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In this video on probability and statisics for data science and ai, we will dive into essential concepts such as probability distributions, Bayes' theorem, hypothesis testing, and regression analysis. Learn how to apply these principles to real-world data sets, enhancing your ability to make data-driven decisions. Whether you're a beginner or looking to solidify your knowledge, this video breaks down complex topics into easy-to-understand segments, providing practical examples and hands-on exercises. Join us to master the statistical foundation crucial for a successful career in Data Science and AI.

Following are the topics covered in Complete Statistics For Data Science Full Course 2025:

00:00:00 - Introduction to Complete Statistics For Data Science in 8 Hours
00:01:45 - Probability and Statistics
00:48:04 - Mathematics for machine learning
02:38:28 - What is Data Science
02:42:49 - Data science course unboxing
02:48:59 - Roadmap to Data Science
02:58:11 - Classification of Machine Learning
Decision Tree in Machine Learning
Random Forest Algorithm
K Means Clustering Algorithm
Naive Bayes Classifier
What is Deep Learning?
06:15:10 - Data Science Interview Questions

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✅ About Post Graduate Program In Data Science:
This in-depth Data Science program is designed for working professionals, and covers job-critical topics like R, Python programming, Machine Learning techniques, NLP notions, and Data Visualization with Tableau, using an active learning model that includes live sessions from global professionals, practical labs, IBM Hackathons, and corporate ready projects

Key Features
✅ Program completion certificate from Purdue University Online and Simplilearn
✅ Access to Purdue’s alumni association membership on program completion
✅ Core curriculum delivered in live online classes by industry experts
✅ Industry-recognized IBM certificates for IBM courses
✅ Engage in live online masterclasses led by Purdue faculty, staff, and IBM experts
✅ Exclusive hackathons and Ask Me Anything (AMA) sessions by IBM
✅ Gain exposure to ChatGPT, Gemini, Keras, TensorFlow and other prominent tools
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✅ 3 capstones and 25+ hands-on projects from various industry domains
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Skills Covered
✅ Exploratory Data Analysis
✅ Descriptive Statistics
✅ Inferential Statistics
✅ Model Building and Finetuning
✅ Supervised and Unsupervised Learning
✅ Ensemble Learning
✅ Deep Learning
✅ Data Visualization
✅ Generative AI
✅ Prompt Engineering
✅ ChatGPT
✅ Explainable AI
✅ Machine Learning Algorithms
✅ Model Training and Optimization
✅ Model Evaluation and Validation
✅ Natural Language Processing
✅ Computer Vision
✅ Reinforcement Learning
✅ Speech Recognition

Learning Path
✅ Program Induction
✅ Programming Refresher
✅ Data Management using SQL
✅ Python for Data Science (IBM)
✅ Applied Data Science with Python
✅ Machine Learning
✅ Deep Learning Specialization

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