Machine learning with Python

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Course Overview

Xpertised Offers Advanced and Personalized Instructor Led Online Classroom training on Machine learning with Python which gives you the opportunity to interact with a Machine learning with Python instructor and help you enhance yourself to meet the demands of the industry.

Learn from our instructors from the convenience of your home or office. Interact and learn live with trainers and other participants. Implement various type of classification methods including SVM, Naive bayes, decision tree, and random forest Interpret Unsupervised learning and learn to use clustering algorithms Tuning of ML solutions, Bias-variance tradeoff, Minibatch, and Shuffling, Overfitting avoidance Basics of Neural Networks, Perceptron, MLP Data preprocessing techniques, Univariate and Multivariate analysis, Missing values and outlier treatment etc Implement linear and polynomial regression, understand Ridge and lasso Regression Appreciate the breadth & depth of ML applications and use cases in real-world scenarios. Import and wrangle data using Python libraries and divide them into training and test datasets

Course Content

Introduction to Machine Learning

  • 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

Creating a Machine Learning Model

  • Types of ML algorithms
  • Labelled Dataset
  • Training and Testing Data
  • Importing the Libraries
  • Importing the Dataset
  • Demo: Creating a machine model

Data Preparation and Exploration

  • 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


  • How is classification used?
  • Applications of classification
  • Logistic Regression, Sigmoid function
  • Decision tree
  • K-Nearest Neighbors (K-NN)
  • SVM
  • Naive Bayes
  • Understand limitations of linear classifer and evaluate abilities of non-linear classifiers using a data set

Evaluation of Classification Models

  • Confusion Matrix
  • Precision, Recall
  • F1-score
  • RoC, AuC
  • n-fold cross validation
  • Measuring classifier performance
  • Overfitting
  • Ensemble Learning
  • Bagging and Boosting

Unsupervised Learning - Clustering

  • Application of Unsupervised learning, examples, and applications
  • Clustering
  • Hierarchical Clustering in Python, Agglomerative and Divisive techniques
  • Measuring the distanvce between two clusters
  • k-means algorithm
  • Limitations of K-means clustering
  • SSE and Distortion measurements
  • Demo: Agglomerative Hierarchical clustering

Dimensionality Reduction

  • What is dimensionality reduction?
  • Applications of dimensionality reduction
  • Feature selection
  • Feature extraction
  • Dimensionality reduction via Principal component analysis
  • Eigenvalue and Eigenvectors
  • Hands on PCA on MNSIT data

Reinforcement Learning

  • What is reinforcement learning
  • Applications of reinforcement learning
  • An Example use case
  • Components of RL
  • Approachs to RL
  • RL algorithms
  • Deep reinforcement learning

Introduction to Natural Language Processing (NLP)

  • What is NLP?
  • Why NLP
  • Applications of NLP
  • Components of NLP
  • NLP techniques

Introduction to Deep Learning

  • Why deep learning?
  • Neural networks
  • Applications of neural networks
  • Biological Neuron vs Artificial Neuron
  • Artificial Neural networks, layers

Customer Reviews

Thanks to Xpertised and the tutor who walked me through all the topics with Practical exposure which is helping me in my current project.

Course was quite helpful in terms of understanding of concepts and practicality. Its really a very friendly environment to learn. The timing were mutually chosen, as we both are working professional. I am quite satisfied with the course.


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