Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
8 Hour(s) 7 Minute(s)
Language
English
Taught by
Sanjay Singh
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Machine Learning & Data Science in Python with real life projects. Source codes included.
Join the most comprehensive Machine Learning Hands-on Course, because now is the time to get started!
From basic concepts about Python Programming, Supervised Machine Learning, Unsupervised Machine Learning to Reinforcement Machine Learning, Natural Language Processing (NLP), this course covers all you need to know to become a successful Machine Learning Professional!
But that's not all! Along with covering all the steps of Machine Learning functions, this course also has quizzes and projects, which allow you to practice the things learned throughout the course!
You'll not only learn about the concepts but also practice each of those concepts through hands-on and real-life Projects.
And if you do get stuck, you benefit from extremely fast and friendly support - both via direct messaging or discussion. You have my word!
With more than two decades of IT experience, I have designed this course for students and professionals who wish to master how to develop and support industry-standard Machine learning projects.
This course will be kept up-to-date to ensure you don't miss out on any changes once Machine Learning is required in your project!
Why Machine Learning?
In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According to available data, Machine Learning Engineer Is The Best Job of 2019 with a 344% growth and an average base salary of $146,085 per year.
If you are looking for a thriving career in Data Analytics, Artificial Intelligence, Robotics, this is the right time to learn Machine Learning.
Don't be left out and prepare well for these opportunities.
So, what are you waiting for?
Pay once, benefit a lifetime! This is an evolving course! Machine Learning and future enhancements will be covered in this course. You won’t lose out on anything! Don’t lose any time, gain an edge, and start now!
Course Content
- 21 section(s)
- 75 lecture(s)
- Section 1 Introduction
- Section 2 Basic Math and Statistics
- Section 3 Prerequisite Tools
- Section 4 Python Programming
- Section 5 Data Pre-processing
- Section 6 Part 1: Supervised Learning -> Regression
- Section 7 Simple Linear Regression
- Section 8 Multiple Linear Regression (MLR)
- Section 9 Polynomial Linear Regression (PLR)
- Section 10 K-Nearest Neighbors (KNN)
- Section 11 Advanced Regression Techniques
- Section 12 Part 2: Supervised Learning -> Classification
- Section 13 KNN Classifier
- Section 14 Logistic Regression
- Section 15 Support Vector Machine (SVM)
- Section 16 Naive Bayes
- Section 17 Decision Tree
- Section 18 Ensemble Learning
- Section 19 K-Fold Validation
- Section 20 Model Deployment
- Section 21 Bonus Lectures
What You’ll Learn
- Hands-on explanation of every major Machine Learning techniques.
- Model Development, Deployment and Monitoring.
- Regression: Simple, Polynomial, and Multinomial
- Classification: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes
- Ensemble Modeling: Voting Classifier, Bagging, Boosting, Stacking, Random Forest
- Implementation of every concepts explained in the course. Source codes are made available to you for your use.
- Data Visualization with MatPlotLib and Seaborn
- Use train, test and Cross Validation to choose and tune data
- Feature Engineering (Reduce Noise, Outliers) and Data Preprocessing
- Practical examples of How to trade-off between Bias, Variance, Irreducible errors using Ensemble Learning model and Bagging, Boosting
- Understand how to implement Machine Learning at massive scale
- Understand math and statistics behind Machine Learning models
Skills covered in this course
Reviews
-
IIkponmwosa Enorense
Very informative. Pace was great.
-
SShashank c
it's good and neetly explained
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NNayan Sharma
It is overall a really good course. Would be much better if some exerciseing could be given for practice with the solution.
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AAnkit Guha
Absolutely loved this course! Covering so many topics in such a short duration with hands on tutorials is extremely commendable. Thank you so much sir!
