Tutorials :

Machine Learning and Data Science Hands-on with Python and R

      Author: Baturi   |   30 October 2018   |   comments: 1

Machine Learning and Data Science Hands-on with Python and R

Machine Learning and Data Science Hands-on with Python and R
.MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 44100 Hz, 2ch | 30.2 GB
Duration: 72.5 hours | Genre: eLearning | Language: English


Machine Learning, Statistics, Python, AI, Tensorflow, AWS, Deep Learning, R Programming, NLP, Bayesian, BI and much more.

What you'll learn

Learn the use of Python for Data Science and Machine Learning
Master Machine Learning on Python & R
Master Machine Learning on Tensorflow
Learn Statistics, Python, Artificial Intelligence AI, Tensorflow, AWS.
Learn Deep Learning, R Programming, NLP, Bayesian Methods, A/B Testing, Business Intelligence BI, Regression.
Learn Hypothesis Testing, Algebra, Adaboost Regressor, Gaussian, Heuristic.
Learn Numpy, Pandas, MetDescriptionlit, Seaborn.
Learn Forecasting, Distribution, Normalization, Trend Analysis, Predictive Modeling, Fraud Detection.
Learn Neural Network, Sequential Model, Data Visualization, Data Analysis, Data Manipulation, KNN Algorithm.
Learn Decision Tree, Random Forests, Kmeans Clustering, Vector Machine, Time Series Analysis, Market Basket Analysis

Requirements

No prior knowledge of machine learning required
Basic knowledge of R tool is an added advantage
Basic Python and Mathematics (Linear Algebra Basics) is an added advantage
Computer Access

Description

Learn from well designed, well-crafted study materials on Machine Learning ML, Statistics, Python, Artificial Intelligence AI, Tensorflow, AWS, Deep Learning, R Programming, NLP, Bayesian Methods, A/B Testing, Face Detection, Business Intelligence BI, Regression, Hypothesis Testing, Algebra, Adaboost Regressor, Gaussian, Heuristic, Numpy, Pandas, MetDescriptionlit, Seaborn, Forecasting, Distribution, Normalization, Trend Analysis, Predictive Modeling, Fraud Detection, Neural Network, Sequential Model, Data Visualization, Data Analysis, Data Manipulation, KNN Algorithm, Decision Tree, Random Forests, Kmeans Clustering, Vector Machine, Time Series Analysis, Market Basket Analysis. Learn by doing. Full Lifetime Access.

Get the skills to work with implementations and develop capabilities that you can use to deliver results in a machine learning project. This program will help you build the foundation for a solid career in Machine learning Tools. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions.





Machine learning is a subfield of computer science stemming from research into artificial intelligence. It has strong ties to statistics and mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining,] although that focuses more on exploratory data analysis. Machine learning and pattern recognition "can be viewed as two facets of the same field.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

Machine learning has proven to be a fruitful area of research, spawning a number of different problems and algorithms for their solution. This algorithm vary in their goals,in the available training data, and in the learning strategies. The ability to learn must be part of any system that would claim to possess general intelligence.

Who is the target audience?

Anyone who wants to learn about Machine Learning.
Data Engineers, Software Engineers, Technical managers, Analysts, Architects, IT operations etc.
Data scientists, Researchers and Students
This course can be taken by anyone. It starts from scratch and has taken care of all concepts required.
Any students in college who want to start a career in Data Science.










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Comments (1)
  #14 Author: Mariadup, 31 October 2018 18:41


Join Date: -- Status:
Я думаю, что это хорошая идея. дроп тут раздача дедиков
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