Machine Learning with Python ECE-A Navya Sri A 20AJ1A0411
Topics to be covered…..  Introduction to Machine Learning  Supervised Learning  Unsupervised Learning  Python libraries for Machine Learning
What is Machine Learning?  The capability of Artificial Intelligence systems to learn by extracting patterns from data is known as Machine Learning.  Machine Learning is an idea to learn from examples and experience, without being explicitly programmed. Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the data given. K. Anvesh, Dept. of IT
Introduction to Machine Learning  Python is a popular platform used for research and development of production systems. It is a vast language with number of modules, packages and libraries that provides multiple ways of achieving a task.  Python and its libraries like NumPy, Pandas, SciPy, Scikit-Learn, Matplotlib are used in data science and data analysis. They are also extensively used for creating scalable machine learning algorithms. K. Anvesh, Dept. of IT
 Python implements popular machine learning techniques such as Classification, Regression, Recommendation, and Clustering.  Python offers ready-made framework for performing data mining tasks on large volumes of data effectively in lesser time K. Anvesh, Dept. of IT
What is Machine Learning?  Data science, machine learning and artificial intelligence are some of the top trending topics in the tech world today. Data mining and Bayesian analysis are trending and this is adding the demand for machine learning.  Machine Learning  Study of algorithms that improve their performance at some task with experience K. Anvesh, Dept. of IT
Machine learning is a discipline that deals with programming the systems so as to make them automatically learn and improve with experience. Here, learning implies recognizing and understanding the input data and taking informed decisions based on the supplied data. It is very difficult to consider all the decisions based on all possible inputs. K. Anvesh, Dept. of IT
ML Machine Learning (ML) is an automated learning with little or no human intervention. It involves programming computers so that they learn from the available inputs. The main purpose of machine learning is to explore and construct algorithms that can learn from the previous data and make predictions on new input data. K. Anvesh, Dept. of IT
Growth of Machine Learning  Machine learning is preferred approach to Speech recognition, Natural language processing Computer vision Medical outcomes analysis Robot control Computational biology  This trend is accelerating Improved machine learning algorithms Improved data capture, networking, faster computers K. Anvesh, Dept. of IT
Applications of Machine Learning Algorithms  The developed machine learning algorithms are used in various applications such as: K. Anvesh, Dept. of IT Web search Computational biology Finance E-commerce Space exploration Robotics Information extraction Social networks Vision processing Language processing Forecasting things like stock market trends, weather Pattern recognition Games [Your favorite area]
Benefits of Machine Learning  Powerful Processing  Better Decision Making & Prediction  Quicker Processing  Accurate  Affordable Data Management  Inexpensive  Analyzing Complex Big Data K. Anvesh, Dept. of IT
Steps Involved in Machine Learning  A machine learning project involves the following steps: Defining a Problem Preparing Data Evaluating Algorithms Improving Results Presenting Results K. Anvesh, Dept. of IT
K. Anvesh, Dept. of IT
K. Anvesh, Dept. of IT
K. Anvesh, Dept. of IT
Magic? No, more like gardening Seeds = Algorithms Nutrients = Data Gardener = You Plants = Programs K. Anvesh, Dept. of IT
So what the machine learning is…  Automating automation  Getting computers to program themselves  Writing software is the bottleneck  Let the data do the work instead! K. Anvesh, Dept. of IT
Machine Learning Techniques Given below are some techniques in this Machine Learning tutorial.  Classification  Categorization  Clustering  Trend analysis  Anomaly detection  Visualization  Decision making K. Anvesh, Dept. of IT
ML in a Nutshell  Machine Learning is a sub-set of Artificial Intelligence where computer algorithms are used to autonomously learn from data and information. Machine learning computers can change and improve their algorithms all by themselves.  Tens of thousands of machine learning algorithms  Every machine learning algorithm has three components:  Representation  Evaluation  Optimization K. Anvesh, Dept. of IT
Representation  Decision trees  Sets of rules / Logic programs  Instances  Graphical models  Neural networks  Support vector machines (SVM)  Model ensembles etc……… K. Anvesh, Dept. of IT
Evaluation Accuracy Precision and recall Squared error Likelihood Posterior probability Cost / Utility Margin Entropy K-L divergence Etc. K. Anvesh, Dept. of IT
Optimization  Combinatorial optimization  E.g.: Greedy search  Convex optimization  E.g.: Gradient descent  Constrained optimization  E.g.: Linear programming K. Anvesh, Dept. of IT
THANK YOU K. Anvesh, Dept. of IT

machine-learning-with-python (1).ppt

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    Topics to becovered…..  Introduction to Machine Learning  Supervised Learning  Unsupervised Learning  Python libraries for Machine Learning
  • 3.
    What is MachineLearning?  The capability of Artificial Intelligence systems to learn by extracting patterns from data is known as Machine Learning.  Machine Learning is an idea to learn from examples and experience, without being explicitly programmed. Instead of writing code, you feed data to the generic algorithm, and it builds logic based on the data given. K. Anvesh, Dept. of IT
  • 4.
    Introduction to Machine Learning Python is a popular platform used for research and development of production systems. It is a vast language with number of modules, packages and libraries that provides multiple ways of achieving a task.  Python and its libraries like NumPy, Pandas, SciPy, Scikit-Learn, Matplotlib are used in data science and data analysis. They are also extensively used for creating scalable machine learning algorithms. K. Anvesh, Dept. of IT
  • 5.
     Python implementspopular machine learning techniques such as Classification, Regression, Recommendation, and Clustering.  Python offers ready-made framework for performing data mining tasks on large volumes of data effectively in lesser time K. Anvesh, Dept. of IT
  • 6.
    What is MachineLearning?  Data science, machine learning and artificial intelligence are some of the top trending topics in the tech world today. Data mining and Bayesian analysis are trending and this is adding the demand for machine learning.  Machine Learning  Study of algorithms that improve their performance at some task with experience K. Anvesh, Dept. of IT
  • 7.
    Machine learning isa discipline that deals with programming the systems so as to make them automatically learn and improve with experience. Here, learning implies recognizing and understanding the input data and taking informed decisions based on the supplied data. It is very difficult to consider all the decisions based on all possible inputs. K. Anvesh, Dept. of IT
  • 8.
    ML Machine Learning (ML)is an automated learning with little or no human intervention. It involves programming computers so that they learn from the available inputs. The main purpose of machine learning is to explore and construct algorithms that can learn from the previous data and make predictions on new input data. K. Anvesh, Dept. of IT
  • 9.
    Growth of MachineLearning  Machine learning is preferred approach to Speech recognition, Natural language processing Computer vision Medical outcomes analysis Robot control Computational biology  This trend is accelerating Improved machine learning algorithms Improved data capture, networking, faster computers K. Anvesh, Dept. of IT
  • 10.
    Applications of MachineLearning Algorithms  The developed machine learning algorithms are used in various applications such as: K. Anvesh, Dept. of IT Web search Computational biology Finance E-commerce Space exploration Robotics Information extraction Social networks Vision processing Language processing Forecasting things like stock market trends, weather Pattern recognition Games [Your favorite area]
  • 11.
    Benefits of MachineLearning  Powerful Processing  Better Decision Making & Prediction  Quicker Processing  Accurate  Affordable Data Management  Inexpensive  Analyzing Complex Big Data K. Anvesh, Dept. of IT
  • 12.
    Steps Involved inMachine Learning  A machine learning project involves the following steps: Defining a Problem Preparing Data Evaluating Algorithms Improving Results Presenting Results K. Anvesh, Dept. of IT
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    Magic? No, more likegardening Seeds = Algorithms Nutrients = Data Gardener = You Plants = Programs K. Anvesh, Dept. of IT
  • 17.
    So what themachine learning is…  Automating automation  Getting computers to program themselves  Writing software is the bottleneck  Let the data do the work instead! K. Anvesh, Dept. of IT
  • 18.
    Machine Learning Techniques Givenbelow are some techniques in this Machine Learning tutorial.  Classification  Categorization  Clustering  Trend analysis  Anomaly detection  Visualization  Decision making K. Anvesh, Dept. of IT
  • 19.
    ML in aNutshell  Machine Learning is a sub-set of Artificial Intelligence where computer algorithms are used to autonomously learn from data and information. Machine learning computers can change and improve their algorithms all by themselves.  Tens of thousands of machine learning algorithms  Every machine learning algorithm has three components:  Representation  Evaluation  Optimization K. Anvesh, Dept. of IT
  • 20.
    Representation  Decision trees Sets of rules / Logic programs  Instances  Graphical models  Neural networks  Support vector machines (SVM)  Model ensembles etc……… K. Anvesh, Dept. of IT
  • 21.
    Evaluation Accuracy Precision and recall Squarederror Likelihood Posterior probability Cost / Utility Margin Entropy K-L divergence Etc. K. Anvesh, Dept. of IT
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    Optimization  Combinatorial optimization E.g.: Greedy search  Convex optimization  E.g.: Gradient descent  Constrained optimization  E.g.: Linear programming K. Anvesh, Dept. of IT
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