Introduction
IBM Career Education Program (CEP) is a comprehensive training program offered by IBM to help individuals gain the skills and knowledge required for in-demand careers in the technology industry. The program aims to bridge the gap between industry requirements and the skills possessed by job seekers, providing them with the necessary training to succeed in the rapidly evolving tech landscape.
Key features of the IBM Career Education Program include::
- Industry-Aligned Curriculum: The program offers industry-aligned training in various technology domains such as artificial intelligence (AI), cloud computing, cybersecurity, data science, blockchain, and more. The curriculum is designed to address the skill gaps identified by IBM and industry experts.
- Hands-On Learning: The program emphasizes hands-on learning through practical exercises, real-world projects, and industry case studies. Participants gain practical experience by working on IBM technologies and platforms, preparing them for the demands of the job market.
- Blended Learning Approach: The program combines online self-paced modules, instructor-led sessions, virtual labs, and collaborative learning environments to provide a flexible and engaging learning experience. Participants can access the program content from anywhere, at their own pace.
- Badging and Certification: Upon completion of the program, participants receive digital badges and certifications from IBM, recognizing their achievement and validating their skills in specific technology areas. These credentials can enhance their resumes and demonstrate their readiness for industry roles.
- Industry Connections and Job Opportunities: IBM collaborates with industry partners, employers, and recruitment agencies to provide participants with job placement assistance, career guidance, and networking opportunities. The program aims to connect qualified candidates with potential employers in the tech industry.
- Continuous Learning Support: The IBM Career Education Program promotes continuous learning by providing access to additional resources, webinars, and communities to support participants in their career growth and development beyond the initial training program.
Overall, AI & IoT solutions have the potential to revolutionize the way we live and work, by creating intelligent systems that can perform tasks faster, more accurately, and with minimal human intervention.
Key Certification
*Certification along with e-courseware offered by IBM- Agile Methodologies
- DevOps Fundamentals
- Python Programming
- Data Analysis with Python
- Python for Data Science
- Database Fundamentals
- Introduction to Big Data Hadoop and the Ecosystems
- Machine Learning with Python
- What is a project
- Project Execution Methodologies
- Agile Deep Dive
- Scrum – Deep Dive
- Scrum Artifacts
- Scrum Ceremonies
- Scrum Sprint Planning
- Scrum Metrics
- Additional Info
- evOps Fundamentals
- DevOps Usecase
- Advanced DevOps
- Introduction to DevOps on IBM Cloud
- Introduction to Python
- Introduction
- Installation
- variables, Operators and Strings
- Deep Dive into Python
- Input Output functions
- Loops, List, dictionaries, tuples
- File Handler
- Python Libraries
- Pandas
- Series and Data Frames
- Grouping, aggregating, and applying
- Error Handling
- Dealing with syntax errors
- Exceptions
- Handling exceptions with try/except
- Advance
- Regression
- Correlation Matrix
- Linear Regression
- Machine Learning Algorithms
- Model Evaluation: Overfitting & Underfitting
- 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
- M Cleaning and Preparing the Data
- Identify and Handle Missing Values
- Data Formatting
- Data Normalization Sets
- Binning
- Indicator variables
- Summarizing the Data Frame
- Descriptive Statistics
- Basic of Grouping
- ANOVA
- Correlation
- More on Correlation
- 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
- Model Evaluation
- Model Evaluation
- Over-fitting, Under-fitting, and Model Selection
- Ridge Regression
- Grid Search
- Model Refinement
- Python Basics
- Your first program
- Types
- Expressions and Variables
- String Operations
- Python Data Structures
- Lists and Tuples
- Sets
- Dictionaries
- Python Programming Fundamentals
- Conditions and Branching
- Loops
- Functions
- Objects and Classes
- Working with Data in Python
- Reading files with open
- Writing files with open
- Loading data with Pandas
- Working with and Saving data with Pandas
- Working with Numpy Arrays and Simple APIs
- Numpy 1D Arrays
- Numpy 2D Arrays
- Simple APIs
- API Setup
- Understanding Database Concepts
- Understanding Database Storage
- Entities and Relationships
- The Relational Data Model
- Normalization
- Database Design and Performance Tuning
- Creating Database Objects
- Manipulating Data
- JDBC As The Fundamental Java API
- JPA as the JAVA ORM API
- Database Security
- Understanding Database Backup And Restore
- Introduction To Mysql
- What is Big Data and Data Analytics
- Overview about HDP
- Introduction to Apache Ambari
- Hadoop and the Hadoop Distributed File System (HDFS)
- MapReduce and YARN
- Apache Spark
- Overview on Data File Formats, HBase, Pig, Hive, R and Python
- ZooKeeper, Slider, and Knox
- Flume and Sqoop
- DataPlane Service
- Stream Computing
- 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
