Machine Learning with Python Training

Live Online & Classroom Enterprise Training

Gain a deep understanding of some of the most widely used ML algorithms and their implementation in our machine learning with python training. Become an expert in creating smart applications for your organization with hands-on training in Python for ML projects.

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Key Features
  • Lifetime Access

  • CloudLabs

  • 24x7 Support

  • Real-time code analysis and feedback

  • 100% Money Back Guarantee

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What is ML with Python Course about?

SpringPeople’s Machine Learning with Python course offers an in-depth understanding of the three major types of machine learning algorithms, comprising of supervised, unsupervised, and reinforcement learning using the most widely used programming language .

Learn the various methods for implementing these algorithms with associated business use cases.

You will also learn advanced topics of ML such as Natural Language Processing (NLP) and Deep Learning in our machine learning with python training.

What are the objectives of ML with Python Course ?

At the end of this Machine Learning with Python Certification course, you will be able to: 

  • Appreciate the breadth & depth of ML applications and use cases in real-world scenarios.
  • Import and wrangle data using Python libraries and divide them to training and test datasets
  • Implement various type of regression methods including SVR, decision tree and random forest
  • Deploy different types of classification algorithms
  • Use clustering algorithms with Python libraries 
  • Deploy association rule learning, and reinforcement learning
  • Implement Natural language processing and other deep learning methods in your application
Available Training Modes

Live Online Training

18 Hours

Classroom Training


3 Days

Who is ML with Python Course for?

  • Anyone looking to implement ML algorithms using Python 
  • Data Architects / Developers who wants to gain expertise in Predictive Analytics
  • Teams getting started with or working on Python ML projects

What are the prerequisites for ML with Python Course?

Course Outline

  • Introduction to Machine Learning
    • What is ML?
    • Applications of ML
    • Why ML is the Future
    • Types of ML
    • Installing Python and Anaconda (MAC & Windows)
  • Data Wrangling
    • Importing the Libraries
    • Importing the Dataset
    • For Python learners, summary of Object-oriented programming: classes & objects
    • Missing Data
    • Categorical Data
    • Splitting the Dataset into the Training set and Test set
    • Feature Scaling
  • Regression
    • Simple Linear Regression
    • Dataset + Business Problem Description
    • Simple Linear Regression in Python
    • Multiple Linear Regression
    • Multiple Linear Regression in Python
    • Polynomial Regression
    • Polynomial Regression in Python
    • Support Vector Regression (SVR)
    • SVR in Python
    • Decision Tree Regression in Python
    • Random Forest Regression in Python
  • Classification Models
    • Logistic Regression in Python
    • K-Nearest Neighbors (K-NN)
    • Support Vector Machine (SVM)
    • Kernel SVM
    • Naive Bayes
    • Decision Tree Classification
    • Random Forest Classification
    • Confusion Matrix
    • CAP Curve
  • Clustering
    • K-Means Clustering in Python
    • Hierarchical Clustering in Python
  • Association Rule Learning
    • Association Rule Learning in Python
    • Apriori
  • Reinforcement Learning
    • Upper Confidence Bound (UCB)
    • Thompson Sampling
  • Natural Language Processing
    • Natural Language Processing in Python
  • Deep Learning
    • Artificial Neural Networks in Python
    • Convolutional Neural Networks in Python

Who is the instructor for this training?

The trainer for this course has extensive experience in data analysis, programming, and ML, including years of experience mentoring professionals in machine learning Python courses.