Data Science / Machine Learning Training @ Softzone

Course Overview
The Data Science and Machine Learning Training at Softzone is designed to provide students with practical knowledge in data analysis, artificial intelligence, and predictive modeling using modern tools and programming languages. This course focuses on real-world applications, hands-on training, and project-based learning to prepare students for IT and AI-related careers.

Module 1: Introduction to Data Science

Topics Covered

• What is Data Science
• Role of Data Scientist
• Applications of Data Science in real life
• AI, Machine Learning, Deep Learning overview
• Data Science workflow
• Tools used in Data Science

Outcome
Students understand the basics of data science and career opportunities.

Module 2: Python Programming for Data Science

Topics Covered

• Python basics
• Variables and data types
• Operators and conditions
• Loops and functions
• Lists, tuples, sets, dictionaries
• File handling
• Python libraries introduction

Practical

• Simple Python programs
• Data handling programs

Outcome
Students learn Python programming for data analysis and machine learning.

Module 3: NumPy and Pandas

Topics Covered

• Introduction to NumPy
• Arrays and operations
• Mathematical operations
• Introduction to Pandas
• Series and DataFrames
• Data cleaning
• Data filtering and sorting
• Handling missing data

Practical

• Working with datasets
• Data analysis using Pandas

Outcome
Students learn how to manage and analyze data.

Module 4: Data Visualization

Topics Covered

• Matplotlib
• Seaborn
• Charts and graphs
• Bar chart
• Line chart
• Pie chart
• Histogram
• Scatter plot
• Data storytelling

Practical

• Visualizing real datasets

Outcome
Students learn how to present data visually.

Module 5: Statistics for Data Science

Topics Covered

• Mean, Median, Mode
• Standard deviation
• Probability
• Distribution
• Correlation
• Hypothesis testing
• Data interpretation

Outcome
Students understand statistical concepts for machine learning.

Module 6: Machine Learning Basics

Topics Covered

• What is Machine Learning
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Machine Learning workflow
• Training and testing data

Outcome
Students understand machine learning fundamentals.

Module 7: Machine Learning Algorithms

Topics Covered

Supervised Learning

• Linear Regression
• Logistic Regression
• Decision Tree
• Random Forest
• KNN
• Support Vector Machine

Unsupervised Learning

• K-Means Clustering
• Hierarchical Clustering

Practical

• Model training
• Prediction using datasets

Outcome
Students can build machine learning models.

Module 8: Model Evaluation

Topics Covered

• Accuracy
• Confusion Matrix
• Precision
• Recall
• F1 Score
• Cross validation

Outcome
Students learn how to evaluate machine learning models.

Module 9: Deep Learning Introduction

Topics Covered

• Neural Networks
• TensorFlow basics
• Keras basics
• Image and text data overview

Outcome
Students understand deep learning basics.

Module 10: Real-Time Projects

Projects

• Student performance prediction
• House price prediction
• Sales prediction
• Customer classification
• AI mini project

Outcome
Students gain real project experience.

Module 11: Tools and Technologies

• Python
• Jupyter Notebook
• Google Colab
• Pandas
• NumPy
• Matplotlib
• Scikit-learn
• TensorFlow
• Excel

Course Features

• Live Practical Training
• Real-time Projects
• Industry-based Syllabus
• Recorded Classes
• Doubt Clearing Sessions
• Certificate from Softzone
• Placement Assistance
• Internship Support

Course Duration
3 to 6 Months Training

Who Can Join

• Plus Two Students
• Degree Students
• BCA / BSc / BCom Students
• Engineering Students
• Job Seekers
• Beginners in AI
• Working Professionals

Career Opportunities

• Data Analyst
• Machine Learning Engineer
• AI Developer
• Python Developer
• Business Analyst
• Data Scientist