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What is Machine Learning with Python and R training about?

With SpringPeople’s machine learning python course, you gain an in-depth understanding of the three major types of machine learning algorithms, including supervised, unsupervised, and reinforcement learning. 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.

With Cloudlabs, you will gain hands-on experience working with Python’s  functions and libraries for ML projects. With easy to follow step-by-step instructions, you will learn to implement all ML algorithms taught in this course using the popular Python programming language.

What are the objectives of Machine Learning with Python and R training?

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 preprocess 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 Machine Learning with Python and R training for?

  • Anyone looking to implement ML algorithms using Python
  • Teams getting started with or working on Python ML projects 

What are the prerequisites for Machine Learning with Python and R training?

  • Knowledge of Python programming and basic understanding of learning types are required.

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 Preprocessing
    • 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
    • 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.