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IBM Free Courses and Certificates

January 23, 2024
Narendra

Python For Datascience :

This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours.

Upon its completion, you’ll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this course is for you.

COURSE SYLLABUS

Module 1 – Python Basics
  • Your first program
  • Types
  • Expressions and Variables
  • String Operations
Module 2 – Python Data Structures
  • Lists and Tuples
  • Sets
  • Dictionaries
Module 3 – Python Programming Fundamentals
  • Conditions and Branching
  • Loops
  • Functions
  • Objects and Classes
Module 4 – Working with Data in Python
  • Reading files with open
  • Writing files with open
  • Loading data with Pandas
  • Working with and Saving data with Pandas
Module 5 – Working with Numpy Arrays and Simple APIs
  • Numpy 1D Arrays
  • Numpy 2D Arrays
  • Simple APIs
  • API Setup

GENERAL INFORMATION

  • This course is self-paced.
  • It can be taken at any time.
  • It can be audited as many times as you wish.


    Python Free Course and Certificate  Link  :Click Here

    SQL Free Course :

About This Course

Data is one of the most critical assets of any business. Data needs a database to store and process data quickly. SQL is a language used for a database to query data.
In this introductory course, you’ll learn the basics of the SQL language and the relational databases. You’ll start by learning about the relational model and relational model concepts and constraints. By the end of this course, you will have learned and used the five basic SQL statements, some advanced SQL syntax, and join statements.

This isn’t your typical textbook introduction. You’re not just learning through lectures. At the end of each module there are assignments, hands-on exercises, review questions, and also a final exam. Successfully completing this course earns you a certificate. So let’s get started!

Course Syllabus

  • Module 1 -SQL and Relational Databases 101
    • Introduction to SQL and Relational Databases
    • Information and Data Models
    • Types of Relationships
    • Mapping Entities to Tables
    • Relational Model Concepts
  • Module 2 – Relational Model Constraints and Data Objects
    • Relational Model Constraints Introduction
    • Relational Model Constraints Advanced
  • Module 3 – Data Definition Language (DDL) and Data Manipulation Language (DML)
    • CREATE TABLE statement
    • INSERT statement
    • SELECT statement
    • UPDATE and DELETE statements
  • Module 4 – Advanced SQL
    • String Patterns, Ranges, and Sets
    • Sorting Result Sets
    • Grouping Result Sets
  • Module 5 – Working with multiple tables
    • Join Overview
    • Inner Join

Outer Join

SQL Free Course and Certificate : Click Here

Machine Learning :

About This Course

This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You’ll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.

Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
  • Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
  • Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.

Get ready to do more learning than your machine!

Course Syllabus

Module 1 – Supervised vs Unsupervised Learning
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning 
  • Supervised Learning Classification 
  • Unsupervised Learning 

Module 2 – Supervised Learning I

  • K-Nearest Neighbors 
  • Decision Trees 
  • Random Forests
  • Reliability of Random Forests 
  • Advantages & Disadvantages of Decision Trees 
 Module 3 – Supervised Learning II
  • Regression Algorithms 
  • Model Evaluation 
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models 
 Module 4 – Unsupervised Learning
  • K-Means Clustering plus Advantages & Disadvantages 
  • Hierarchical Clustering plus Advantages & Disadvantages 
  • Measuring the Distances Between Clusters – Single Linkage Clustering 
  • Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
  • Density-Based Clustering 

Module 5 – Dimensionality Reduction & Collaborative Filtering

  • Dimensionality Reduction: Feature Extraction & Selection 

Collaborative Filtering & Its Challenges

 

 

Machine Learning Free Course and certificate : Click Here

 

Datascience with Python

Data Analysis with Python

Data Analysis has always been a very important field and a highly demanded skill. Until recently, it has been practiced using mostly closed, expensive, and limited tools like Excel or Tableau. Python, pandas, and other open-source libraries have changed Data Analysis forever and have become must-have tools for anyone looking to build a career as a Data Analyst.

-Data visualization is the graphical representation of data in order to interactively and efficiently convey insights to clients, customers, and stakeholders in general. It is a way to summarize your findings and display it in a form that facilitates interpretation and can help in identifying patterns or trends.

Python for Data Science

This introduction to Python will kickstart your learning of Python for data science, as well as programming in general. This beginner-friendly Python course will take you from zero to programming in Python in a matter of hours. Upon its completion, you’ll be able to write your own Python scripts. If you want to learn Python from scratch, this course is for you.

Free Datascience with python : Click Here

 

LEARN TO ANALYZE DATA WITH PYTHON

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more!

You will learn how to:
  • Import data sets
  • Clean and prepare data for analysis
  • Manipulate pandas DataFrame
  • Summarize data
  • Build machine learning models using scikit-learn
  • Build data pipelines

Data Analysis with Python

is delivered through lectures, hands-on labs, and assignments. It includes the following parts:

  • Data Analysis libraries: will learn to use Pandas DataFrames, Numpy multi-dimensional arrays, and SciPy libraries to work with various datasets. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.
COURSE SYLLABUS
Module 1 – Importing Datasets
  • Learning Objectives
  • Understanding the Domain
  • Understanding the Dataset
  • Python package for data science
  • Importing and Exporting Data in Python
  • Basic Insights from Datasets

Module 2 – Cleaning and Preparing the Data
  • Identify and Handle Missing Values
  • Data Formatting
  • Data Normalization Sets
  • Binning
  • Indicator variables

Module 3 – Summarizing the Data Frame
Descriptive Statistics
  • Basic of Grouping
  • ANOVA
  • Correlation
  • More on Correlation


Module 4 – Model Development

Simple and Multiple Linear Regression

Model Evaluation Using Visualization

Polynomial Regression and Pipelines

R-squared and MSE for In-Sample Evaluation

Prediction and Decision Making


  • Module 5 – Model Evaluation
  • Model  Evaluation
  • Over-fitting, Under-fitting, and Model Selection
  • Ridge Regression
  • Grid Search
  • Model Refinement
  • GENERAL INFORMATION
  • This course is self-paced.
  • It can be taken at any time.
  • It can be audited as many times as you wish.
  • Python programming, Statistics
  • REQUIREMENTS
  • Some Python experience is expected
  • Python for Data Science

    Free Data Analysis course with Python :Click Here

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