Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Language
English
Taught by
Mohit Uniyal, Prateek Narang Sr. Software Engineer Google
Rating
7 views
Course Overview
Kickstart Machine Learning, understand maths behind essential algorithms, implement them in python & build 8+ 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!
Course Content
- 10 section(s)
- 198 lecture(s)
- Section 1 Introduction
- Section 2 Supervised vs Unsupervised Learning
- Section 3 Linear Regression
- Section 4 Linear Regression - Multiple Features
- Section 5 Logistic Regression
- Section 6 Dimensionality Reduction/ Feature Selection
- Section 7 Principal Component Analysis (PCA)
- Section 8 K-Nearest Neigbours
- Section 9 PROJECT - Face Recognition
- Section 10 K-Means
What You’ll Learn
- 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
Reviews
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EElvin Kurbanov
Great course for beginners
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LLearner LL
Clearly he doesn't have the experience he claims. The trainer is mostly self-learned which is fine but I don't appreciate how he is passing off the techniques as industry practices. I tried but had to give up quickly, it was going nowhere.
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PPushpjeet Cholkar
I am looking such explaination only, pure maths, with pure coding. Well explained
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AAbhishek Sinha
Really good explanation using mathematical equations. You know what equations are actually functioning behind those libraries consumed for machine learning.
