Learning Path: Deep Dive Into Python Machine Learning

Confidently take your data mining and machine learning skills to your work

The world is emitting data at an enormous rate. There is a need for professionals who can confidently work with data and output meaningful insight. Data Science is a rewarding career field that allows you to solve some of the world's most interesting problems. This Learning Path will give you hands-on experience with popular Python data mining and machine learning algorithms. First, we'll expand your knowledge base by covering basic to advanced concepts of Python. Then, we'll give you hands-on experience with the popular Python data mining algorithms. Going forward, we'll learn how to perform various machine learning tasks in the real world. Finally, we'll dive into the future of data science and implement intelligent systems using deep learning with Python.

Daniel Arbuckle

Daniel Arbuckle holds a Doctorate in Computer Science from the University of Southern California, where he specialized in robotics and was a member of the nanotechnology lab. He now has more than ten years behind him as a consultant, during which time he’s been using Python to help an assortment of businesses, from clothing manufacturers to crowd sourcing platforms. Python has been his primary development language since he was in High School. He’s also an award-winning teacher of programming and computer science.

Saimadhu Polamuri

Saimadhu Polamuri is a data science educator and the founder of Data Aspirant, a Data Science portal for beginners. He has 3 years of experience in data mining and 5 years of experience in Python. He is also interested in big data technologies such as Hadoop, Pig, and Spark. He has a good command of the R programming language and Matlab. He has a rudimentary understanding of Cpp Computer vision library (opencv) and big data technologies.

Prateek Joshi

Prateek Joshi is an artificial intelligence researcher, an author of several books, and a TEDx speaker. He has been featured in Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and many more publications. He is the founder of Pluto AI, a venture funded Silicon Valley start-up building an intelligence platform for water facilities. He graduated from the University of Southern California with a Master's degree specializing in Artificial Intelligence. He has previously worked at NVIDIA and Microsoft Research.

Eder Santana

Eder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras: Deep Learning Library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.

Basic knowledge on Python. Aimed at Python programmers and data scientists who are willing to learn data mining and machine learning algorithms

Get to grips with the basics of operating in a Python development environment

Build Python packages to efficiently create reusable code

Become proficient at creating tools and utility programs in Python

Use the Git version control system to protect your development environment from unwanted changes

Harness the power of Python to automate other software

Distribute computation tasks across multiple processors

Handle high I/O loads with asynchronous I/O to get a smoother performance

Take advantage of Python's metaprogramming and programmable syntax features

Get acquainted to the concepts behind reactive programming and RxPy

Understand the basic data mining concepts to implement efficient models using Python

Know how to use Python libraries and mathematical toolkits such as numpy, pandas, matplotlib, and sci-kit learn

Build your first application that makes predictions from data and see how to evaluate the regression model

Analyze and implement Logistic Regression and the KNN model

Dive into the most effective data cleaning process to get accurate results

Master the classification concepts and implement the various classification algorithms

Explore classification algorithms and apply them to the income bracket estimation problem

Use predictive modeling and apply it to real-world problems

Understand how to perform market segmentation using unsupervised learning

Explore data visualization techniques to interact with your data in diverse ways

Find out how to build a recommendation engine

Understand how to interact with text data and build models to analyze it

Work with speech data and recognize spoken words using Hidden Markov Models

Analyze stock market data using Conditional Random Fields

Work with image data and build systems for image recognition and biometric face recognition

Grasp how to use deep neural networks to build an optical character recognition system

Get a quick brief about backpropagation

Perceive and understand automatic differentiation with Theano

Exhibit the powerful mechanism of seamless CPU and GPU usage with Theano

Understand the usage and innards of Keras to beautify your neural network designs

Apply convolutional neural networks for image analysis

Discover the methods of image classification and harness object recognition using deep learning

Get to know recurrent neural networks for the textual sentimental analysis model