# Machine Learning, Data Science and Deep Learning with Python

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- 14 hours on-demand video
- 6 articles
- Full lifetime access
- Access on mobile and TV

- Certificate of Completion

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Try Udemy for Business- Build artificial neural networks with Tensorflow and Keras
- Classify images, data, and sentiments using deep learning
- Make predictions using linear regression, polynomial regression, and multivariate regression
- Data Visualization with MatPlotLib and Seaborn
- Implement machine learning at massive scale with Apache Spark's MLLib
- Understand reinforcement learning - and how to build a Pac-Man bot
- Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
- Use train/test and K-Fold cross validation to choose and tune your models
- Build a movie recommender system using item-based and user-based collaborative filtering
- Clean your input data to remove outliers
- Design and evaluate A/B tests using T-Tests and P-Values

What to expect in this course, who it's for, and the general format we'll follow.

In part 2 of our Python crash course, we'll cover Python data structures including lists, tuples, and dictionaries.

We cover the differences between continuous and discrete numerical data, categorical data, and ordinal data.

We'll cover how to compute the variation and standard deviation of a data distribution, and how to do it using some examples in Python.

Here we'll go over my solution to the exercise I challenged you with in the previous lecture - changing our fabricated data to have no real correlation between ages and purchases, and seeing if you can detect that using conditional probability.

An overview of Bayes' Theorem, and an example of using it to uncover misleading statistics surrounding the accuracy of drug testing.

We introduce the concept of linear regression and how it works, and use it to fit a line to some sample data using Python.

We cover the concepts of polynomial regression, and use it to fit a more complex page speed - purchase relationship in Python.

Multivariate models let us predict some value given more than one attribute. We cover the concept, then use it to build a model in Python to predict car prices based on their number of doors, mileage, and number of cylinders. We'll also get our first look at the statsmodels library in Python.

We'll actually write a working spam classifier, using real email training data and a surprisingly small amount of code!

Decision trees can automatically create a flow chart for making some decision, based on machine learning! Let's learn how they work.

One way to recommend items is to look for other people similar to you based on their behavior, and recommend stuff they liked that you haven't seen yet.

We'll implement a complete item-based collaborative filtering system that uses real-world movie ratings data to recommend movies to any user.

Data that includes many features or many different vectors can be thought of as having many dimensions. Often it's useful to reduce those dimensions down to something more easily visualized, for compression, or to just distill the most important information from a data set (that is, information that contributes the most to the data's variance.) Principal Component Analysis and Singular Value Decomposition do that.

Cloud-based data storage and analysis systems like Hadoop, Hive, Spark, and MapReduce are turning the field of data warehousing on its head. Instead of extracting, transforming, and then loading data into a data warehouse, the transformation step is now more efficiently done using a cluster after it's already been loaded. With computing and storage resources so cheap, this new approach now makes sense.

We'll describe the concept of reinforcement learning - including Markov Decision Processes, Q-Learning, and Dynamic Programming - all using a simple example of developing an intelligent Pac-Man.

Cleaning your raw input data is often the most important, and time-consuming, part of your job as a data scientist!

We'll walk through an example of coding up and running a decision tree using Apache Spark's MLLib! In this exercise, we try to predict if a job candidate will be hired based on their work and educational history, using a decision tree that can be distributed across an entire cluster with Spark.

We'll introduce the concept of TF-IDF (Term Frequency / Inverse Document Frequency) and how it applies to search problems, in preparation for using it with MLLib.

There are many limitations associated with running short-term A/B tests - novelty effects, seasonal effects, and more can lead you to the wrong decisions. We'll discuss the forces that may result in misleading A/B test results so you can watch out for them.

We'll cover the evolution of artificial neural networks from 1943 to modern-day architectures, which is a great way to understand how they work.

As with any new technology, sometimes we can become overzealous in how we use it. A few cautionary tales to make sure your deep learning work does more good than harm.

- You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The 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.

**New! Updated for Winter 2019**

*with extra content on feature engineering, regularization techniques, and tuning neural networks - as well as*

*Tensorflow 2.0**!*

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. This comprehensive machine learning tutorial includes over **100 lectures** spanning **14 hours of video**, and most topics include **hands-on Python code examples** you can use for reference and for practice. I’ll draw on **my 9 years of experience at Amazon 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, 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

Data Visualization in Python with MatPlotLib and Seaborn

Transfer Learning

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

Multiple 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

Feature Engineering

Hyperparameter Tuning

...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. And you'll also get access to this course's **Facebook Group**, where you can stay in touch with your classmates.

If you're new to Python, don't worry - the course starts with a crash course. If you've done some programming before, you should pick it up quickly. This course shows you how to get set up on Microsoft Windows-based PC's, Linux desktops, and Macs.

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 real-world industry data scientists. These are topics any successful technologist absolutely needs to know about, so what are you waiting 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*

- Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.
- Technologists curious about how deep learning really works
- 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.
- If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first.