Machine Learning A-Z™: Hands-On Python & R In Data Science [Updated]
What you will learn
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- Master Machine Learning on Python & R
- Have a great intuition of many Machine Learning models
- Make accurate predictions
- Make a powerful analysis
- Make robust Machine Learning models
- Create strong added value to your business
- Use Machine Learning for personal purpose
- Handle specific topics like Reinforcement Learning, NLP and Deep Learning
- Handle advanced techniques like Dimensionality Reduction
- Know which Machine Learning model to choose for each type of problem
- Build an army of powerful Machine Learning models and know how to combine them to solve any problem
Section 1: Welcome to the course!
Section 2: Data Preprocessing in Python
Section 3: Data Preprocessing in R
Section 4: Simple Linear Regression
Section 5: Multiple Linear Regression
Section 6: Polynomial Regression
Section 7: Support Vector Regression (SVR)
Section 8: Decision Tree Regression
Section 9: Random Forest Regression
Section 10: Evaluating Regression Models Performance
Section 11: Regression Model Selection in Python
Section 12: Regression Model Selection in R
Section 13: Logistic Regression
Section 14: K-Nearest Neighbors (K-NN)
Section 15: Support Vector Machine (SVM)
Section 16: Kernel SVM
Section 17: Naive Bayes
Section 18: Decision Tree Classification
Section 19: Random Forest Classification
Section 20: Classification Model Selection in Python
Section 21: Evaluating Classification Models Performance
Section 22: K-Means Clustering
Section 23: Hierarchical Clustering
Section 24: Apriori
Section 25: Eclat
Section 26: Upper Confidence Bound (UCB)
Section 27: Thompson Sampling
Section 28: -------------------- Part 7: Natural Language Processing --------------------
Section 29: -------------------- Part 8: Deep Learning --------------------
Section 30: Artificial Neural Networks
Section 31: Convolutional Neural Networks
Section 32: Principal Component Analysis (PCA)
Section 33: Linear Discriminant Analysis (LDA)
Section 34: Kernel PCA
Section 35: Model Selection
Section 36: XGBoost
Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.
- Just some high school mathematics level.
Interested in the field of Machine Learning? Then this course is for you!
This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.
We will walk you 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 lucrative sub-field of Data Science.
This course is fun and exciting, but at the same time we 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 which 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 math and who want to start learning Machine Learning.
- 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 people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Machine Learning tools.