Get yourself trained on LEARNING PATH: Python: with this Online Training LEARNING PATH: Python: Real-World Data Science with Python.
Online Training LEARNING PATH: Python: Real-World Data Science with Python
In todays world, everyone wants to gain insights from the deluge of data coming their way. Data mining provides a way of finding these insights, and Python is one of the most popular languages for data mining, providing both power and flexibility in analysis. Python has become the language of choice for data scientists for data analysis, visualization, and machine learning. Machine learning gives you unimaginably powerful insights into data. Deep learning is the next step to machine learning with a more advanced implementation. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results. If you’re interested to dive into the future of data science and implement intelligent systems using deep and machine learning with Python, then go for this Learning Path. Packts 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. The highlights of this Learning Path are: Know how to use Python libraries and mathematical toolkits such as numpy, pandas, matplotlib, and sci-kit learnSuccessfully evaluate and apply the most effective models to problemsDiscover the methods of image classification and harness object recognition using deep learningGet to know recurrent neural networks for the textual sentimental analysis modelLets take a quick look at your learning journey. In this Learning Path, youll discover the key concepts of data mining and learn how to apply different data mining techniques to find the valuable insights hidden in real-world data. Youll also tackle some notorious data mining problems to get a concrete understanding of these techniques. Youll be introduced you to the important data mining concepts and the Python libraries used for data mining. Youll understand the process of cleaning data and the steps involved in filtering out noise and ensuring that the data available can be used for accurate analysis. Youll also build your first intelligent application that makes predictions from data. This Learning Path is also a unique blend of projects that teach you what Machine Learning is all about and how you can implement machine learning concepts in practice. Six different independent projects will help you master machine learning in Python. Further, this Learning Path will take you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understand automatic differentiation. Youll get a thorough training in convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. Also, we will examine the performance of the sentimental analysis model and will conclude with the introduction of TensorFlow. By the end of this Learning Path, youll be able to implement intelligent data mining systems using deep and machine learning with Python. Meet Your Experts: We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth: SaimadhuPolamuriis 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.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.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.
Udemy helps organizations of all kinds prepare for the ever-evolving future of work. Our curated collection of top-rated business and technical courses gives companies, governments, and nonprofits the power to develop in-house expertise and satisfy employees’ hunger for learning and development.
Learn on your schedule with Udemy
Investing in yourself through Learning
As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.