Machine Learning Essentials - Master core ML concepts

• Jumpstart the world of AI & ML
• Maths of Machine Learning
• Regression & Classification Techniques
• Linear & Logistic Regression
• K-Nearest Neighbours, K-Means
• Naive Bayes, Text Classification
• Decision Trees & Random Forests
• Ensemble Learning - Bagging & Boosting
• Dimensionality Reduction
• Neural Networks
• 8+ Hands on Projects

Read to jumpstart the world of Machine Learning &Artificial intelligence?

This hands-on course is designed for absolute beginners as well as for proficient programmers who want kickstart Machine Learning for solving real life problems. You will learn how to work with data, and train models capable of making "intelligent decisions"

Data Science has one of the most rewarding jobs of the 21st century and fortune-500 tech companies are spending heavily on data scientists! Data Science as a career is very rewarding and offers one of the highest salaries in the world. Unlike other courses, which cover only library-implementations this course is designed to give you a solid foundation in Machine Learning by covering maths and implementation from scratch in Python for most statistical techniques.

This comprehensive course is taught by Prateek Narang &Mohit Uniyal, who not just popular instructors but also have worked in Software Engineering and Data Science domains with companies like Google. They have taught thousands of students in several online and in-person courses over last 3+ years.

We are providing you this course to you at a fraction of its original cost! This is action oriented course, we not just delve into theory but focus on the practical aspects by building 8+ projects.

With over 170+ high quality video lectures, easy to understand explanations and complete code repository this is one of the most detailed and robust course for learning data science.

Some of the topics that you will learn in this course.

• Logistic Regression

• Linear Regression

• Principal Component Analysis

• Naive Bayes

• Decision Trees

• Bagging and Boosting

• K-NN

• K-Means

• Neural Networks
  
  
  Some of the concepts that you will learn in this course.
  

  • Convex Optimisation

  • Overfitting vs Underfitting

  • Bias Variance Tradeoff

  • Performance Metrics

  • Data Pre-processing

  • Feature Engineering

  • Working with numeric data, images & textual data

  • Parametric vs Non-Parametric Techniques
    

Sign up for the course and take your first step towards becoming a machine learning engineer! See you in the course!