<Python with Machine learning TRAINING Institutes In Noida, Ghaziabad, Gurgaon, Faridabad, Greater Noida

Python with Machine learning Training

Python is mainly stated as high-level, general-purpose programming language, which emphasizes code readability. The syntax helps the programmers to express their concepts in few general "lines of code" when compared with other promising languages, like Java or C++. Through our courses, you can easily construct clear programs, on both large and small scales.As the importance of Python programming language is gaining huge popularity, therefore; the need to understand and know the language is increasing among people. When you think about Python training, you must look for an Ducat expert help.

Python Basics

  • Introduction
  • basic syntax
  • Data types
  • if..else and for & while loop
  • functions
  • Modules and packages
  • file handling
  • Class and object
  • Regular expression

Introduction to Machine learning

  • what is ML..?
  • Examples on ML
  • ML package :scikit-learn
  • Anaconda
  • Types of ML
  • Some basic steps of ML

Data Preprocessing

  • Dealing with missing data
  • Identifying missing values
  • Imputing missing values
  • Drop samples with missing values
  • Handling categorical data
  • Nominal and Ordinal features
  • Encoding class labels
  • One-hot-encoding
  • Split data into training and testing sets
  • feature scaling

Machine learning classifiers

  • K-Nearest Neighbors(KNN)
  • Logistic regression
  • Support vector machines
  • Decision tree
  • Combining multiple decision tree via Random forest

Model Evaluation and Hyperparameter Tuning

  • Cross validation via K-Fold
  • Overfitting and underfitting
  • Tuning hyperparameters via grid search
  • Confusion matrix
  • Precision and Recall
  • ROC and AUC
  • Scoring metrics for multiclass classification

Predicting Continuous Target Variables:Regression

  • Introducing linear regression
  • Simple linear regression
  • Multiple linear regression
  • Turning a linear regression model into a curve:
  • Polynomial Regression
  • Evaluating performance of linear regression model
  • Dealing with non-linear relationship:
  • Random forest regression
  • Decsion tree regression
  • Predicting prices for housing dataset

Clustering and dimension Reduction

  • K-means clustering
  • Using the elbow method to find the optimal number of clusters
  • Principal component analysis(PCA)
  • Main steps behind PCA
  • Exracting the principal components step by step
  • Implementing PCA using scikit-learn
  • Supervised data compression via linear discriminant analysis
  • LDA via scikit-learn

Neural Networks

  • Definition of an artificial neural network
  • Perceptron
  • Minimizing cost function with Gradient descent
  • Classifying Handwritten digits with
  • Multilayer Perceptron

Working with opencv

  • Installing opencv
  • Reading and writing images
  • Writing text on images
  • Image Manipulations
  • face detection
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