Data Science and Machine Learning 2023 : Basic to Advanced

Data Science and Machine Learning 2023 Basic to Advanced : Complete Introduction to Data Science and Machine Learning from Basic to Advanced.

What you will learn ?

• Students will develop understanding of libraries used for Data Analysis like Pandas and Numpy.
• Learn to create impactful visualizations using Matplotlib and Seaborn. By creating these visualizations you will be able to derive better conclusions from data.
• After this course you will learn to build complete Data Science Pipeline from Data preparation to building the best Machine Learning Model.
• The course contains practical section after every new concept discussed and the course also has two projects at the end.

Data Science and Machine Learning 2023 Course Includes

• 5 hours on-demand video
• 48 downloadable resources
• Access on mobile and TV
• Full lifetime access
• Certificate of completion

Requirements

• Basic understanding of Python Programming Language.

Description

In this comprehensive course, you will acquire a strong grasp of essential data analysis tools and techniques using NumPy and Pandas. These foundational skills will empower you to explore, manipulate, and derive insights from your data effectively.

You will also dive into the art of crafting impactful visualizations with Matplotlib and Seaborn. Creating compelling visuals is pivotal in gaining a deeper understanding of your datasets and conveying insights effectively.

Throughout the course, you’ll master key data preprocessing steps, including handling missing values, feature encoding, and feature scaling. These crucial processes lay the groundwork for cleaner, more reliable data analysis.

Delve into the world of machine learning as you familiarize yourself with various algorithms such as Random Forest, Decision Trees, KNN, SVM, Linear Regression, and Logistic Regression. Each algorithm will be thoroughly explained, with practical implementations to solidify your understanding.

Moreover, discover the art of optimizing your machine learning models by learning how to choose the best hyperparameters through GridSearch CV. This essential skill can significantly enhance your model’s accuracy and performance.

One of the course highlights is the creation of a complete machine learning pipeline, covering the entire journey from data collection to data preprocessing to model building. Understanding the ML pipeline is invaluable for tackling large-scale ML projects effectively.

The course culminates with two hands-on projects that integrate all the concepts you’ve learned. The first project revolves around Diabetes Prediction using a classification machine learning algorithm, while the second project centers on predicting insurance premiums using a regression machine learning algorithm.

By the end of this course, you will be equipped with a comprehensive skill set to excel in data analysis, visualization, and machine learning, making you proficient in tackling real-world data challenges.

Who this course is for:

• Anyone who is looking to start his or her Data Science and Machine Learning Journey. People who are at intermediate level and already have some basic understanding of Data Science will also find this course helpful.

Frequently Asked Questions

1. What are the prerequisites for taking this course on data analysis and machine learning?

• Generally, a basic understanding of Python programming is recommended. Familiarity with concepts like variables, data types, and control structures will be helpful. Prior exposure to data analysis or machine learning is not required but can be advantageous.

2. What tools and libraries will be used in this course?

• This course primarily utilizes Python for programming. The key libraries covered include NumPy and Pandas for data manipulation, Matplotlib and Seaborn for data visualization, and scikit-learn for implementing machine learning algorithms. Jupyter Notebook is often used for an interactive coding environment.

3. How will this course help me in my data analysis and machine learning journey?

• This course provides a comprehensive foundation in data analysis, data preprocessing, and machine learning. You’ll learn essential concepts, techniques, and best practices to work with real-world datasets and develop predictive models. By the end of the course, you’ll be equipped to tackle data analysis and machine learning projects effectively.

4. Can you explain the two projects mentioned in the course?

• Certainly! The first project focuses on “Diabetes Prediction” using a classification machine learning algorithm. You’ll work with a dataset related to diabetes patients and apply classification models to predict whether a person has diabetes or not. The second project is about “Predicting Insurance Premiums” using regression machine learning. Here, you’ll build models to estimate insurance premiums based on various factors, which can be valuable for insurance companies.

5. What is GridSearch CV, and why is it important in machine learning?

• GridSearch CV (Cross-Validation) is a technique used to systematically search for the best combination of hyperparameters for a machine learning model. It helps in optimizing model performance by finding the hyperparameters that yield the highest accuracy or lowest error. GridSearch CV is crucial as it ensures that your machine learning model is fine-tuned for optimal results and prevents overfitting or underfitting.

Price & Validity

Actual Price : Rs.2799/-
After 100% Discount : Rs.0/-

Valid for First 1000 Users or till the last date. Hurry up before it closes

Note : Udemy Courses listed here are offered FREE only for first 1000 users or are limited by a date. If the 1000 users limit or last date is completed, the course becomes paid.