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)
