Read by author. — Udemy, updated 12/2018. — MP4,
video: AVC 1280x720,
audio: AAC 44KHz 2 channels . — Duration 12:02:59. — Lec: 92.
Machine Learning and artificial intelligence (AI) is 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. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That’s just the average! And it’s not just about money – it’s interesting work too!
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.
Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!
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: Deep Learning / Neural Networks (MLP's, CNN's, RNN's) with TensorFlow and Keras; 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; Multivariate 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.