Machine Learning with Python

Machine Learning with Python Overview

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The Machine Learning with Python course focuses on giving your team a head start and a practical approach to building deployable machine learning models by providing an in-depth understanding of the three major types of machine learning algorithms, supervised, unsupervised, and reinforcement learning, using the most widely used programming language. Learn about the various techniques for implementing these algorithms, as well as the commercial use cases that go along with them.

Machine Learning with Python Objective

  • Recognize the range and depth of machine learning (ML) applications and use cases in real-world circumstances.
  • Use Python libraries to import and manage data, then divide it into training and test datasets.
  • Techniques for data preprocessing, univariate and multivariate analysis, missing values and outlier management, and so on.
  • Apply linear and polynomial regression, learn about ridge and lasso regression, and more.
  • Implement SVM, Naive Bayes, Decision Trees, and Random Forests among other classification methods.
  • Unsupervised learning is interpreted, and clustering algorithms are learned.
  • Overfitting avoidance, Bias-variance tradeoff, Minibatch, and Shuffling, ML solution tuning
  • Perceptron, Neural Networks, Basics of Neural Networks

Machine Learning with Python Audience

  • Data Analyst who want to gain expertise in Predictive Analytics
  • Developers
  • Data Architects
  • Tech Leads handling a team of Analysts

Machine Learning with Python Prerequisites

Knowledge with Python programming and data analysis foundations is necessary.
It is beneficial to have a basic understanding of statistics and maths.

Machine Learning with Python Outline

  • What is ML?
  • Applications of ML
  • Why ML?
  • Uses of ML
  • Machine learning methods
  • Machine learning algorithms(Regression, Classification, Clustering, Association)
  • A brief introduction python libraries
  • Types of ML algorithms
  • Labelled Dataset
  • Training and Testing Data
  • Importing the Libraries
  • Importing the Dataset
  • Demo: Creating a machine model
  • What is data?
  • What is information?
  • Analyzing data to fetch the information
  • Entropy, Information gain
  • Data exploration and preparation
  • Univariate, bivariate, and multivariate analysis
  • Correlation
  • Chi-Square, Z-test, T-test, ANOVA
  • Categorical Data
  • Feature Scaling
  • Dimensionality Reduction
  • outliers
  • What is regression?
  • Applications of regression
  • Types of regression
  • Fitting the regression line
  • Simple linear regression
  • Simple linear regression in python
  • Polynomial regression
  • Polynomial regression in python
  • Gradiant Descent
  • Cost function
  • Regularization
  • Demo: Perform regression on a real world dataset
  • Ridge and lasso Regression

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