Learn how to develop Machine learning course Algorithms in Python and R from two Data Science Specialists. This includes coding Templates.
What you’ll learn
- Learn Python and R Machine Learning.
- Have a strong understanding of numerous Machine Learning models.
- Make precise projections
- Conduct thorough research
- Create strong Machine Learning models.
- Create significant added value for your company.
- Use Machine learning course for your own benefit.
- Handle specialized subjects such as Reinforcement Learning, NLP, and Deep Learning.
- Master advanced techniques such as Dimensionality Reduction.
- Understand the Machine Learning model to use for each problem category.
- Create a formidable army of Machine Learning models and understand how to combine them to tackle any problem.
Description
Are you interested in Machine learning course? Then this is the course for you!
This course was created by two expert Data Scientists to share knowledge and assist you in learning difficult theory, algorithms, and coding libraries in it’s original way.
We will guide you through the world of machine learning step by step. With each session, you will learn new abilities and gain a better grasp of this difficult but lucrative topic of Data Science.
This Machine learning course is entertaining and thrilling, but it also delves deeply into Machine Learning. They are the following:
- Data Processing
- 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 Techniques: K-Means and Hierarchical Clustering
- Learning Association Rules: Apriori, Eclat
- Reinforcement Learning: Upper Confidence Bound, Thompson Sampling.
- Natural Language Processing: The Bag-of-Words Model and NLP Algorithms
- Deep Learning: Artificial Neural Networks and Convolutional Neural Networks
- Dimensionality Reduction Techniques: PCA, LDA, and Kernel PCA
- Model Selection and Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost.
Furthermore, the Machine learning course is jam-packed with practical activities based on real-life experiences. So you’ll not only learn the theory, but you’ll also get some hands-on experience developing your own models.
This Machine learning course also provides Python and R code templates that you can download and use on your own projects as a bonus.
Important revisions (June 2020):
- ALL CODES ARE CURRENT
- TENSORFLOW 2.0 CODED DEEP LEARNING
- XGBOOST AND CATBOOST ARE TWO OF THE BEST GRADIENT BOOSTING MODELS!
This program includes:
44 hours of video on demand
38 articles.
Eight downloadable resources.
Complete lifetime access
Mobile and television access
Completion certificate
Requirements
- Just some high school mathematics level.
Who this course is for:
- Anyone with a passion in Machine Learning.
- Students with at least a high school level of arithmetic expertise who wish to begin learning Machine Learning.
- Any intermediate-level individuals who are familiar with the fundamentals of machine learning, including traditional techniques such as linear regression or logistic regression, but wish to learn more about it and explore all of its various fields.
- Anyone who is not very familiar with coding but is interested in Machine Learning and wants to easily apply it to datasets.
- Any college students interested in pursuing a career in data science.
- Any data analysts looking to advance in Machine Learning.
- Anyone who is dissatisfied with their current employment and wishes to become a Data Scientist.
- Anyone who wants to add value to their business by leveraging strong Machine Learning techniques.
