Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. 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. Professionals handling projects in real time will assist students and fresher's to understand challenges and working scenario in the industry. Ducat is the Best Machine Learning in python 6 week training institute in noida.

Introduction to Machine learning

  • What is ML?
  • Types of ML
  • ML package :scikit-learn
  • Anaconda
  • How to install anaconda

Basic Introduction numpy and Pandas

  • Introduction to NumPy
  • Creating an array
  • Class and Attributes of ndarray
  • Basic Operations
  • Activity-Slice
  • Stack operations
  • Mathematical Functions of NumPy
  • Introduction to Pandas
  • Understanding DataFrame
  • Series
  • Concatenating and appending DataFrames
  • loc and iloc
  • Drop columns or rows
  • Groupby
  • Map and apply

Data Preprocessing

  • Introduction
  • Dealing with missing data
  • Handling categorical data
    • Encoding class labels
    • One-hot-encoding
  • Split data into training and testing sets
  • Bringing Features onto same scale

Regression

  • Introduction
  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial Regression
  • Evaluate Performance of a linear regression model
  • Overfitting and underfitting

K-Nearest Neighbors(KNN)

  • KNN theory
  • Implementing KNN with scikit-learn
  • KNN Parameters
    • n_neighbors
    • metric
  • How to find Nearest Neighbors
  • Writing Own KNN classifier from scratch

Logistic Regression

  • Logistic Regression theory
  • Implementing Logistic regression with scikit-learn
  • Logistic Regression Parameters
  • Multi-class classification
  • MNIST digit dataset with Logistic Regression
  • Predictive modeling on adult income dataset

Support Vector Machine(SVM)

  • SVM theory
  • Implementing SVM with scikit-learn
  • SVM Parameters:
    • C and gamma
  • Plot hyperplane for linear classification
  • Decision function

Decision Tree and Random Forest

  • Theory behind decision tree
  • Implementing decision tree with scikit-learn
  • Decision tree parameters
  • Combining multiple decision trees via Random forest
  • How random forest works..?

Naīve Bayes Classification

  • Theory Naive Bayes Algorithm
  • Features extraction
    • Countvectorizer
    • TF-IDF
  • Text Classification

Model Evaluation and Parameter Tuning

  • Cross validation via K-Fold
  • Tuning hyperparameters via grid search
  • Confusion matrix
  • Recall and Precision
  • ROC and AUC

Clustering and Dimension Reduction

  • K-means Clustering
  • Elbow method
  • Principal components analysis(PCA)
  • PCA step by step
  • Implementing PCA with scikit-learn
  • LDA with scikit-learn
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