Applied Machine Learning With Python
Learn the fundamentals of Machine Learning with this comprehensive course. Developed by professional Data Scientists, it covers regression, classification, clustering, deep learning, and more. Gain practical experience through hands-on exercises and access Python and R code templates. Suitable for beginners and working professionals. Updated with the latest codes and includes top gradient boosting models. Start your journey into the world of Machine Learning today!
What you’ll learn
- Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
- Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
- Clustering: K-Means, Hierarchical Clustering
- Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
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 theories, 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 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.
Important updates (June 2020):
CODES ALL UP TO DATE
DEEP LEARNING CODED IN TENSORFLOW 2.0
TOP GRADIENT BOOSTING MODELS INCLUDING XGBOOST AND EVEN CATBOOST!
Who this course is for:
- Just some high school mathematics level and Working professionals also
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