Master Data Science with Python

₹ 19,999

Master Data Science with Python

₹ 19,999

Opportunities unlocked for learner
You will learn one of the hottest fields of the 21st century and will get a Kickass Kickstart.Will be able to build Web Scrapers, Data Cleaning with python fundamentals.Will be able to apply various Machine Learning algorithms like Linear Regression, Logistic Regression, Decision Trees, Naive Bayes, Principal Component Analysis, Feature Engineering, T-SNE Visualizations, Deep Learning & Reinforcement Learning for video games.
Hands on Projects and Problems:
Web Crawler - will be able to build Spiders using Scrapy for Amazon, PepperFry etc.Pokedex- Will be able to classify Pokemons using Transfer Learning. Emoji Predictor - Will be able to predict emojis using Recurrent Neural Networks.Many More- Face Recognition,Odd One Out, Titanic Survivor Prediction,Handwritten Digit Recognition, Language Generator.
Why choose CB Live:
Two-way Interaction: Share screen with your mentor for any doubt resolution 24*7.Placement Support: From Interview recommendations to placement, we are there for you at every step.Get into Bigwigs: Our expert will help you crack interviews for Amazon, Microsoft, Apple, Google, and Facebook.
This course is essential for you:
Who has familiarity and ability to write code in any programming language.Who wants to work with bigwigs like Amazon, Google, Microsoft paying handsome salaries.Who wants to learn Data Analysis, Manipulation, and Visualization

Lectures Schedule

Mon, Wed, Fri, Sun | 7PM - 10PM

Syllabus
Lecture 01
Data Science Quickstart Mode
Lecture 02
Introduction to Python
Lecture 03
Conditions, Loops and functions
Lecture 04
String and other inbuilt data structures
Lecture 05
Files Handling
Lecture 06
Numpy
Lecture 07
Exploratory data analysis with Pandas
Lecture 08
Data visualization using Matplotlib
Lecture 09
Linear Regression
Lecture 10
Logistic Regression
Lecture 11
KNN and related Classification
Lecture 12
Face Recognition with KNN
Lecture 13
K-Means and related clustering
Lecture 14
Principle component Analysis (PCA)
Lecture 15
Decision tree, Random Forest
Lecture 16
Manual Ensembling Vs.Automated Ensembling
Lecture 17
Support Vector Machines
Lecture 18
Markov Chain
Lecture 19
NLTK and Naïve Bayes
Lecture 20
Web Scraping and web api (Flask)
Lecture 21
Intro to Neural Net and TensorFlow
Lecture 22
First DNN with Keras
Lecture 23
Functional Models
Lecture 24
CNN Intro
Lecture 25
Transfer Learning
Lecture 26
Auto Encoders
Lecture 27
Recurrent and Combined Architectures
Lecture 28
Time Series - Averages, Smoothening, AR Models
Lecture 29
Generative adversarial networks9
Lecture 30
Reinforcement learning and Gene10ic algorith30

Demo Class Recordings

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