Machine Learning A-Z: Hands-On Python & R In Data Science

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Machine Learning A-Z: Hands-On Python & R In Data Science. Machine learning (ML) is a branch of artificial intelligence that allows computers to learn from data without being explicitly programmed. ML algorithms are used to make predictions based on data. Python and R are two widely used programming languages for ML, due in part to their versatility and ease of use. This course will teach you how to use both Python and R to train machine learning models.

Machine Learning A-Z: Hands-On Python & R In Data Science

In this course, you will learn how to create machine learning algorithms in Python and R using code templates. You will also gain experience understanding how to interpret and visualize machine learning models. This course is designed for beginners who want to learn more about this exciting field.

You will Learn

  • Master Machine Learning on Python & R
  • Have a great intuition of many Machine Learning models
  • Make accurate predictions
  • Make 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

This course is designed for people who want to learn about machine learning and data analysis in a simple and easy-to-understand way. . You will start by learning the basics of machine learning using Python, and then move on to using R for more advanced techniques.

Best Machine Learning Course

Throughout the course, you will be introduced to various types of data and how they can be used in machine learning applications. The course covers the basics of machine learning, including how to choose the right algorithm and how to code it using libraries such as Python. We also cover supervised and unsupervised learning algorithms, as well as deep learning.

This course is Divided into 10 Parts:

  • 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

There are many reasons why people want to learn data science. Data science can be used in a variety of industries, such as finance, marketing, and health care. It can also be used for research purposes. Additionally, data science can help you learn more about how the world works. Finally, data science is a growing field that offers many opportunities for advancement. R is widely used in a variety of industries, including finance, healthcare, and science.

It’s also popular among data scientists and statisticians as it has a well-developed statistical computing package. Additionally, R is relatively easy to learn and use, making it an ideal language for data analysis projects. This course covers more details related to Data science and R.

This course covers everything from the basics of these subjects to more advanced techniques. You will learn how to create models using reinforcement learning, develop natural language processing skills using NLP techniques, and use deep learning to improve your predictions. This is the perfect course for anyone who wants to learn more about machine learning.

This Course is Good for

  • 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.
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