Elite

Machine Learning

About Course

  • This course provides a introduction to machine learning, datamining, and statistical pattern recognition. The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. In this course 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 knowledge needed to quickly and powerfully apply these techniques to new problems.
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Description

  • Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
  • Machine learning is the science of getting computers to act without being explicitly programmed. 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. The future of AI and machine learning in this industry include an ability to evaluate hedge funds and analyze stock market movement to make financial recommendations. Machine learning may render usernames, passwords, and security questions obsolete by taking anomaly -detection to the next level: facial or voice recognition, or other biometric data.

What will I learn

  • Data Clustering Algorithms
  • Data Classification Algorithms
  • Artificial Neural Network
  • Machine learning Algorithms
  • Machine Learning Concepts
  • Decision Tree

Why? - Description

  • Machine Learning provides smart alternative to analyzing vast volumes of data. By developing fast and efficient algorithms and data-driven models for real-time processing of data, Machine Learning can produce accurate results and analysis. The nearly limitless quantity of available data, affordable data storage, and the growth of less expensive and more powerful processing has propelled the growth of machine learning.

Tags

  • Artificial Neural Network
  • Logistics Regression
  • Machine Learning (ML) Algorithms
  • Machine Learning

Topics for this course

  1. What is Machine learning?
  2. Data Pre-processing
  3. Libraries of Machine Learning: Tensorflow, Keras
  4. ML algorithms
  5. Practical Knowledge of Machine learning examples
    1. Simple Linear regression
    2. Decision Tree Regression
    3. Rendom Forest Regression
    4. Multiple regression Logistic regression
    5. Predicting house prices with regression
    1. Definition
    2. Types of clustering
    3. Hierarchical Clustering
    4. PCA
    5. Dimensionality Reduction
    6. The k-means clustering algorithm
    1. Introducing data mining Decision Tree
    2. Affiity Analysis Clustering
    1. Install nltk
    2. How NLTK works
    3. Word and Sentence Tokenizer
    4. Stop words with NLTK
    5. Stemming
    6. Lemmatizer
    7. TF_IDF
    8. Count Vectorizer
    1. Introducing matplotlib Bar Charts
    2. Introduction to SeaBorne
    3. Feature Scaling
    4. Line Charts Scatter plots
    1. Introduction to Open CV
    2. Loading and displaying images
    3. Applying image filters Tracking faces
    4. Face recognition
    1. Naïve Bayes
    2. Decision -Tree

Target Audience

  • who want to improve their programming skills by applying industry best practices in their daily work.
  • Individuals looking to learn Machine learning
  • Project Managers, Software architects and Development Team Leaders, who want to implement or improve a software development process within a project, and who want to define a project or company-wide set of recommended tools and best practices.
  • Project Managers, Software architects and Development Team Leaders, who want to implement or improve a software development process within a project, and who want to define a project or company-wide set of recommended tools and best practices.
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