Last Update: 10/2021
Duration: 46h 10m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 22.9 GB
Genre: eLearning | Language: English
Build & Deploy Data Science, ML, Deep Learning Projects Course(Python, Flask, Django, AWS, Azure, GCP, Heruko Cloud)
What you'll learn:
Make robust Machine Learning models
Understand the full product workflow for the machine learning lifecycle.
Real life case studies and projects to understand how things are done in the real world
Know which Machine Learning model to choose for each type of problem
Learn how to program in Python using the latest Python 3
Learn to pre process data, clean data, and analyze large data
Learn to use NumPy for Numerical Data
Learn to use Pandas for Data Analysis
Have a great intuition of many Machine Learning models
Requirements:
Some prior coding or python scripting experience is required.
Basic Knowledge of machine learning & data science
Description:
In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud).
Data science can be defined as a blend of mathematics, business acumen, tools, algorithms, and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.
In data science, one deals with both structured and unstructured data. The algorithms also involve predictive analytics. Thus, data science is all about the present and future. That is, finding out the trends based on historical data which can be useful for present decisions, and finding patterns that can be modeled and can be used for predictions to see what things may look like in the future.
Data Science is an amalgamation of Statistics, Tools, and Business knowledge. So, it becomes imperative for a Data Scientist to have good knowledge and understanding of these.
With the amount of data that is being generated and the evolution in the field of Analytics, Data Science has turned out to be a necessity for companies. To make the most out of their data, companies from all domains, be it Finance, Marketing, Retail, IT or Bank. All are looking for Data Scientists. This has led to a huge demand for Data Scientists all over the globe. With the kind of salary that a company has to offer and IBM is declaring it as the trending job of the 21st century, it is a lucrative job for many. This field is such that anyone from any background can make a career as a Data Scientist.
In This Course, We Are Going To Work On 50 Real World Projects Listed Below:
Project-1: Pan Card Tempering Detector App -Deploy On Heroku
Project-2: Dog breed prediction Flask App
Project-3: Image Watermarking App -Deploy On Heroku
Project-4: Traffic sign classification
Project-5: Text Extraction From Images Application
Project-6: Plant Disease Prediction Streamlit App
Project-7: Vehicle Detection And Counting Flask App
Project-8: Create A Face Swapping Flask App
Project-9: Bird Species Prediction Flask App
Project-10: Intel Image Classification Flask App
Project-11: Language Translator App Using IBM Cloud Service -Deploy On Heroku
Project-12: Predict Views On Advertisement Using IBM Watson -Deploy On Heroku
Project-13: Laptop Price Predictor -Deploy On Heroku
Project-14: WhatsApp Text Analyzer -Deploy On Heroku
Project-15: Course Recommendation System -Deploy On Heroku
Project-16: IPL Match Win Predictor -Deploy On Heroku
Project-17: Body Fat Estimator App -Deploy On Microsoft Azure
Project-18: Campus Placement Predictor App -Deploy On Microsoft Azure
Project-19: Car Acceptability Predictor -Deploy On Google Cloud
Project-20: Book Genre Classification App -Deploy On Amazon Web Services
Project-21: Sentiment Analysis Django App -Deploy On Heroku
Project-22: Attrition Rate Django Application
Project-23: Find Legendary Pokemon Django App -Deploy On Heroku
Project-24: Face Detection Streamlit App
Project-25: Cats Vs Dogs Classification Flask App
Project-26: Customer Revenue Prediction App -Deploy On Heroku
Project-27: Gender From Voice Prediction App -Deploy On Heroku
Project-28: Restaurant Recommendation System
Project-29: Happiness Ranking Django App -Deploy On Heroku
Project-30: Forest Fire Prediction Django App -Deploy On Heroku
Project-31: Build Car Prices Prediction App -Deploy On Heroku
Project-32: Build Affair Count Django App -Deploy On Heroku
Project-33: Build Shrooming Predictions App -Deploy On Heroku
Project-34: Google Play App Rating prediction With Deployment On Heroku
Project-35: Build Bank Customers Predictions Django App -Deploy On Heroku
Project-36: Build Artist Sculpture Cost Prediction Django App -Deploy On Heroku
Project-37: Build Medical Cost Predictions Django App -Deploy On Heroku
Project-38: Phishing Webpages Classification Django App -Deploy On Heroku
Project-39: Clothing Fit-Size predictions Django App -Deploy On Heroku
Project-40: Build Similarity In-Text Django App -Deploy On Heroku
Project-41: Heart Attack Risk Prediction Using Eval ML (Auto ML)
Project-42: Credit Card Fraud Detection Using Pycaret (Auto ML)
Project-43: Flight Fare Prediction Using Auto SK Learn (Auto ML)
Project-44: Petrol Price Forecasting Using Auto Keras
Project-45: Bank Customer Churn Prediction Using H2O Auto ML
Project-46: Air Quality Index Predictor Using TPOT With End-To-End Deployment (Auto ML)
Project-47: Rain Prediction Using ML models & PyCaret With Deployment (Auto ML)
Project-48: Pizza Price Prediction Using ML And EVALML(Auto ML)
Project-49: IPL Cricket Score Prediction Using TPOT (Auto ML)
Project-50: Predicting Bike Rentals Count Using ML And H2O Auto ML
Tip: Create A 50 Days Study Plan, Spend 1-2hrs Per Day, Build 50 Projects In 50 Days.
The Only Course You Need To Become A Data Scientist, Get Hired And Start A New Career
Note (Read This): This Course Is Worth Of Your Time And Money, Enroll Now Before Offer Expires.Who this course is for:Anyone who is beginner in data science.
Who this course is for:
Anyone who is beginner in data science.
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