In this course, you’ll go hands-on with the neural network, artificial intelligence, and machine learning techniques employers are seeking! Enrolling in Course, Machine Learning, Data Science and Deep Learning with Python by Frank Kane.
Course: Machine Learning, Data Science and Deep Learning with Python
Author: Frank Kane (A former engineer and the senior manager from Amazon and IMDb, as well as a Data Miner and Software Engineer, holds 17 issued patents in the fields of distributed computing, data mining, and machine learning)
Description: Go hands-on with the neural network, artificial intelligence, and machine learning techniques employers are seeking!
Skill Level: All Levels
Content: 12 hours on-demand video
Lessons: 90 lectures
Includes: Lifetime access
Categories: Business / Data & Analytics
There is no risk: 30 day money back guarantee!
Learn Anywhere Available: iPhone, iPad, Tablets, iOS and Android
Recognition Certificate: Course Certificate of Completion.
Take the course, Data Science, Machine Learning, Data Science and Deep Learning with Python, you will be able to:
After taking this course, you will be able to develop using iPython notebooks
You will be able to visualize data distributions, probability mass functions, and probability density functions
You will be able to understand statistical measures such as standard deviation
You will be able to visualize data with matplotlib
You will be able to use covariance and correlation metrics
You will be able to use Bayes’ Theorem to identify false positives
You will be able to make predictions using linear regression, polynomial regression, and multivariate regression
You will be able to apply conditional probability for finding correlated features
You will be able to understand complex multi-level models
You will be able to use train/test and K-Fold cross-validation to choose the right model
Upon completion of the course, you will be able to build a spam classifier using Naive Bayes
You will be able to use decision trees to predict hiring decisions
You will be able to cluster data using K-Means clustering and Support Vector Machines (SVM)
You will be able to build a movie recommender system using item-based and user-based collaborative filtering
You will be able to predict classifications using K-Nearest-Neighbor (KNN)
Upon completion of the course, you will be able to understand reinforcement learning – and how to build a Pac-Man bot
At the end of the course, you will be able to apply dimensionality reduction with Principal Component Analysis (PCA) to classify flowers
You will be able to clean your input data to remove outliers
You will be able to design and evaluate A/B tests using T-Tests and P-Values
You will be able to implement machine learning, clustering, and search using TF/IDF at massive scale with Apache Spark’s MLLib.
This course is intended for anyone?
This course is for software developers or programmers who want to transition into the lucrative data science career path
This course is for data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you’ll need some prior experience in coding or scripting to be successful.
Enroll in This Online Course Now!
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