Machine Learning with Real World Projects

Machine Learning with Real World Projects

by Teclov
Released July 2019
Publisher(s): Packt Publishing
ISBN: 9781838985363

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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 step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field 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
  • Real-World 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 skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge 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 real-world case studies that calibrate the learning experience of students.

Table of contents

  1. Chapter 1 : Simple Linear Regression
    1. Installing Anaconda using Jupyter Notebook
    2. Introduction to Machine Learning
    3. Types Of Machine Learning
    4. Introduction to Linear Regression (LR)
    5. How LR Works
    6. Some Fun with Maths Behind LR
    7. R Square
    8. LR Case Study Part1
    9. LR Case Study Part2
    10. LR Case Study Part3
    11. Residual Square Error (RSE)
  2. Chapter 2 : Multiple Linear Regression
    1. Introduction
    2. Case study Part1
    3. Case study Part2
    4. Case study Part3
    5. Adjusted R Square
    6. Case Study Part1
    7. Case Study Part2
    8. Case Study Part3
    9. Case Study Part4
    10. Case Study Part5
    11. Case study Part6 (RFE)
  3. Chapter 3 : Hotstar, Netflix Real world Case Study for Multiple Linear Regression
    1. Introduction to The Problem Statement
    2. Playing with Data
    3. Building Model Part1
    4. Building Model Part2
    5. Building Model Part3
    6. Verification of Model
  4. Chapter 4 : Gradient Descent
    1. Pre-req for Gradient Descent part1
    2. Pre-req for Gradient Descent part2
    3. Cost Functions
    4. Defining Cost Functions more formally
    5. Gradient Descent
    6. Optimisation
    7. Closed Form Vs Gradient Descent
    8. Gradient Descent Case Study
  5. Chapter 5 : KNN
    1. Introduction to Classification
    2. Defining Classification Mathematically
    3. Introduction To KNN
    4. Accuracy of KNN
    5. Effectiveness of KNN
    6. Distance Metrics
    7. Distance Metrics Part2
    8. Finding K
    9. KNN on Regression
    10. Case Study
    11. Classification Case1
    12. Classification Case2
    13. Classification Case3
    14. Classification Case4
  6. Chapter 6 : Model Performance Metrics
    1. Performance Metrics Part1
    2. Performance Metrics Part2
    3. Performance Metrics Part3
  7. Chapter 7 : Model Selection Part1
    1. Model Creation Case1
    2. Model Creation Case2
    3. Grid Search Case Study Part1
    4. Grid Search Case Study Part2
  8. Chapter 8 : Naive Bayes
    1. Introduction to Naive Bayes
    2. Bayes Theorem
    3. Practical Example from NB with One Column
    4. Practical Example from NB with Multiple Column
    5. Naive Bayes on Text Data Part1
    6. Naive Bayes on Text Data Part2
    7. Laplace Smoothing
    8. Bernoulli Naive Bayes
    9. Case Study 1
    10. Case Study 2 Part1
    11. Case Study 2 Part2
  9. Chapter 9 : Logistic Regression
    1. Introduction
    2. Sigmoid Function
    3. Log Odds
    4. Case Study
  10. Chapter 10 : Support Vector Machine (SVM)
    1. Introduction
    2. Hyperplane Part1
    3. Hyperplane Part2
    4. Maths Behind SVM
    5. Support Vectors
    6. Slack Variables
    7. SVM Case Study Part1
    8. SVM Case Study Part2
    9. Kernel Part1
    10. Kernel Part2
    11. Case Study 2
    12. Case Study 3: Part1
    13. Case Study 3: Part2
    14. Case Study 4
  11. Chapter 11 : Decision Tree
    1. Introduction
    2. Example Of DT
    3. Homogenity
    4. Gini Index
    5. Information Gain Part1
    6. Information Gain Part2
    7. Advantages and Disadvantages Of DT
    8. Preventing Overlifting Issues in DT
    9. DT Case Study Part1
    10. DT Case Study Part2
  12. Chapter 12 : Ensembling
    1. Introduction to Ensembles
    2. Bagging
    3. Advantages
    4. Runtime
    5. Case study
    6. Introduction to Boosting
    7. Weak Learners
    8. Shallow Decision Tree
    9. Adaboost Part1
    10. Adaboost Part2
    11. Adaboost Case Study
    12. XGboost
    13. Boosting Part1
    14. Boosting Part2
    15. Xgboost Algorithm
    16. Case Study Part1
    17. Case Study Part2
    18. Case Study Part3
  13. Chapter 13 : Model Selection Part2
    1. Model Selection Part1
    2. Model Selection Part2
    3. Model Selection Part3
  14. Chapter 14 : Unsupervised Learning
    1. Introduction to Clustering
    2. Segmentation
    3. Kmeans
    4. Maths Behind Kmeans
    5. More Maths
    6. Kmeans Plus
    7. Value of K
    8. Hopkins Test
    9. Case Study Part1
    10. Case Study Part2
    11. More on Segmentation
    12. Heirarchical Clustering
    13. Case Study
  15. Chapter 15 : Dimension Reduction
    1. Introduction
    2. PCA
    3. Maths Behind PCA
    4. Case Study Part1
    5. Case Study Part2
  16. Chapter 16 : Advanced Machine Learning Algorithms
    1. Introduction
    2. Example Part1
    3. Example Part2
    4. Optimal Solution
    5. Case Study
    6. Regularization
    7. Ridge and Lasso
    8. Case Study
    9. Model Selection
    10. Adjusted R Square
  17. Chapter 17 : Deep Learning
    1. Expectations
    2. Introduction
    3. History
    4. Perceptron
    5. Multi Layered Perceptron
    6. Neural Network Playground
  18. Chapter 18 : Project - Medical Treatment
    1. Introduction to Problem Statement
    2. Playing with Data
    3. Translating the Problem into Machine Learning World
    4. Dealing with Text Data
    5. Train, Test and Cross Validation Split
    6. Understanding Evaluation Matrix: Log Loss
    7. Building a Worst Model
    8. Evaluating a Worst ML Model
    9. First Categorical column Analysis
    10. Response Encoding and One Hot Encoder
    11. Laplace Smoothing and Calibrated classifier
    12. Significance of first categorical column
    13. Second Categorical column
    14. Third Categorical column
    15. Data pre-processing before building machine learning model
    16. Building Machine Learning model Part1
    17. Building Machine Learning model Part2
    18. Building Machine Learning model Part3
    19. Building Machine Learning model Part4
    20. Building Machine Learning model Part5
    21. Building Machine Learning model Part6
  19. Chapter 19 : Project - Quora Project
    1. Quora Introduction
    2. Quora Data
    3. Quora Understanding ML
    4. Quora Data Distribution
    5. Quora Datalist
    6. Quora Basic Feature Engineering
    7. Quora Text
    8. Advanced Feature Engineering Part1
    9. Advanced Feature Engineering Part2
    10. Advanced Feature Engineering Part3
    11. Advanced Feature Engineering Part4
    12. Quora Advance Feature Analysis
    13. Featuring Text Data with TF-IDF Weighted Word2Vec
    14. Building Machine Learning Models - Part 1
    15. Building Machine Learning Models - Part 2
  20. Chapter 20 : Real World Problem - Investment Requirement Analysis for a Company
    1. Investment Project Brief
    2. Investment Project_Data Cleaning Part 1
    3. Investment Project_Data Cleaning - II Part 2
    4. Investment Project_Funding_Country_Sector Analysis Part 1
    5. Investment Project_Funding_Country_Sector Analysis Part 2
  21. Chapter 21 : Loan Analysis Project
    1. Problem Statement
    2. Lending Club Default Analysis - Data Understanding and Data Cleaning
    3. Data Analysis - Univariate Bivariate Analysis
    4. Segmented Univariate Analysis
  22. Chapter 22 : Car Project
    1. Problem Statement
    2. Data Understanding and Exploration
    3. Data Cleaning Data Preparation
    4. Model Building and Evaluation
    5. Final Model Evaluation
  23. Chapter 23 : Stack Overflow Project - Facebook Recruitment
    1. Problem Statement
    2. Performance Metric
    3. Hamming Loss
    4. Analysis of Tags
    5. Problem - Multi Label Part1
    6. Problem - Multi Label Part2
    7. Problem_Apply Logistic Regression with OnevsRest Classifier
    8. Problem_Final

Product information

  • Title: Machine Learning with Real World Projects
  • Author(s): Teclov
  • Release date: July 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781838985363