Machine Learning Training
Data Science, Deep Learning, & Machine Learning with Python
Go handson with the neural network, artificial intelligence, and machine learning techniques employers are seeking!
About Machine Learning Course
Data Scientists enjoy one of the toppaying 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. This comprehensive course includes most topics include handson Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Reliance, Amazone and IMDb to guide you through what matters, and what doesn’t.
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 and data mining techniques real employers are looking for, including:
 Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s)
 Regression analysis
 KMeans Clustering
 Principal Component Analysis
 Train/Test and cross validation
 Bayesian Methods
 Decision Trees and Random Forests
 Multivariate Regression
 MultiLevel Models
 Support Vector Machines
 Reinforcement Learning
 Collaborative Filtering
 KNearest Neighbor
 Bias/Variance Tradeoff
 Ensemble Learning
 Term Frequency / Inverse Document Frequency
 Experimental Design and A/B Tests
…and much more! There’s also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to “big data” analyzed on a computing cluster.
If you’re new to Python, don’t worry. If you’ve done some programming before, you should pick it up quickly.
If you’re a programmer looking to switch into an exciting new career track, or a data analyst looking to make the transition into the tech industry – this course will teach you the basic techniques used by realworld industry data scientists. I think you’ll enjoy it!
What Will You Learn ?
 Develop using iPython notebooks
 Understand statistical measures such as standard deviation
 Visualize data distributions, probability mass functions, and probability density functions
 Visualize data with matplotlib
 Use covariance and correlation metrics
 Apply conditional probability for finding correlated features
 Use Bayes’ Theorem to identify false positives
 Make predictions using linear regression, polynomial regression, and multivariate regression
 Understand complex multilevel models
 Use train/test and KFold cross validation to choose the right model
 Build a spam classifier using Naive Bayes
 Use decision trees to predict hiring decisions
 Cluster data using KMeans clustering and Support Vector Machines (SVM)
 Build a movie recommender system using itembased and userbased collaborative filtering
 Predict classifications using KNearestNeighbor (KNN)
 Apply dimensionality reduction with Principal Component Analysis (PCA) to classify flowers
 Understand reinforcement learning – and how to build a PacMan bot
 Clean your input data to remove outliers
 Implement machine learning, clustering, and search using TF/IDF at massive scale with Apache Spark’s MLLib
Design and evaluate A/B tests using TTests and PValues.
Requirements
You’ll need a desktop computer (Windows, Mac, or Linux) capable of running Enthought Canopy 1.6.2 or newer. During course will walk you through installing the necessary free software.
Some prior coding or scripting experience is required.
At least high school level math skills will be required.
This course walks through getting set up on a Microsoft Windows based desktop PC. While the code in this course will run on other operating systems, we cannot provide OSspecific support for them.
Who can learn Machine Learning Course ?
Software developers or programmers who want to transition into the lucrative data science career path will learn a lot from this course.
Data analysts in the finance or other nontech 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.
If you have no prior coding or scripting experience, you should NOT take this course – yet. First Learn Python course.
For More Details Call 9930112627/9664545072.
Course Features
 Lectures 83
 Quizzes 0
 Duration 50 hours
 Skill level All levels
 Language English
 Students 97
 Assessments Yes

Introduction

Statistics and Probability Refresher, and Python Practise
 Types of Data
 Mean, Median, Mode
 Using mean, median, and mode in Python
 Variation and Standard Deviation
 Probability Density Function; Probability Mass Function
 Common Data Distributions
 Percentiles and Moments
 A Crash Course in matplotlib
 Covariance and Correlation
 Conditional Probability
 Exercise Solution: Conditional Probability of Purchase by Age
 Bayes’ Theorem

Predictive Models

Machine Learning with Python
 Supervised vs. Unsupervised Learning, and Train/Test
 Using Train/Test to Prevent Overfitting a Polynomial Regression
 Bayesian Methods: Concepts
 Implementing a Spam Classifier with Naive Bayes
 KMeans Clustering
 Clustering people based on income and age
 Measuring Entropy
 Install GraphViz
 Decision Trees: Concepts
 Decision Trees: Predicting Hiring Decisions
 Ensemble Learning
 Support Vector Machines (SVM) Overview
 Using SVM to cluster people using scikitlearn

Recommender Systems

More Data Mining and Machine Learning Techniques

Dealing with RealWorld Data

Apache Spark: Machine Learning on Big Data

Experimental Design

Deep Learning and Neural Networks
 Deep Learning PreRequisites
 The History of Artificial Neural Networks
 Deep Learning in the Tensorflow Playground
 Deep Learning Details
 Introducing Tensorflow
 Using Tensorflow, Part 1
 Using Tensorflow, Part 2
 Introducing Keras
 Using Keras to Predict Political Affiliations
 Convolutional Neural Networks (CNN’s)
 Using CNN’s for handwriting recognition
 Recurrent Neural Networks (RNN’s)
 Using a RNN for sentiment analysis
 The Ethics of Deep Learning The Ethics of Deep Learning
 Learning More about Deep Learning