utilized by real-world business knowledge scientists. These are matters any profitable technologist completely must find out about, so what are you ready for? Enroll now!
- “I started doing your course in 2015… Eventually I got interested and never thought that I will be working for corporate before a friend offered me this job. I am learning a lot which was impossible to learn in academia and enjoying it thoroughly. To me, your course is the one that helped me understand how to work with corporate problems. How to think to be a success in corporate AI research. I find you the most impressive instructor in ML, simple yet convincing.” – Kanad Basu, PhD
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