Overview Course

Transformational advancements in technology in today’s world are making it possible for data scientists to develop machines that think for themselves. Based on complex algorithms that can glean information from data, today’s computers can use neural networks to mimic human brains, and make informed decisions based on the most likely scenarios. The immense possibilities that machine learning can unlock are fascinating, and with data exploding across all fields, it appears that in the near future Machine Learning will be the only viable alternative simply because there is nothing quite like it!

Do you want to build systems that learn from experience? Or exploit data to create simple predictive models of the world? KnowledgeHut’s comprehensive course will help you go from basic to advanced concepts in Machine Learning using Python, the most popular language in the Data Science space. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each using Python libraries. You will work on real life case studies to get hands-on experience and learn Machine Learning techniques to build predictive models.

A career in Machine Learning is much sought after because it defines and shapes the future. Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey.

Learning Outcome

  • Statistical Learning
    • Understand the behavior of data as you build significant models
  • Python for Machine Learning
    • Learn about the various libraries offered by Python to manipulate, preprocess and visualize data
  • Fundamentals of Machine Learning
    • Learn about Supervised and Unsupervised Machine Learning and look into how statistical modelling relates to machine learning
  • Optimization Techniques
    • Learn to use optimization techniques to find the minimum error in your machine learning model
  • Machine Learning Algorithms
    • Learn various machine learning algorithms like KNN, Decision Trees, SVM, Clustering in detail and build model using them to implement in real life scenarios using Python libraries such as Scikit learn
  • Dimensionality Reduction
    • Learn the technique to reduce the number of variables using Feature Selection and Feature Extraction
  • Neural Networks
    • Understand Neural Network and apply them to classify image and perform sentiment analysis using CNN and RNN
  • Ensemble Learning
    • Learn to use multiple learning algorithms to obtain better predictive performance

Who should Attend?

This course is for you if:
  • You are interested in the field of machine learning and want to learn essential machine learning algorithms and implement them in real life business problem
  • You're a Software or Data Engineer interested in learning the fundamentals of quantitative analysis and machine learning

Eligibility Criteria

  • Elementary programming knowledge  
  • Familiarity with statistics
This course is endorsed under Critical Infocomm Technology Resource Programme Plus (CITREP+) Programme.
To find out more about CITREP+ Funding, please refer to Programme Support under CITREP+ page

Information is accurate as of 14 October 2019