Data Science Career Guidance Warriors Way Bundle

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₹ 24,491
Description

Data Science Career Guidance Bundle - Warriors Way

BUNDLE INCLUDES:

Technical Skill Set:

💥Python Programming
💥Data Analytics
💥Artificial Intelligence
💥Machine Learning
💥Computer Vision

Mindset & Soft Skills:

💥Resume Building
💥LinkedIn Mastery
💥Interview Skill

Bonus Bundle:

💥Bonus #1: Resume Review
💥Bonus #2: Group Career Coaching
💥Bonus #3: Weekly Master Mind
💥Bonus #4: Private Community
💥Bonus #5: Monthly Hackathon

6 Months Internship E-Certificate

Detailed Agenda - What You Will Learn

Python Master Class

✅Introduction to Python
✅Installing & Working with Python IDLE
✅Configuring Environmental Variables - Command Window
✅Installing Anaconda Navigator (Jupyter Notebook)
✅Working with Anaconda Navigator (Spyder Notebook)
✅Working with Google Colab
✅Working with Pycharm
✅Working with Libraries
✅Simple Arithmetic
✅Introduction to Strings
✅Indexing and Slicing with Strings
✅String Properties and Methods
✅Print Formatting with Strings
✅Lists in Python
✅Dictionaries in Python
✅Tuples with Python
✅Sets in Python
✅Booleans in Python
✅I/O with Basic Files in Python
✅Python Objects and Data Structures
✅Comparison Operators in Python
✅Chaining Comparison Operators in Python with Logical Operators
✅If Elif and Else Statements in Python
✅For Loops in Python
✅While Loops in Python
✅Useful Operators in Python
✅List Comprehensions in Python
✅Methods and the Python Documentation
✅Introduction to Functions
✅Basics of Python Functions
✅Logic with Python Functions
✅Tuple Unpacking with Python Functions
✅*args and **kwargs in Python
✅Lambda Expressions, Map, and Filter Functions
✅Attributes & Class Keyword
✅Class Object Attributes and Methods
✅Inheritance and Polymorphism
✅Special(Magic/Dunder) Methods
✅Modules and Packages
name and "main"
✅Errors and Exceptions Handling
✅Pylint Overview
✅Decorators with Python Overview
✅Generators with Python
✅Python Collections Module
✅Opening and Reading Files and Folders
✅Python Datetime Module
✅Python Math and Random Modules
✅Python Debugger
✅Python Regular Expressions
✅Timing Your Python Code
✅Zipping and Unzipping files with Python
✅Setting Up Web Scraping Libraries
✅Grabbing a Title
✅Grabbing an Image
✅Book Examples
✅Introduction to Images with Python
✅Working with CSV Files in Python
✅Working with PDF Files in Python
✅Sending Emails with Python
✅Receiving Emails with Python

Computer Vision

✅DAY – 1 Introduction to Python & Computer Vision | Python Installation & Installing Libraries | Basic CV| Reading an image | Display,Writing, Saving an Image | Draw a line, circle, rectangle | Draw a text string |Find and Draw Contours | Image Resizing | Blurring an Image
✅DAY – 2 Create Border around Images | Convert an image from one color space to another | Scaling, Rotating, Shifting, and Edge Detection | Erosion and Dilation of images | Denoising of colored images | OpenCV Bitwise AND, OR, XOR, and NOT | Play a video using OpenCV | Video acquisition from the camera | Video acquisition from the Mobile Camera
✅DAY – 3 OpenCV Haar Cascades - Face detection | Eye detection | Mouth detection | Full/partial body detection
✅DAY – 4 Multi-template Matching with OpenCV
✅DAY – 5 OCR a Document, Form with Tesseract & OpenCV
✅DAY – 6 Object Tracking using OpenCV
✅DAY – 7 Watermarking images with OpenCV
✅DAY – 8 Saving Key event video clips with OpenCV
✅DAY – 9 Determining Object Shape and Color with OpenCV
✅DAY – 10 Real-time Panorama & Image Stitching with OpenCV
✅DAY – 11 Barcode reader using Computer Vision
✅DAY – 12 OpenCV Video Augmented Reality
✅DAY – 13 Recognizing digits with OpenCV and Python
✅DAY – 14 Document Scanner using OpenCV
✅DAY – 15 Harry Potter Invisible Cloak

Artificial Intelligence

✅DAY – 1 Overview of this course | Introduction to AI | How to create basic AI application (Chat bot using DialogFlow)
✅DAY – 2 How to install Python & Libraries | Basics of python Programming for AI.

COMPUTER VISION
✅DAY – 3 Introduction to Computer Vision| How to install computer vision libraries
✅DAY – 4 Moving Object Detection and tracking using OpenCV
✅DAY – 5 Face Detection and Tracking using OpenCV
✅DAY – 6 Object Tracking based on color using OpenCV
✅DAY – 7 Face Recognition using OpenCV
✅DAY – 8 Face Emotion recognition using 68-Landmark Predictor OpenCV

DEEP LEARNING
✅DAY – 9 Introduction to Deep learning | How to install DL libraries
✅DAY – 10 Designing your First Neural Network
✅DAY – 11 Object recognition from Pre-trained model
✅DAY – 12 Image classification using Convolutional Neural Network
✅DAY – 13 Hand gesture recognition using Deep Learning
✅DAY – 14 Leaf disease detection using Deep Learning
✅DAY – 15 Character recognition using Convolutional Neural Network
✅DAY – 16 Label reading using Optical Character recognition
✅DAY – 17 Smart Attendance system using Deep Learning
✅DAY – 18 Vehicle detection using Deep Learning
✅DAY – 19 License plate recognition using Deep Learning
✅DAY – 20 Drowsiness detection using Deep Learning
✅DAY – 21 Road sign recognition using Deep Learning

MACHINE LEARNING
✅DAY – 22 Introduction to Machine learning| How to install ML libraries
✅DAY – 23 Evaluating and Deploying the various ML model
✅DAY – 24 Fake news detection using ML
✅DAY – 25 AI snake game design using ML

NATURAL LANGUAGE PROCESSING
✅DAY – 26 Introduction to NLP & it’s Terminology | How to install NLP Libraries NLTK
✅DAY – 27 Title Formation from the paragraph design using NLP
✅DAY – 28 Speech emotion analysis using NLP

DEPLOYING AI IN HARDWARE
✅DAY – 29 Cloud-based AI, Object recognition using Amazon Web Service (AWS) & Imagga
✅DAY – 30 Deploying AI application in Raspberry Pi with Neural Compute stick & Nvidia Jetson Nano

Machine Learning

✅Day-1: Overview A.I | Machine Learning
✅Day-2: Introduction to Python | How to write code in Google Colab, Jupyter Notebook, Pycharm & IDLE

SUPERVISED LEARNING - CLASSIFICATION & REGRESSION
✅Day-3: Advertisement Sale prediction from an existing customer using

LOGISTIC REGRESSION
✅Day-4: Salary Estimation using K-NEAREST NEIGHBOR
✅Day-5: Character Recognition using SUPPORT VECTOR MACHINE
✅Day-6: Titanic Survival Prediction using NAIVE BAYES
✅Day-7: Leaf Detection using DECISION TREE
✅Day-8: Handwritten digit recognition using RANDOM FOREST
✅Day-9: Evaluating Classification model Performance using CONFUSION
MATRIX, CAP CURVE ANALYSIS & ACCURACY PARADOX
✅Day-10: Classification Model Selection for Breast Cancer classification
✅Day-11: House Price Prediction using LINEAR REGRESSION Single Variable
✅Day-12: Exam Mark Prediction using LINEAR REGRESSION Multiple Variable
✅Day-13: Predicting the Previous salary of the New Employee using
POLYNOMIAL REGRESSION
✅Day-14: Stock price prediction using SUPPORT VECTOR REGRESSION
✅Day-15: Height Prediction from the Age using DECISION TREE REGRESSION
✅Day-16: Car price prediction using RANDOM FOREST
✅Day-17: Evaluating Regression model performance using R-SQUARED
INTUITION & ADJUSTED R-SQUARED INTUITION
✅Day-18: Regression Model Selection for Engine Energy prediction.

UNSUPERVISED LEARNING - CLUSTERING
✅Day-19: Identifying the Pattern of the Customer spent using K-MEANS
CLUSTERING
✅Day-20: Customer Spending analysis using HIERARCHICAL CLUSTERING
✅Day-21: Leaf types data visualization using PRINCIPLE COMPONENT
ANALYSIS
✅Day-22: Finding Similar Movie based on ranking using SINGULAR VALUE
DECOMPOSITION

UNSUPERVISED LEARNING - ASSOCIATION
✅Day-23: Market Basket Analysis using APIRIORI
✅Day-24: Market Basket Optimization/Analysis using ECLAT

REINFORCEMENT LEARNING
✅Day-25: Web Ads. Click through Rate optimization using UPPER BOUND
CONFIDENCE

Natural Language Processing
✅Day-26: Sentimental Analysis using Natural Language Processing
✅Day-27: Breast cancer Tumor prediction using XGBOOST

DEEP LEARNING
✅Day-28: Bank Customer classification using ANN
✅Day-29: Pima-Indians Diabetes Classification using CONVOLUTIONAL
NEURAL NETWORK
✅Day-30: A.I Snake Game using REINFORCEMENT LEARNING

Data Analytics

✅Day-1: Introduction to Artificial Intelligence, Data Analytics & Road Map to become a Data Scientist
EXCEL
✅Day-2: Data Preparation - Power Query & Tables
✅Day-3: Data analytics- Formula & Pivot Table
✅Day-4: Story Telling - Charts & Dashboard
✅Day-5: Automation - VBA Macros & Power Query
STATISTICS & PROBABILITY
✅Day-6: Descriptive Statistics - Mean, Mode, Median, Quartile, Range, InterQuartile Range, Standard Deviation
✅Day-7: Probability - Permutations, Combinations
✅Day-8: Population and Sampling
✅Day-9: Probability Distributions - Normal, Binomial and Poisson Distributions
✅Day-10: Hypothesis Testing & ANOVA - One Sample and Two Samples - z Test, t-Test, F Test and Chi-Square Test
BI tools - Tableu
✅Day-11: Connect Tableau to a Variety of Datasets
✅Day-12: Analyze, Blend, Join, and Calculate Data
✅Day-13: Visualize Data in the Form of Various Charts, Plots, and Maps
BI tools - Power BI
✅Day-14: Connect Tableau to a Variety of Datasets
✅Day-15: Visualize Data in the Form of Various Charts, Plots, and Maps and Calculate Data
Python
✅Day-16: Introduction to Python & Installing Python and its Libraries
✅Day-17: Basic Python Programming for Data Analytics
Numpy & Pandas
✅Day-18: Python Numpy functions
✅Day-19: Pandas for Data analytics in Python
Data Visualization
✅Day-20: Matplotlib for data visualization
✅Day-21: Seaborn for data visualization
Kaggle Exploratory
✅Day-22: Kaggle Dataset and Notebooks
Database - SQL
✅Day-23: SQL basics for Data analytics - Part-1
✅Day-24: SQL basics for Data analytics - Part-2
Database - MongoDB
✅Day-25: MongoDB basics for Data analytics
Machine Learning
✅Day-26: Introduction to Machine Learning & its libraries
✅Day-27: Evaluating and Deploying Machine Learning Classification algorithm for classification of State of Electric power system
Deep Learning
✅Day-28: Introduction to Deep Learning & its libraries
✅Day-29: Covid-19 Detection using X-Ray Images with CNN
Natural Language Processing
✅Day-30: Tag Identification system using NLTK

Deep Learning

Section 1: Course Overview
✅ DAY–1 Introduction to Deep Learning
✅ DAY–2 Basic Computer Vision

Section 2: Artificial Neural Network
✅ DAY–3 Neurons & Perceptron
✅ DAY–4 Activation Function
✅ DAY–5 Gradient Descent
✅ DAY – 6 Stochastic Gradient Descent
✅ DAY – 7 Backpropagation
✅ DAY – 8 Artificial Neural Network – Project 1

Section 3: Deep Neural Network
✅ DAY – 9 Optimization Algorithms – SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
✅ DAY – 10 Batch Normalization
✅ DAY– 11 Hyperparameter tuning
✅ DAY– 12 Interpretability
✅ DAY– 13 Deep Neural Network – Project 2
Section 4: Convolutional Neural Network
✅ DAY– 14 Convolutional Neural Network & its Layers
✅ DAY– 15 CNN Architecture
✅ Day–16 Different frameworks on Deep Learning (Tensorflow, Keras, PyTorch & Caffe)
✅ Day-17 Object Recognition using Pre Trained Model – Caffe – Project 3
✅ Day-18 Image classification using Convolutional Neural Network from Scratch – Tensorflow & Keras – Project 4
✅ Day-19 Custom Image Classification using Transfer Learning – Project 5
✅ Day-20 YOLO Object recognition – Project 6
✅ Day 21 Image Segmentation – Project 7
✅ Day 22 Project using MxNet – Project 8
✅ Day 23 Project using PyTorch – Project 9
✅ Day 24 Social Distancing detector – Project 10
✅ Day 25 Face Mask detector – Project 11

Section 5: Recurrent Neural Network
✅ Day 26 Introduction to RNN and LSTM
✅ Day 27 Project using RNN – Project 12

Section 6:
✅ Day 28 Introduction CUDA Toolkit and cuDNN for deep learning
✅ Day 29 Getting started with the Intel Movidius Neural Compute Stick – Project 13
✅ Day 30 Custom Object classification using Nvidia Jetson – Project 15