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Machine Learning using Python : Learn Hands-On

Features Includes:
  • Self-paced with Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App

Course Preview Video

  • Categories

    IT & Software Systems

  • Duration

    07:07:27

  • 4 Students Enrolled

Description

Learn to use Python, the ideal programming language for Machine Learning, with this comprehensive course from Hands-On System. Python plays a important role in the adoption of Machine Learning (ML) in the business environment.

Now a day’s Machine Learning is one of the most sought after skills in industry. After completion of this course students will understand and apply the concepts of machine learning and applied statistics for real world problems.

The topics we will be covering in this course are: Python libraries for data manipulation and visualization such as numpy, matplotlib and pandas. Linear Algebra, Exploratory Data Analysis, Linear Regression, Various Classification techniques, Clustering, Dimensionality reduction and Artificial Neural Networks.

This course is designed for Students who are pursuing bachelor’s or master’s degree in Statistics, Mathematics, Computer Science, Economics or any engineering fields. The students should have a little bit of knowledge in coding and undergraduate level mathematics.

Terminal competencies of the course, one would have learnt about tools to train machines based on real-world situations using Machine Learning algorithms, as well as to create complex algorithms and neural networks. During the latter stage of the course, learners will be introduced to real-world use cases of Machine Learning with Python for a Hands-On learning experience which would prepare them to create applications efficiently.


Basic knowledge
  • Basic knowledge in math and statistics

What will you learn
  • Linear Regression, SVR, Decision Tree Regression, Random Forest Regression
  • Polynomial Regression
  • Logistic Regression
  • K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification
  • Random Forest Classification
  • Clustering: K-Means, Hierarchical Clustering
  • Data Visualization in Python with MatPlotLib and Seaborn
  • Dimensionality Reduction: PCA, PCA sklearn
  • Supervised Learning & Unsupervised Learning
  • Support Vector Machine
  • Curse of Dimensionality
  • Neural Networks
Course Curriculum
Number of Lectures: 48 Total Duration: 07:07:27
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