Video description
Want to become a good Data Scientist? Then this is a right course for you.
This course has been designed by IIT professionals who have mastered in Mathematics and Data Science. We will be covering complex theory, algorithms and coding libraries in a very simple way which can be easily grasped by any beginner as well.
We will walk you stepbystep into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative subfield of Data Science from beginner to advance level.
We have solved few Kaggle problems during this course and provided complete solutions so that students can easily compete in real world competition websites.
What You Will Learn
 Master Machine Learning in Python
 Learn to use MatplotLib for Python Plotting
 Learn to use Numpy and Pandas for Data Analysis
 Learn to use Seaborn for Statistical Plots
 Learn All the Mathematics Required to understand Machine Learning Algorithms
 Implement Machine Learning Algorithms along with Mathematic intuitions
 Projects of Kaggle Level are included with Complete Solutions
 Learning End to End Data Science Solutions
 All Advanced Level Machine Learning Algorithms and Techniques like Regularisations, Boosting, Bagging and many more included
 Learn All Statistical concepts To Make You Ninza in Machine Learning
 RealWorld Case Studies
 Model Performance Metrics
 Deep Learning
 Model Selection
Audience
Anyone who wants to build his career in Data Science / Machine Learning
About The Author
Teclov: Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skillset to their customers to make them jobready and prepare them to crack any challenge. They have the best trainers for cuttingedge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated realworld case studies that calibrate the learning experience of students.
Table of contents
 Chapter 1 : Simple Linear Regression
 Chapter 2 : Multiple Linear Regression
 Chapter 3 : Hotstar, Netflix Real world Case Study for Multiple Linear Regression
 Chapter 4 : Gradient Descent
 Chapter 5 : KNN
 Chapter 6 : Model Performance Metrics
 Chapter 7 : Model Selection Part1
 Chapter 8 : Naive Bayes
 Chapter 9 : Logistic Regression
 Chapter 10 : Support Vector Machine (SVM)
 Chapter 11 : Decision Tree
 Chapter 12 : Ensembling
 Chapter 13 : Model Selection Part2
 Chapter 14 : Unsupervised Learning
 Chapter 15 : Dimension Reduction
 Chapter 16 : Advanced Machine Learning Algorithms
 Chapter 17 : Deep Learning

Chapter 18 : Project  Medical Treatment
 Introduction to Problem Statement
 Playing with Data
 Translating the Problem into Machine Learning World
 Dealing with Text Data
 Train, Test and Cross Validation Split
 Understanding Evaluation Matrix: Log Loss
 Building a Worst Model
 Evaluating a Worst ML Model
 First Categorical column Analysis
 Response Encoding and One Hot Encoder
 Laplace Smoothing and Calibrated classifier
 Significance of first categorical column
 Second Categorical column
 Third Categorical column
 Data preprocessing before building machine learning model
 Building Machine Learning model Part1
 Building Machine Learning model Part2
 Building Machine Learning model Part3
 Building Machine Learning model Part4
 Building Machine Learning model Part5
 Building Machine Learning model Part6

Chapter 19 : Project  Quora Project
 Quora Introduction
 Quora Data
 Quora Understanding ML
 Quora Data Distribution
 Quora Datalist
 Quora Basic Feature Engineering
 Quora Text
 Advanced Feature Engineering Part1
 Advanced Feature Engineering Part2
 Advanced Feature Engineering Part3
 Advanced Feature Engineering Part4
 Quora Advance Feature Analysis
 Featuring Text Data with TFIDF Weighted Word2Vec
 Building Machine Learning Models  Part 1
 Building Machine Learning Models  Part 2
 Chapter 20 : Real World Problem  Investment Requirement Analysis for a Company
 Chapter 21 : Loan Analysis Project
 Chapter 22 : Car Project
 Chapter 23 : Stack Overflow Project  Facebook Recruitment
Product information
 Title: Machine Learning with Real World Projects
 Author(s):
 Release date: July 2019
 Publisher(s): Packt Publishing
 ISBN: 9781838985363
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