Python code is understandable by humans, which makes it easier to build models for machine learning. Since Python is a general-purpose language, it can do a set of complex machine learning tasks and enable you to build prototypes quickly that allows you to test your product for machine learning purposes.
Python is used for Machine learning by almost all programmers for their work. developers. All these features of Python make it the first choice for Machine learning. From development to implementation and maintenance, Python is assisting developers to be productive and confident about the software they are developing
Python is very much essential for machine learning, data science, artificial intelligence, deep learning, and natural language processing (NLP).
In this post, I have listed down the top 5 best free resources to learn Python for Machine Learning.
Data Science, Machine Learning, Data Analysis, Python & R
Description: This course you will learn:
How to Solve any data analytics problem using R and Python
Perform Data exploratory functions,importing and exporting the data sets,Data manipulation
Perform Logistic Regression using Cancer remission data set.
Data manipulation in R-Dply,Filter,multiple filter,mutate,arrange,summarize.
Data visualization in R-Bar graphs,Stacked bar,grouped bar graph,Line chart for time series
Association Analysis in R using Market Basket analysis.
Google’s Python Class
Description: This is a free class for people with a little bit of programming experience who want to learn Python. The class includes written materials, lecture videos, and lots of code exercises to practice Python coding. These materials are used within Google to introduce Python to people who have just a little programming experience.
The first exercises work on basic Python concepts like strings and lists, building up to the later exercises which are full programs dealing with text files, processes, and http connections. The class is geared for people who have a little bit of programming experience in some language, enough to know what a “variable” or “if statement” is. Beyond that, you do not need to be an expert programmer to use this material.
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Data Science with Analogies, Algorithms and Solved Problems
This course has been designed such that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.
We will walk you into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this field. While preparing this course special care is taken that the concepts are presented in fun and exciting way but at the same time, we dive deep into machine learning.
Here is a list of a few of the topics we will be learning:
• Difference between Data Mining and Deep Learning
• Data and 5 Vs of Big Data
• Types of Attributes
• Outliers
• Supervised learning, Unsupervised learning, Reinforcement learning
• Python Libraries
• CNN, RNN, LSTM
Machine Learning In Python
Description: This book focuses on the machine learning process and so covers just a few of the most effective and widely used algorithms. It does not provide a survey of machine learning techniques. Too many of the algorithms that might be included in a survey are not actively used by practitioners.
This book deals with one class of machine learning problems generally referred to as function approximation. Function approximation is a subset of problems that are called supervised learning problems. Linear regression and its classifier cousin, logistic regression, provide familiar examples of algorithms for function approximation problems. Function approximation problems include an enormous breadth of practical classification and regression problems in all sorts of arenas, including text classification, search responses, ad placements, spam filtering, predicting customer behavior, diagnostics, and so forth. The list
is almost endless.
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Machine Learning With Python
Description: Python is a general-purpose high level programming language that is being increasingly used in data science and in designing machine learning algorithms. This tutorial provides a quick introduction to Python and its libraries like numpy, scipy, pandas, matplotlib and explains how it can be applied to develop machine learning algorithms that solve real-world problems.
This tutorial starts with an introduction to machine learning and the Python language and shows you how to set up Python and its packages. It further covers all important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation.
This tutorial also provides various projects that teach you the techniques and functionalities such as news topic classification, spam email detection, online ad clickthrough prediction, stock prices forecast and other several important machine learning algorithms.
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Modern Machine Learning in Python
Description: Machine learning has become an important tool for multitude of scientific disciplines. Training based approaches are rapidly substituting traditional manually engineered pipelines. Python is becoming an increasingly central tool for data science. Python is becoming an increasingly central tool for data science.
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