Machine Learning A to Z-Hands-On Python & R In Data Science with Passion
Learn to create Machine Learning Algorithms in Python and R from Data Science experts. Code templates included.
What you'll learn
- Master Machine Learning on Python & R
- Make accurate predictions
- Have a great intuition of many Machine Learning models
- Make robust Machine Learning models
- Make powerful analysis
- Use Machine Learning for personal purpose
- Create strong added value to your business
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Know which Machine Learning model to choose for each type of problem
- Handle advanced techniques like Dimensionality Reduction
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem
Checkout this course preview here: https://www.udemy.com/course/machinelearning (Copy and paste this link to your URL address bar to open)
Some knowledge about high school mathematics.
Are you one of the interested guy in the field of Machine Learning? Then this course is for you only!
This course has been designed by two professional Data Scientists. It will help you to learn complex theory, algorithms, and coding libraries in a simple way.
You will walk through step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet beneficial subfield of Data Science.
This course is fun and exciting, but at the same time, you dive deep into Machine Learning. It is structured the following way:
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
Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.
Who this course is for:
- Anyone interested in Machine Learning.
- Students who have at least high school knowledge in maths and who want to start learning Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
- Any data analysts who want to level up in Machine Learning.
- Any students in college who want to start a career in Data Science.
- Any people who want to create added value to their business by using powerful Machine Learning tools.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
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