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Data Science, Deep Learning & Machine Learning with...

Data Science, Deep Learning & Machine Learning with Python Module 1 - Saturday featured image

About this Course:

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.

This class requires you to know Python! If you do not have any programming background, you will not be eligible for this course.

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!

Module 1

This is the first module of the course. There are two modules all together. In this module, we will teach you the fundamental concepts of mathematics including statistics and probability to get you started on your journey to becoming a data scientist.

What am I going to get from this course?

• 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
• 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 multi-level models
• Use train/test and K-Fold cross validation to choose the right model
• Build a spam classifier using Naive Bayes
• Use decision trees to predict hiring decisions

Course Schedule

Lesson 1: Introduction to Machine Learning Basics - May 26th, 2018 01:00PM - 03:30PM

For this lesson, we will set up our local environment in our computers together to prepare for our future data science lessons. We will see what experienced data scientists do and look at their job descriptions in different fields. We shall see the requirements of the skills of a data scientist. We will be dissecting at why Python is useful for data science and analytics, then we will start using Python to work on simple spreadsheets and build data sets.

Topics Covered:
- Getting Started
- Getting what you need
- Installing Enthought Canopy
- Python Basics
- Introducing the Pandas Library

Lesson 2: Statistics and Probability Refresher, and Python Practice - June 2nd, 2018 01:00PM - 03:30PM

Let's dive right in to data science! We will be looking at mathematical and statistical concepts. You will learn the basic graphing concepts and how to deal with statistical data sets. We will also use Python to plot some charts and solve some problems here. At the same time, refresh your memory on how to use Python. We will end the lesson with a bit of probability concepts.

Topics Covered:
- Types of Data
- Mean, Median, Mode
- 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

Lesson 3: Predictive Models - June 9th, 2018 01:00PM - 03:30PM

This lesson will teach us all we need to know about basic machine learning! We will be building prediction models using graphing techniques. We will plot out charts and start predicting values. At the end of this class, we will work with a data set and do some exercise on price predictions based on historical data.

Topics Covered:
- Linear Regression
- Polynomial Regression
- Multivariate Regression, and Predicting Car Prices
- Multi-Level Models

Lesson 4: Machine Learning with Python - June 16th, 2018 01:00PM - 03:30PM

We will be applying what we learnt in the past few lessons. We will learn about probability concepts and explore how entropy affects machine learning. We will build decision trees and clustering, through which we will be able to build more advanced data science projects.

Topics Covered:
- 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
- K-Means 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 scikit-learn

Lesson 5: Collaborative Filtering for Recommendations - June 23rd, 2018 01:00PM - 03:30PM

- Use collaborative filtering to translate data on item ratings by users into recommendations

Lesson 6: From A/B Testing to Balancing Learning & Earning in Operations - June 30th, 2018 01:00PM - 03:30PM

- Use A/B testing to choose designs to implement based on response of the market
- Learn to balance learning & earning by weaving continuous experimentation into operations (via bandit problem methodologies)

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