Machine Learning And Deep Learning with Python From Scratch

Learn and download free complete course of Machine Learning And Deep Learning with Python From Scratch. In this course you will learn about deep learning, machine learning, python and Scratch. This course is very useful for everyone who want to learn python and want to understand machine learning. This course is very useful for programmers and researcher’s. Machine learning is actually study of algorithms of computer. Generally Artificial Intelligence is an application that use to access data and give systems ability to do work automatically like humans. We all know that python is a high level programming language. Python is very popular language in all the computer languages and you can easily understand it.¬† Syntax of python is very simple. With the help of python language you can develop software and applications. In this course you will learn how to use python from Scratch.

In this book you will learn about the abilities of computer that how computer learn from Data. You will learn about different types of machine learning. In this course you will learn how to solve interactive problems with reinforcement learning. You will learn how to discover hidden structures with unsupervised learning

You Cover These Topics From This course:

  • Training Simple Machine Learning Algorithms for Classification
  • Artificial neurons
  • The formal definition of an artificial neuron
  • Implementing a perceptron learning algorithm in Python
  • Adaptive linear neurons and the convergence of learning
  • Implementing Adaline in Python
  • Improving gradient descent through feature scaling
  • A Tour of Machine Learning Classifiers Using scikit-learn
  • Modeling class probabilities via logistic regression
  • Training a logistic regression model with scikit-learn
  • Tackling overfitting via regularization
  • Maximum margin intuition
  • Decision tree learning
  • Building a decision tree
  • Combining multiple decision trees via random forests
  • K-nearest neighbors
  • Building Good Training Sets
  • Handling categorical data
  • Nominal and ordinal features
  • Compressing Data via Dimensionality Reduction
  • The main steps behind principal component analysis
  • Kernel functions and the kernel trick
  • Kernel principal component analysis in scikit-learnLearning Best Practices for
  • Model Evaluation and
  • Hyperparameter Tuning
  • K-fold cross-validation
  • Reading a confusion matrix
  • Combining Different Models for Ensemble Learning
  • Learning with ensembles
  • Bootstrap samples
  • Applying Machine Learning to Sentiment Analysis
  • Decomposing text documents with LDA
  • LDA with scikit-learn
  • Embedding a Machine Learning Model into a
  • Web Application
  • Serializing fitted scikit-learn estimators
  • Adding style via CSS
  • Creating the result page
  • Creating a PythonAnywhere account
  • Predicting Continuous Target Variables with Regression Analysis
  • Exploring the Housing dataset
  • Loading the Housing dataset into a data frame


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