Technologies

☛  Java

☛  Data Science and AI

Java Training

Core features with version enhancements

About Java Training is curated by me as per the industrial requirements & demands. Java Training encompasses comprehensive knowledge on basic and advanced concepts of core Java & Java EE along with popular frameworks like Hibernate, Spring, and Spring Boot.

In this course, you will gain expertise in the concepts like Java Array, Java OOPs, Java Function, Java Loops, Java Collections, Java Thread, Java Servlet, Java Design Patterns, and Web Services using industry use-cases.

Curriculum

Introduction to Java

Learning Objectives: In this module, you will learn about Java architecture, advantages of Java, develop the code with various data types, conditions, and loops.
Topics:
Introduction to Java
Class Files
Data types and Operations
Boxing, unautoboxing
Switch and Enumerations
Bytecode
Compilation Process
if conditions
Loops – for, while and do while
break and continue

Data Handling and Functions

Learning Objectives: In this module, you will learn how to code with arrays, functions, and strings.
Topics:
Arrays – Single/Multidimensional
Function with Arguments
Static Polymorphism
String, StringBuffer, String Builder Classes
Functions
Function Overloading
String Constant Pool

Object Oriented Programming in Java

Learning Objectives: In this module, you will learn object-oriented programming through Java using Classes, Objects and various Java concepts like Abstract, Final etc.
Topics:
OOPS in Java: Concept of Object Orientation
Attributes and Methods
Classes and Objects
Methods and Constructors – Default Constructors and Constructors with Arguments
Inheritance and its types
Abstract Classes/ interfaces
Final Classes/Methods
Static fields/methods/blocks

Packages and Multi Threading

Learning Objectives: In this module, you will learn about packages in Java and scope specifiers of Java. You will also learn exception handling and how multi-threading works in Java.
Topics:
Packages and Interfaces
Access Specifiers: Public, Private, Protected and Package
Exception Handling: Try, Catch, Finally, Throw and Throws
Multi-Threading: Runnable Interface, Extending a Thread Class, Synchronization in Threads

Java Collections

Learning Objectives: In this module, you will learn how to write code with Wrapper Classes, Inner Classes, and Applet Programs. How to use io.lang and util packages of Java and the very important topic of Java which is Collections.
Topics:
Wrapper Classes and Inner Classes: Integer, Character, Boolean, Float etc
Applet Programs: How to write UI programs with Applet, Java.lang, Java.io, Java.util
Collections: ArrayList, Vector, HashSet, TreeSet, HashMap, HashTable
Gradle, Maven, SpringBoot
Restful web services – writing API implementation
Use postman, REST Client
Git Repository

** Every topic covers with industry standards includes best practices, code quality, naming conventions, organizing code, formatting , documentation, logging

AI and Data Science Full Course Curriculum

Module 1: Introduction to Data Science and AI

1.1 Overview of Data Science

Definition and importance
Key concepts and terminology
Data science lifecycle

1.2 Introduction to AI

What is AI?
Types of AI: Narrow AI vs. General AI
Applications of AI in various industries

1.3 Data Science vs. AI

Understanding the relationship between data science and AI
How they complement each other

Module 2: Data Collection and Preprocessing

2.1 Data Sources

Structured vs. unstructured data
Public datasets and APIs
Web scraping techniques

2.2 Data Cleaning

Handling missing values
Outlier detection and treatment
Data transformation techniques

2.3 Data Exploration and Visualization

Exploratory Data Analysis (EDA)
Data visualization tools (e.g., Matplotlib, Seaborn)
Descriptive statistics

Module 3: Programming for Data Science

3.1 Introduction to Python/R

Setting up the environment
Basic programming concepts
Libraries for data science (e.g., NumPy, Pandas, Scikit-learn)

3.2 Data Manipulation with Pandas

Dataframes and series
Data manipulation techniques (filtering, grouping, merging)

3.3 Data Visualization Techniques

Creating effective visualizations
Visualization libraries (Matplotlib, Seaborn, Plotly)

Module 4: Machine Learning Fundamentals

4.1 Introduction to Machine Learning

What is machine learning?
Types of machine learning: Supervised, unsupervised, and reinforcement learning

4.2 Supervised Learning Algorithms

Linear Regression
Logistic Regression
Decision Trees
Support Vector Machines (SVM)

4.3 Unsupervised Learning Algorithms

Clustering (K-means, Hierarchical)
Dimensionality Reduction (PCA, t-SNE)

Module 5: Advanced Machine Learning Techniques

5.1 Ensemble Learning

Bagging and boosting
Random Forests
Gradient Boosting Machines (GBM)

5.2 Neural Networks and Deep Learning

Introduction to neural networks
Understanding feedforward and backpropagation
Deep learning frameworks (TensorFlow, Keras)

5.3 Natural Language Processing (NLP)

Text preprocessing techniques
Sentiment analysis
Topic modeling (LDA)

Module 6: Model Evaluation and Selection

6.1 Performance Metrics

Classification metrics (accuracy, precision, recall, F1-score)
Regression metrics (MSE, RMSE, R-squared)

6.2 Cross-Validation Techniques

K-fold cross-validation
Grid search for hyperparameter tuning

6.3 Model Deployment

Introduction to model deployment
Tools and frameworks for deployment (Flask, Docker)

Module 7: Big Data Technologies

7.1 Introduction to Big Data

What is big data?
Characteristics of big data (volume, velocity, variety)

7.2 Big Data Tools and Frameworks

Overview of Hadoop and Spark
Data processing with PySpark

7.3 NoSQL Databases

Introduction to NoSQL databases (MongoDB, Cassandra)
When to use NoSQL vs. SQL

Module 8: Ethical AI and Data Science

8.1 Understanding Bias and Fairness

Ethical considerations in data collection and analysis
Addressing bias in machine learning models

8.2 Privacy and Security

Data privacy regulations (GDPR, CCPA)
Techniques for secure data handling

8.3 Responsible AI Practices

Transparency in AI algorithms
Building trust in AI systems

Module 9: Capstone Project

9.1 Project Planning and Design

Identifying a real-world problem
Formulating hypotheses and designing experiments

9.2 Data Collection and Analysis

Collecting data using various methods
Analyzing data and building models

9.3 Presentation and Reporting

Preparing a report and presentation
Sharing findings with stakeholders

Additional Resources

Resources

Recommended Reading:
Books, articles, and journals on AI and data science.
Online Resources:
Websites, blogs, and forums for continued learning.
Tools and Software:
List of tools used throughout the course (e.g., Jupyter Notebooks, Tableau).

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