Course Overview
This course provides a thorough introduction to Data Science and Machine Learning, equipping participants with the essential tools and techniques required to extract insights from data and build predictive models. Through a blend of theoretical concepts and practical exercises, attendees will explore data preprocessing, feature selection, model training, evaluation, and deployment. The course emphasizes hands-on learning using popular tools and libraries such as Python, pandas, scikit-learn, and TensorFlow, making it ideal for professionals aiming to harness the power of data in their respective fields.
Course Duration
5 Days
Who Should Attend
- Data Analysts and Data Scientists looking to enhance their skills.
- IT professionals and software engineers interested in transitioning to data science roles.
- Business analysts and managers who want to leverage data-driven decision-making.
- Academics and researchers exploring data science and machine learning methodologies.
- Anyone with a background in programming and statistics looking to delve into machine learning.
Course Objectives
By the end of this course, participants will be able to:
- Understand the fundamental concepts of data science and machine learning.
- Learn how to preprocess and clean data for analysis.
- Develop skills to build, train, and evaluate machine learning models.
- Gain proficiency in using Python and its libraries for data science tasks.
- Apply machine learning algorithms to solve real-world problems.
Course Outline:
Module 1: Introduction to Data Science and Python for Data Analysis
- Overview of Data Science
- Python programming basics for data analysis
- Introduction to Jupyter Notebooks
- Data manipulation with pandas
- Data visualization with matplotlib and seaborn
Module 2: Data Preprocessing and Exploration
- Data cleaning and handling missing values
- Feature engineering and selection techniques
- Data normalization and scaling
- Exploratory data analysis (EDA)
- Handling categorical and time series data
Module 3: Supervised Learning: Regression and Classification
- Understanding supervised learning
- Linear and logistic regression
- Decision trees and random forests
- Support Vector Machines (SVM)
- Model evaluation metrics: accuracy, precision, recall, F1-score
Module 4: Unsupervised Learning: Clustering and Dimensionality Reduction
- Overview of unsupervised learning
- K-Means and hierarchical clustering
- Principal Component Analysis (PCA)
- Anomaly detection techniques
- Application of clustering in customer segmentation
Module 5: Introduction to Deep Learning and Model Deployment
- Basics of neural networks and deep learning
- Introduction to TensorFlow and Keras
- Building simple neural networks
- Overfitting, regularization, and hyperparameter tuning
- Model deployment strategies and tools (Flask, Docker)
Customized Training
This training can be tailored to your institution needs and delivered at a location of your choice upon request.
Requirements
Participants need to be proficient in English.
Training Fee
The fee covers tuition, training materials, refreshments, lunch, and study visits. Participants are responsible for their own travel, visa, insurance, and personal expenses.
Certification
A certificate from Ideal Workplace Solutions is awarded upon successful completion.
Accommodation
Accommodation can be arranged upon request. Contact via email for reservations.
Payment
Payment should be made before the training starts, with proof of payment sent to [email protected].
For further inquiries, please contact us on details below:
