Artifical Intelligence,
Machine Learning,
Deep Learning,
Quantitative,
Data Analyst,
Data Science,
Probability,
Statistics,
Algorithm,
analytics,
Finance,
iNeuron,
Course,
Python,
Quant,
AI
iNeuron – Full-Stack Data Science
This full-stack data science course with a certification program and a full-time one-year internship provided by iNeuron intelligence private limited, where you will learn all the stack required to work in the data science, data analytics, and big data industries, including machine learning operations and cloud infrastructure, as well as real-time industry project and product development with the iNeuron product development team, and you will contribute on various levels.
Course contents
- Part 1: Python Programming from Fundamentals to Advanced
- Module 1 - Welcome to the Course
- Module 2 - Python Fundamentals
- Module 3 - Advanced Python
- Module 4 - Working with Databases & Python
- Module 5 - Working with Pandas & Numpy
- Module 6 - GUI Programming
- Module 7 - Working with Graphs & Charts
- Module 8 - API Development with Python
- Module 9 - Python Projects
- Part 2: Data Science & Statistics Foundations
- Module 10 - Statistics Fundamentals
- Module 11 - EDA & Feature Engineering
- Module 12 - Machine Learning Introduction
- Module 13 - Machine Learning End-to-End Project
- Module 14 - PCA in Machine Learning
- Module 15 - NLP for Machine Learning
- Module 16 - Time Series Analysis
- Module 17 - Advanced Statistics
- Part 3: Machine Learning Projects (ML Projects)
- Module 18 - ML Projects Overview
- Module 19 - ML Project 1: Fault Detection in Wafers Based on Sensor Data
- Module 20 - ML Project 2: Cement Strength Prediction
- Module 21 - ML Project 3: Credit Card Defaulters Prediction
- Module 22 - ML Project 4: Forest Cover Type Prediction
- Module 23 - ML Project 5: Income Prediction
- Module 24 - ML Project 6: Insurance Fraud Detection
- Module 25 - ML Project 7: Mushroom Classification
- Module 26 - ML Project 8: Phishing Classifier
- Module 27 - ML Project 9: Thyroid Detection
- Module 28 - ML Project 10: Visibility Climate Prediction
- Module 29 - Advanced Time Series Project
- Part 4: Deep Learning & Neural Networks (DL ANN)
- Module 30 - DL ANN: Introduction
- Module 31 - DL ANN: Perceptron
- Module 32 - DL ANN: Neural Networks Foundations
- Module 33 - DL ANN: Training Deep Neural Networks
- Module 34 - DL ANN: Optimization and Regularization
- Module 35 - DL ANN: Hyperparameter Tuning
- Module 36 - DL ANN: Advanced Architectures
- Part 5: Computer Vision
- Module 37 - Computer Vision: Introduction
- Module 38 - Computer Vision: CNN Foundations
- Module 39 - Computer Vision: CNN Architectures
- Module 40 - Computer Vision: Image Classification Hyperparameter Tuning
- Module 41 - Computer Vision: Data Augmentation
- Module 42 - Computer Vision: Object Detection Basics
- Module 43 - Computer Vision: Object Detection Architectures
- Module 44 - Computer Vision: Object Detection using TensorFlow 1.x
- Module 45 - Computer Vision: Training a Custom Cards Detector using TensorFlow 1.x
- Module 46 - Computer Vision: Creating a Cards Detector Web App with TFOD1
- Module 47 - Computer Vision: Object Detection using TensorFlow 2.x
- Module 48 - Computer Vision: Training a Custom Chess Piece Detector using TensorFlow 2.x
- Module 49 - Computer Vision: Creating a Chess Piece Detector Web App with TFOD2
- Module 50 - Computer Vision: Object Detection using Detectron2
- Module 51 - Computer Vision: Training a Custom Detector using Detectron2
- Module 52 - Computer Vision: Creating a Custom Detector Web App with Detectron2
- Module 53 - Computer Vision: Object Detection using YoloV5
- Module 54 - Computer Vision: Training a Custom Warehouse Apparel Detector using YoloV5
- Module 55 - Computer Vision: Creating a Warehouse Apparel Detector Web App with YOLOV5
- Module 56 - Computer Vision: Image Segmentation
- Module 57 - Computer Vision: MASK R-CNN Practicals with TFOD
- Module 58 - Computer Vision: MASK R-CNN Practicals with Detectron2
- Module 59 - Computer Vision: Face Recognition Project
- Module 60 - Computer Vision: Object Tracking Project
- Module 61 - Computer Vision: GANs (Generative Adversarial Networks)
- Part 6: Computer Vision Projects
- Module 62 - Computer Vision Project: Traffic Vehicle Detection
- Module 63 - Computer Vision Project: Helmet Detection
- Module 64 - Computer Vision Project: Fashion Apparel Detection
- Module 65 - Computer Vision Project: Image to Text (OCR)
- Module 66 - Computer Vision Project: Shredder System
- Module 67 - Computer Vision Project: Automatic Number Plate Recognition with TFOD1.x
- Part 7: Natural Language Processing (NLP)
- Module 68 - NLP: Overview
- Module 69 - NLP: Word Embeddings
- Module 70 - NLP: RNN
- Module 71 - NLP: LSTM & GRU
- Module 72 - NLP: Attention-Based Model
- Module 73 - NLP: Transfer Learning in NLP
- Part 8: Natural Language Processing (NLP) Projects
- Module 74 - NLP Project: Megatron
- Module 75 - NLP Project: Brand Measures
- Module 76 - NLP Project: Text to Speech
- Module 77 - NLP Project: Speech to Text
- Module 78 - NLP Project: Spell Corrector
- Module 79 - NLP Project: Named Entity Recognition (NER)
- Module 80 - NLP Project: Machine Translation & Keyword Spotting
- Module 81 - NLP Project: Keyword Extractor & Text Summarization
- Module 82 - NLP Project: Text Paraphrasing
- Part 9: AIOps & Development Tools
- Module 83 - AIOPS: Introduction
- Module 84 - AIOPS: Linux
- Module 85 - AIOPS: Git
- Module 86 - AIOPS: Docker
- Part 10: Big Data
- Module 87 - BigData: Introduction to Big Data and Data Engineering
- Module 88 - BigData: Introduction to Distributed Systems - Hadoop and MapReduce
- Module 89 - BigData: MapReduce & YARN
- Module 90 - BigData: Hive
- Module 91 - BigData: Hive Hands-On
- Module 92 - BigData: NoSQL and HBase
- Module 93 - BigData: Sqoop
- Module 94 - BigData: Spark
- Module 95 - BigData: Spark ML
- Module 96 - BigData: Spark Streaming
- Module 97 - BigData: Kafka
- Module 98 - BigData: Apache Airflow - Workflow Management Platform
- Module 99 - BigData: Capstone Projects
- Part 11: Data Visualization (Power BI)
- Module 100 - Power BI: Basic Charts
- Module 101 - Power BI: Working with Maps
- Module 102 - Power BI: Tables and Matrix
- Module 103 - Power BI: Other Charts
- Module 104 - Power BI: Cards and Filters
- Module 105 - Power BI: Slicers
- Module 106 - Power BI: Advanced Charts
- Module 107 - Power BI: Objects and Formatting
- Module 108 - Power BI Service Introduction
- Module 109 - Power Query: Text Functions
- Module 110 - Power Query: Date Functions
- Module 111 - Power Query: Number Functions
- Module 112 - Power Query: Append Files
- Module 113 - Power Query: Merge Files
- Module 114 - Power Query: Conditional Columns
- Module 115 - Power Query: Important Topics
- Module 116 - Power Query: M Language Introduction
- Part 12: Data Visualization (Tableau)
- Module 117 - Tableau: Introduction
- Module 118 - Tableau: Charts - Part 1
- Module 119 - Tableau: Charts - Part 2
- Module 120 - Tableau: Charts - Part 3
- Module 121 - Tableau: Charts - Part 4
- Module 122 - Tableau: Charts - Part 5
- Module 123 - Tableau: Charts - Part 6
- Module 124 - Tableau: Charts - Part 7
- Module 125 - Tableau: Charts - Part 8
- Module 126 - Tableau: Charts - Part 9
- Module 127 - Tableau: Charts - Part 10
- Module 128 - Tableau: Charts - Part 11
- Part 13: Supplementary Tools & Chatbots
- Module 129 - SQL for Data Science
- Module 130 - Excel for Data Analysis
- Module 131 - Chatbot: Google Dialogflow
- Module 132 - Chatbot: RASA
- Module 133 - Chatbot: Amazon Lex
- Module 134 - Chatbot: Azure LUIS
- Part 14: Interview Preparation
- Module 135 - Interview Preparation: Introduction & Induction
- Module 136 - Interview Preparation: Transition Stories
- Module 137 - Interview Preparation: AI Projects Discussion
- Module 138 - Interview Preparation: Python
- Module 139 - Interview Preparation: Databases
- Module 140 - Interview Preparation: General Interview Questions
- Module 141 - Interview Preparation: Project Discussion Deep Dive
- Module 142 - Interview Preparation: Advanced Interview Questions
- Module 143 - Interview Preparation: Final Review & General Discussion
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