Master Python and Machine Learning in Data Analysis and Get Certified

The foundations of Python and machine learning will be covered in this module. Additionally, you will discover how machine learning and Python are used in data analysis.

The fundamentals of Python programming will be covered here, beginning with variables, arithmetic operators, and comparison operators. Additionally, you will learn how to set up your development environment by installing Anaconda and using Jupyter Notebook, which will be your main tool for data analysis and coding.

The foundational ideas of applied mathematics and statistics, which are crucial for data analysis and machine learning, will be covered in this session. In order to help you comprehend how data is gathered, measured, and interpreted, you will start with a summary of essential statistical principles.

The basic building blocks of Python programming will be covered in this module, beginning with variables, which are data storage containers. Additionally, you will learn about arithmetic operators, which enable you to carry out fundamental mathematical operations.

This module will teach you about comparison operators, which are crucial for using Python to make logical conclusions.

What you’ll discover

Making named constraints and checks
Modifying tables: Adding, deleting, and renaming columns

Three key ideas in Python programming will be covered in this module: tuples, which are immutable (unchangeable) collections that store several things in a single variable, and indexing. and list, one of the most adaptable data structures in Python.

This module will teach you about looping, a basic Python concept that makes it possible to effectively automate repetitive processes.

This is where you will learn about NumPy (Numerical Python), a robust Python library for numerical computation.

This module will teach you about Pandas, one of the most potent Python packages for data analysis and manipulation. You can effectively handle structured data with Pandas’ user-friendly data structures, such as Series and DataFrames.

Here, you’ll examine Pandas’ statistical features, which make it simple to conduct in-depth data analysis. Like me, you’ll begin by learning how to compute descriptive statistics using Pandas functions.

One of the most popular Python data visualization packages, Matplotlib, will be covered in this module. Initially, you will learn how to install and import Matplotlib and comprehend its fundamental elements, including Figure, Axes, and Plot.

This lesson will introduce you to the field of AI-assisted programming, which uses automation, intelligent code recommendations, and debugging support to improve the development process.

You will use your data analysis and visualization abilities in this assignment to investigate connections between different HR-related elements inside a company.

In this project, you’ll analyze different modes of transport (MOT) for Liquefied Natural Gas (LNG) in Canada using data analytics and visualization techniques.

In this assignment, you will use data analytics and visualization approaches to compare the hazards of crude oil and highly volatile liquids (HVL).

You will master the fundamentals of machine learning (ML), a subfield of artificial intelligence that allows computers to learn from data and make predictions without explicit programming, in this module.

In this module, you’ll dive into regression algorithms, which are used to predict continuous numerical values based on input data. Regression is a fundamental concept in machine learning, commonly applied in fields like finance, marketing, and healthcare for tasks such as predicting sales, stock prices, and medical outcomes.

In this module, you’ll explore classification algorithms, a core component of machine learning used to predict categorical outcomes or labels. Classification is widely used in applications such as spam detection, image recognition, medical diagnosis, and more.

You will learn about dimensionality reduction in this lesson, which is an essential machine learning technique that keeps critical information while reducing the number of features (variables) in a dataset. This procedure aids in overfitting and multicollinearity mitigation, model simplification, and increased computational efficiency.

One of the most well-known and often utilized unsupervised machine learning methods, k-Means Clustering, will be covered in this session. A clustering algorithm called k-Means divides data into a predetermined number of clusters (k) according to how similar they are.

You will learn about Weka, a potent open-source data mining and machine learning software suite, in this subject.

You’ll learn more about the connection between artificial intelligence (AI) and machine learning (ML) in this subject, as well as how these technologies combine to produce intelligent systems that can solve challenging issues.

You will apply all of the knowledge you have gained throughout the course to a thorough capstone project in this last module. This project will test your ability to use AI, data analysis, and machine learning to address a real-world problem.