Who is this course for?
- Anyone interested in Machine Learning.
- Those who know the basics of Machine Learning, including the classical algorithms like linear regression or logistic regression, but want explore all the different fields.
- Anyone who’s not too comfortable with coding would like to dive into Machine Learning to apply to datasets.
- Any data analysts who want to start on Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- College students who want to start a career in Data Science, who want to start learning about Machine Learning and have at least high school knowledge of maths.
What will you learn?
- How to Master Machine Learning on Python & R
How to make accurate predictions and create powerful analysis
- How to make robust Machine Learning models
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Handle advanced techniques like Dimensionality Reduction
- Learn which Machine Learning model to choose for each problem
- Create a log of powerful Machine Learning models and combine them to solve problems.
The course will walk you through the exciting world of Machine Learning and equip you with the new skills that can develop and improve your understanding of this challenging yet lucrative sub-field of Data Science. Before joining us, you only need to have a basic, high school level, understanding of Mathematics.
With the course, you will also be provided with downloadable templates of Python and R code that you can keep for your own projects and development.
Take a look at how we break the course down:
- Part 1 – Data Preprocessing
- Part 2 – Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Part 3 – Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Part 4 – Clustering: K-Means, Hierarchical Clustering
- Part 5 – Association Rule Learning: Apriori, Eclat
- Part 6 – Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
- Part 7 – Natural Language Processing: Bag-of-words model and algorithms for NLP
- Part 8 – Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
- Part 9 – Dimensionality Reduction: PCA, LDA, Kernel PCA
- Part 10 – Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost
Practical exercises are included to provide you with the opportunity to practice your newly learned skills on real life problems.
Take a look at some feedback…
“Great for beginners. I was confused at my real classroom course, this online version picked me up from scratch. Well done!”
“I simply love this course and I definitely learned a ton of new concepts.”
“Kirill and Haedlin are awesome instructors when it comes to machine learning … the way they break down complex topics into simple explanations is amazing!”
This comprehensive course comprises of 41 hours of learning and 285 lectures.
This course is delivered by Udemy.