Machine Learning with Python

Machine Learning with Python

Machine Learning is often an unbelievably useful tool to uncover hidden insights and predict future trends. This Machine Learning with Python course can provide you with all the tools you would like to get started with supervised and unsupervised learning.

Course Overview

Introduction– This machine learning certification course dives into the fundamentals of machine learning using Python, an approachable and well-known programming language. you will find out about supervised vs. unsupervised learning, look into however statistical modeling relates to machine learning, and do a comparison of every.

About Machine Learning with Python certification– Xcelit Pro's Machine Learning Certification training with Python can assist you gain experience in varied machine learning algorithms like regression, clustering, decision trees, random forest, Naïve bayes, and Q-Learning. This Machine Learning training also will assist you perceive the ideas of Statistics, time series, and completely different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms.Once you complete the Machine Learning with Python course, Xcelit Pro will provide you Data Scientist with Python certificate which has lifelong validity. – 

Key features

  • Automate data analysis using python
  • Describe Machine Learning
  • Work with real-time data
  • Learn tools and techniques for predictive modeling
  • Discuss Machine Learning algorithms and their implementation
  • Validate Machine Learning algorithms
  • Explain Time Series and it’s related concepts


  • Basics of Python
  • You must be comfortable with variables, linear equations, graphs of functions, histograms.
  • You should be a good programmer.

Course Syllabus

Machine Learning with Python Course Syllabus

  1. Statistical Learning

Understand the behavior of data as you build significant models

  1. Python for Machine Learning

Learn about the various libraries offered by Python to manipulate, preprocess and visualize data

  1. Fundamentals of Machine Learning

Learn about Supervised and Unsupervised Machine Learning

  1. Optimization Techniques

Learn to use optimization techniques to find the minimum error in your machine learning model

  1. Machine Learning Algorithms

Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail

  1. Building models

Build model using algorithms to implement in scenarios using Python libraries such as Scikit learn

  1. Dimensionality Reduction

Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction

  1. Neural Networks

Understand Neural Network, apply them to classify image and perform sentiment analysis

  1. Ensemble Learning

Learn to use multiple learning algorithms to obtain better predictive performance