Are you interested to enter into the world of data science and learn the most effective machine learning tools and techniques with Python? then you should surely go for this Learning Path.

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

Machine learning and data science are some of the top buzzwords in the technical world today. Machine learning -  the application and science of algorithms that makes sense of data, is the most exciting field of all the computer sciences! The resurgent interest in machine learning is due to the same factors that have made data science more popular than ever. We are living in an age where data comes in abundance; using the self-learning algorithms from the field of machine learning, you can turn this data into knowledge. Machine learning gives you unimaginably powerful insights into data. Python has topped the charts in the recent years over other programming languages. The usage of Python is such that it cannot be limited to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complex processes such as artificial intelligence, machine learning, natural language processing, data science, and so on.

The highlights of this Learning Path are: 

• Solve interesting, real-world problems using machine learning and Python as the learning  journey unfolds
• Use Python to visualize data spread across multiple dimensions and extract useful features

Let’s take a quick look at your learning journey. This Learning Path is your entry point to machine learning. It starts with an introduction to machine learning and Python language. You’ll learn the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. With the help of the various projects included, you’ll acquire the mechanics of several important machine learning algorithms. You’ll also be guided step-by-step to build your own models from scratch. You’ll learn to tackle data-driven problems and implement your solutions with the powerful yet simple Python language. Interesting and easy-to-follow examples—including news topic classification, spam email detection, online ad click-through prediction, and stock prices forecasts—will keep you glued to the screen. Moving further, six different independent projects will help you master machine learning in Python. Finally, you’ll have a broad picture of the machine learning ecosystem and mastered best practices for applying machine learning techniques.

By the end of this Learning Path, you’ll have learned to apply various machine learning algorithms with Python packages and libraries to implement your own machine learning models.

Meet Your Experts:

We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth:

Yuxi (Hayden) Liu is currently an applied research scientist working in the largest privately-owned Canadian artificial intelligence R&D company. He is focused on developing machine learning systems and models and implementing appropriate architectures for given learning tasks, including deep neural networks, convolutional neural networks, recurrent networks, SVM, and random forest. He has worked for a few years as a data scientist at several computational advertising companies, where he applied his machine learning expertise in ad optimization. Yuxi earned his degree from the University of Toronto, and published five first-authored IEEE transactions and conference papers during his master's research. He has authored a Packt book titled Python Machine Learning By Example, which was ranked the #1 best seller in Amazon India in 2017. He is also a machine learning education enthusiast and provides weekly training in machine learning.

Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a full-time lead instructor for a data science immersive program in New York City.