Applied Data Science - AI/ML & GenAI
Applied Skills
15 modules
Hinglish
Certificate of completion
Lifetime access
Master the art of applying machine learning algorithms to real-world problems
Overview
In this comprehensive course on "Applied Data Science - AI/ML & GenAI," you will dive deep into the world of data science, artificial intelligence (AI), machine learning (ML), and generative artificial intelligence (GenAI). The course is designed to equip you with the practical knowledge and skills needed to apply data science techniques, AI algorithms, ML models, and GenAI technology in real-world scenarios. You will learn how to collect, analyze, and interpret data using various tools and techniques. In the AI component of the course, you will explore the principles of artificial intelligence, including machine learning, deep learning, natural language processing, computer vision, and more. You will also gain hands-on experience in building AI models and algorithms to solve complex problems. The ML portion of the course will cover the fundamentals of machine learning, including supervised learning, unsupervised learning, reinforcement learning, and neural networks. You will learn how to train ML models, evaluate their performance, and deploy them in production environments. Moreover, the course will introduce you to the emerging field of generative artificial intelligence (GenAI), where you will learn how to create AI systems that can generate new content, such as images, text, music, and more. You will explore cutting-edge techniques in Generative Adversarial Networks (GANs), variational autoencoders, and other GenAI models. By the end of the course, you will have a solid understanding of how to apply data science, AI, ML, and GenAI techniques to tackle real-world problems and drive innovation in various industries. Whether you are a beginner or an experienced professional looking to upskill, this course will provide you with the knowledge and tools needed to excel in the field of data science and artificial intelligence.
Key Highlights
Learn advanced data science techniques
Master artificial intelligence and machine learning
Explore the fascinating field of GenAI
Hands-on projects and real-world applications
Understanding of algorithms and models
Practical experience with AI tools
Develop AI/ML skills for various industries
Interactive sessions and expert instructors
What you will learn
Understanding Applied Data Science
Explore the foundations and principles of applied data science in AI, ML, and GenAI technologies.
Implementing AI and ML Techniques
Learn to implement advanced AI and ML techniques to analyze data sets and derive insights and predictions.
Leveraging Generative AI
Discover the capabilities of Generative AI and its applications in various domains for creative and innovative solutions.
Modules
Introduction
1 attachment • 33.62 mins
All About course - Watch before enrolling the course
Data Understanding
16 attachments
All about Data Analysis : Pandas Session - 1
All about Data Analysis : Pandas Session - 2
Numpy in depth
Pandas Notebook
Data Visualization Technique - Exploratory Data Analysis - I
EDA Notebook
Data Visualization Technique - Exploratory Data Analysis - II
EDA notebook 2
Project - I : Data Analyst
Retail_data_analysis_notebook
Retail_DATA
Analyst Interview - I
DATA ANALYSIS USING PANDAS
[Revision - 1] - How to perform data analysis
data_retail_walmat([Revision - 1] - How to perform data analysis)
Data_Analyst_Interview_Questions
Basic Math
16 attachments • 12 mins
Linear Algebra - I
Linear Algebra - II
Linear Algebra - ||| (Code)
Probability and Statistics - I
Probability and Statistics - II
Calculus - 1
Calculus - 2
Calculus - 3
Calculus - 4
Calculus - 5
Calculus - 6
Calculus - 7
Calculus - 8
Calculus - 9
Calculus - 10
Calculus - 11
Models : Zero level understanding
3 attachments
Simple mathematical models
Model warm-up : KNN basics
Simple_Mathematcal_model
Applied Machine learning
47 attachments • 5 hrs
Deep dive into KNN
Building Machine learning solution using KNN algorithm
ML Algorithms Different Cases - I
ML Algorithms Different Cases - II
ML Algorithms Different Cases- III
Performance Metrics - I
Performance Metrics - II
Logistic Regression - I
Logistic Regression - II
L2 Regularization : How to avoid overfitting
Logistic Regression: Code
GridSearch & RandomSearch : How to optimized parameters
Mini Project : Loan Approval Prediction - End to End implementation - I
Loan-Approval-Code-1
Mini Project : Loan Approval Prediction - End to End implementation - II
Loan_approval_final - II
Naive Bayes Algorithm - Basics ( What is conditional probability, bayes theorem)
What kind of problems we can solve using Naive bayes algorithm
Math behind Naive bayes - I
Math behind Naive bayes - II
Naive Bayes issue: Why we do Laplace smoothing
Naive bayes issue : Numerical stability issue
Naive Bayes : Hands-on Code
Nive_bayes_code_notebook
Introduction to Decision tree
What is Decision tree : How it is different from other algorithm
What is Entropy
What is information gain
What is gini impurity
How to construct three in Decision Tree
Hand-on Decision tree implementation
Decision Tree Learning material [Optional]
Hands-on : Decision Tree Code
How we split numerical feature in DT
Is feature scaling needed in DT
Overfitting and underfitting in DT
How DT will behave in different cases
What are Ensembles
What is bagging techniques and how it work internally
Some advantages of Bagging Technique
Introduction to Random Forest algorithm
What is OOB
Random Forest code walk through
Boosting vs bagging
Boosting in depth
Residules_Loss_Function
GBDT_XGBoost_code
Introduction to NLP
16 attachments • 3 hrs
What is NLP
Problem Statement : What problem we are solving to learn NLP
Text input : How to formulate given problem into ML/DL Problem
Why to convert input text into vector or numerical form
What is Bag of Word : Vectorization technique - I
why pre-processing needed before vectorization
What is stemming and lemmatization
What is TF-IDF : Vectorization technique-2
Hands-on : Text preprocessing
Code_Text_preprocessing
what is unigram bigram and n gram in depth
What is word2vec and other variation for vectorization
Mini Project : Amazon review Sentiment Analysis
Code: Amazon review Sentiment Analysis
What is RNN - Mathematics behind RNN model
Code walkthrough RNN
Case study : Machine learning
3 attachments
Case Study - I
Case Study - II
Case Study - III
Interview Prep For ML/Analyst Role
2 attachments
Interview Prep Session - I
Interview Prep Session - II
Deep Learning
8 attachments • 1 hrs
History of Neural networks and Deep Learning
How Biological Neurons work?
Growth of biological neural networks
Mathematical Notation; Feedforward, Backpropagation and weight tunning
ANN_basics
chapter3
111 pages
[Continue] Backpropagation, Feedforward and activation function
Example - 1
Case Study : Deep learning
Assignments - Data Analyst
Assignment - Machine learning
1 attachment
Assignment - I
GenAI
Structured Query Language
Live Sessions
2 attachments
Doubt Session - 1
Test
Certification
When you complete this course you receive a ‘Certificate of Completion’ signed and addressed personally by me.
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Can I interact with the instructor during the course?
Absolutely! we are committed to providing an engaging and interactive learning experience. You will have opportunities to interact with them through our community. Take full advantage to enhance your understanding and gain insights directly from the expert.
About the creator
Applied Skills
Computer Engineer with a passion for Data Science, specializing in building tools from scratch. Over 10 years of experience teaching Applied Mathematics for IIT-JEE, GATE, and 5+ years in data science.
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