Video Training →Become A Data Scientist – Job Training (Beginner – Advanced)

What you'll learn
Introduction to the field of Data Science
How to build a Data Scientist's mindset
Data Science approach
Supervised Models: Regression
Supervised Learning Models: Classification
Various types of classification models for binary and multi-class classification
Performance metrics: Confusion Matrix, F1, Precision, Recall, Accuracy - which one to use in which case, with examples
Unsupervised Learning: Clustering, performance metrics
Model Improvement Methods
What does model improvement mean? What are the different options at our disposal and when to use them?
Feature Selection and Dimensionality reduction methods: Lasso Regression, Ridge Regression, PCA
Hyperparameter Tuning: Various methods, deep dive into Grid-search and Random-search
Cross-Validation
Application to hyperparameter tuning
Introduction NLP with various common NLP tasks
How is Natural Language data different from Panel or Tabular Data?
Steps for processing natural language inputs: NLP pipeline (Tokenisation, Stopword Removal, Stemming, Lemmatisation)
Various methods of word embedding: Bag of Words, Word2vec, Glove, and using Large Language Models
Brief introduction to Recurrent Neural Networks and Long Short Term Memory models, the building blocks for all LLMs
Pre-trained models and transfer learning
Requirements
No previous knowledge required! Suitable for candidates from any domain.
Description
Welcome to 'Data Scientist - Job training', a first-of-its kind short program designed for jobseekers and career change aspirants. This course is specifically created as a JOB-BASED TRAINING program thereby teaching concepts hands-on and relevant to real-work environment. If you are looking for a job in Data Science or if you are a student who would like to get first experience in this domain, this course is exactly for you. How is this program a JOB-BASED TRAINING and how will it equip you with skills relevant for your role:What companies ask for a Data Scientist role? Gather and clean large datasets from diverse sources to ensure data accuracy and completeness.Collaborate with cross-functional teams to understand data requirements and optimize data collection processes.Conduct exploratory data analysis to identify patterns, trends, and anomalies.Design and implement machine learning models for predictive and prescriptive analytics.Develop and engineer relevant features to improve model performance and accuracy.Evaluate model performancesStay informed about the latest advancements in machine learning and data science techniques.How this course meets the requirements?Learn about Data Loading and EDACollaborate with team members on various tasks and on KaggleGain knowledge on EDA and Data Insight generationUnderstand the concepts of feature engineering, feature selection, baseline model building, model performance analysis and model metricsLearn about hyperparameter tuning, comparison of models, grid search, cross-validationLearn and evaluate model performance and compare various modelsGet introduced to NLP and advancements in Data ScienceTools you will learn: PandasNumpy functionsPython functions Kaggle notebooksGoogle colabmatplotlibseabornnltkScikit learnXGBoostLightGBMTransformers, and many more ML packages and libraries, model metrics: cross entropy, DB Index, etc. NLP, and LLMs for ML tasks (hugging face library) Extra Module and Benefits:1. AI Fundamentals and Applications: Unlock exclusive access to one of our AI modules Learn from our experts leveraging AI to enhance your productivity and understand the wide variety of applications of AI across industries2. Career GuidanceUnderstand how to effectively search for a job, find startups, craft a compelling CV and Cover Letter, types of job platforms and many more!Trainers:Dr. Chetana Didugu - GermanyDr. Chetana Didugu is an Experienced Data Scientist, Product Expert, and PhD graduate from IIM Ahmedabad. She has worked 10+ years in various top companies in the world like Amazon, FLIX, Zalando, HCL, etc in topics like Data Analysis and Visualisation, Business Analysis, Product Management, Product Analytics & Data Science. She has trained more than 100 students in this domain till date.Aravinth Palaniswamy - GermanyFounder of 2 startups in Germany and India, Technology Consultant, and Chief Product Officer of Moyyn, and has 10+ years of experience in Venture Building, Product and Growth Marketing.
Overview
Section 1: Introduction
Lecture 1 Data Scientist Workshop
Lecture 2 Introduction to Data Science (contd)
Lecture 3 Supervised learning
Lecture 4 Unsupervised learning
Lecture 5 Hyperparameter tuning
Section 2: Career Guidance
Lecture 6 How to search for jobs effectively?
Lecture 7 Hoe to write a good CV and Cover Letter?
Section 3: AI applications
Lecture 8 Part 1
Lecture 9 Part 2
Jobseekers,Aspiring Data Analysts,Entrepreneurs,Non tech candidates,Students in any domain,Career change aspirants
Homepage
https://www.udemy.com/course/become-a-data-scientist-ai-job-training-beginner-advanced-gate-moyyn/