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2 Month/20 Hours Price:85,000
80,000
Python for Data Science and Machine Learning Course with Projects
This course provides a comprehensive introduction to Python programming for data science and machine learning. Students will learn Python basics, data manipulation, visualization, and machine learning fundamentals. By the end of the course, participants will develop a portfolio of projects, showcasing their new skills in data science and machine learning.
Key Learnings:
Master Python programming fundamentals and libraries for data science.Perform data cleaning, manipulation, and analysis using Pandas.
Visualize data insights using Matplotlib and Seaborn.
Build and evaluate machine learning models using scikit-learn.
Develop practical, real-world projects to showcase data science and machine learning skills.
Course Modules:
Module 1: Introduction to Python for Data Science
Overview of Python for Data SciencePython basics: data types, variables, loops, and conditionals
Functions, modules, and packages
Introduction to Jupyter Notebooks
Module 2: Data Analysis and Manipulation with Pandas
Introduction to data analysis and PandasData frames, series, and basic operations
Data cleaning and preprocessing
Handling missing data and data manipulation
Module 3: Data Visualization with Matplotlib and Seaborn
Introduction to data visualizationBasic plots with Matplotlib (line, bar, scatter)
Advanced visualizations with Seaborn (heatmaps, pair plots)
Customizing and exporting visualizations
Module 4: Statistical Analysis and Hypothesis Testing
Basics of descriptive statisticsProbability distributions
Inferential statistics and hypothesis testing
T-tests, chi-square tests, and ANOVA
Module 5: Introduction to Machine Learning
Overview of machine learning and its applicationsTypes of machine learning: supervised, unsupervised, and reinforcement
Introduction to scikit-learn library
Building a simple machine learning model
Module 6: Supervised Learning Algorithms
Linear regression and logistic regressionDecision trees and random forests
Model evaluation metrics: accuracy, precision, recall
Hyperparameter tuning and model selection
Module 7: Unsupervised Learning and Clustering
Introduction to unsupervised learningK-means clustering and hierarchical clustering
Dimensionality reduction techniques (PCA)
Practical applications of clustering
Module 8: Model Evaluation and Optimization (Week 8)
Cross-validation and model evaluation techniquesGrid search and randomized search
Ensemble learning: bagging, boosting, and stacking
Final project guidance and best practices
Aspiring data scientists and machine learning engineers.
Professionals looking to integrate data science skills into their careers.
Career Pathways:
Data AnalystJunior Data Scientist
Machine Learning Engineer (entry-level)
Python Developer for Data Science Applications
International Student Fees: USD295
Job Interview Preparation (Soft Skills Questions & Answers)
Tough Open-Ended Job Interview Questions
What to Wear for Best Job Interview Attire
Job Interview Question- What are You Passionate About?
How to Prepare for a Job Promotion Interview
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Internships, Freelance and Full-Time Work opportunities
Join Internships and Referral Program (click for details)
Work as Freelancer or Full-Time Employee (click for details)
Flexible Class Options
Week End Classes For Professionals SAT | SUN
Corporate Group Training Available
Online Classes – Live Virtual Class (L.V.C), Online Training
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