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Machine Learning with Real World Projects
By Packt
- 30 Day Money Back Guarantee
- Completion Certificate
- 24/7 Technical Support
Highlights
On-Demand course
29 hours 47 minutes
All levels
Description
Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights
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.
All the codes and supporting files for this course are available at: https://github.com/PacktPublishing/Machine-Learning-with-Real-World-Projects
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
Approach
An exhaustive course packed with step-by-step instructions, working examples, and helpful advice. This course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.
Key Features
Learn Machine Learning with real-world case studies * Learn complex theory, algorithms and coding libraries in a very simple way
Github Repo
https://github.com/packtpublishing/machine-learning-with-real-world-projects
About the Author
Geekshub Pvt. Ltd.
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.
Course Outline
1. Simple Linear Regression
1. Installing Anaconda & using Jupyter Notebook Simple Linear Regression: Installing Anaconda & using Jupyter Notebook |
2. Introduction to Machine Learning Simple Linear Regression: Introduction to Machine Learning |
3. Types Of Machine Learning Simple Linear Regression: Types Of Machine Learning |
4. Introduction to Linear Regression (LR) Simple Linear Regression: Introduction to Linear Regression (LR) |
5. How LR Works Simple Linear Regression: How LR Works |
6. Some Fun with Maths Behind LR Simple Linear Regression: Some Fun with Maths Behind LR |
7. R Square Simple Linear Regression: R Square |
8. LR Case Study Part1 Simple Linear Regression: LR Case Study Part1 |
9. LR Case Study Part2 Simple Linear Regression: LR Case Study Part2 |
10. LR Case Study Part3 Simple Linear Regression: LR Case Study Part3 |
11. Residual Square Error (RSE) Simple Linear Regression: Residual Square Error (RSE) |
2. Multiple Linear Regression
1. Introduction Multiple Linear Regression: Introduction |
2. Case study Part1 Multiple Linear Regression: Case study Part1 |
3. Case study Part2 Multiple Linear Regression: Case study Part2 |
4. Case study Part3 Multiple Linear Regression: Case study Part3 |
5. Adjusted R Square Multiple Linear Regression: Adjusted R Square |
6. Case Study Part1 Multiple Linear Regression: Case Study Part1 |
7. Case Study Part2 Multiple Linear Regression: Case Study Part2 |
8. Case Study Part3 Multiple Linear Regression: Case Study Part3 |
9. Case Study Part4 Multiple Linear Regression: Case Study Part4 |
10. Case Study Part5 Multiple Linear Regression: Case Study Part5 |
11. Case study Part6 (RFE) Multiple Linear Regression: Case study Part6 (RFE) |
3. Hotstar, Netflix Real world Case Study for Multiple Linear Regression
1. Introduction to The Problem Statement Hotstar, Netflix Real world Case Study for Multiple Linear Regression: Introduction to The Problem Statement |
2. Playing with Data Hotstar, Netflix Real world Case Study for Multiple Linear Regression: Playing with Data |
3. Building Model Part1 Hotstar, Netflix Real world Case Study for Multiple Linear Regression: Building Model Part1 |
4. Building Model Part2 Hotstar, Netflix Real world Case Study for Multiple Linear Regression: Building Model Part2 |
5. Building Model Part3 Hotstar, Netflix Real world Case Study for Multiple Linear Regression: Building Model Part3 |
6. Verification of Model Hotstar, Netflix Real world Case Study for Multiple Linear Regression: Verification of Model |
4. Gradient Descent
1. Pre-req for Gradient Descent part1 Gradient Descent: Pre-req for Gradient Descent part1 |
2. Pre-req for Gradient Descent part2 Gradient Descent: Pre-req for Gradient Descent part2 |
3. Cost Functions Gradient Descent: Cost Functions |
4. Defining Cost Functions more formally Gradient Descent: Defining Cost Functions more formally |
5. Gradient Descent Gradient Descent: Gradient Descent |
6. Optimisation Gradient Descent: Optimisation |
7. Closed Form Vs Gradient Descent Gradient Descent: Closed Form Vs Gradient Descent |
8. Gradient Descent Case Study Gradient Descent: Gradient Descent Case Study |
5. KNN
1. Introduction to Classification KNN: Introduction to Classification |
2. Defining Classification Mathematically KNN: Defining Classification Mathematically |
3. Introduction To KNN KNN: Introduction To KNN |
4. Accuracy of KNN KNN: Accuracy of KNN |
5. Effectiveness of KNN KNN: Effectiveness of KNN |
6. Distance Metrics KNN: Distance Metrics |
7. Distance Metrics Part2 KNN: Distance Metrics Part2 |
8. Finding K KNN: Finding K |
9. KNN on Regression KNN: KNN on Regression |
10. Case Study KNN: Case Study |
11. Classification Case1 KNN: Classification Case1 |
12. Classification Case2 KNN: Classification Case2 |
13. Classification Case3 KNN: Classification Case3 |
14. Classification Case4 KNN: Classification Case4 |
6. Model Performance Metrics
1. Performance Metrics Part1 Model Performance Metrics: Performance Metrics Part1 |
2. Performance Metrics Part2 Model Performance Metrics: Performance Metrics Part2 |
3. Performance Metrics Part3 Model Performance Metrics: Performance Metrics Part3 |
7. Model Selection Part1
1. Model Creation Case1 Model Selection Part1: Model Creation Case1 |
2. Model Creation Case2 Model Selection Part1: Model Creation Case2 |
3. Grid Search Case Study Part1 Model Selection Part1: Grid Search Case Study Part1 |
4. Grid Search Case Study Part2 Model Selection Part1: Grid Search Case Study Part2 |
8. Naive Bayes
1. Introduction to Naive Bayes Naive Bayes: Introduction to Naive Bayes |
2. Bayes Theorem Naive Bayes: Bayes Theorem |
3. Practical Example from NB with One Column Naive Bayes: Practical Example from NB with One Column |
4. Practical Example from NB with Multiple Column Naive Bayes: Practical Example from NB with Multiple Column |
5. Naive Bayes on Text Data Part1 Naive Bayes: Naive Bayes on Text Data Part1 |
6. Naive Bayes on Text Data Part2 Naive Bayes: Naive Bayes on Text Data Part2 |
7. Laplace Smoothing Naive Bayes: Laplace Smoothing |
8. Bernoulli Naive Bayes Naive Bayes: Bernoulli Naive Bayes |
9. Case Study 1 Naive Bayes: Case Study 1 |
10. Case Study 2 Part1 Naive Bayes: Case Study 2 Part1 |
11. Case Study 2 Part2 Naive Bayes: Case Study 2 Part2 |
9. Logistic Regression
1. Introduction Logistic Regression: Introduction |
2. Sigmoid Function Logistic Regression: Sigmoid Function |
3. Log Odds Logistic Regression: Log Odds |
4. Case Study Logistic Regression: Case Study |
10. Support Vector Machine (SVM)
1. Introduction Support Vector Machine (SVM): Introduction |
2. Hyperplane Part1 Support Vector Machine (SVM): Hyperplane Part1 |
3. Hyperplane Part2 Support Vector Machine (SVM): Hyperplane Part2 |
4. Maths Behind SVM Support Vector Machine (SVM): Maths Behind SVM |
5. Support Vectors Support Vector Machine (SVM): Support Vectors |
6. Slack Variables Support Vector Machine (SVM): Slack Variables |
7. SVM Case Study Part1 Support Vector Machine (SVM): SVM Case Study Part1 |
8. SVM Case Study Part2 Support Vector Machine (SVM): SVM Case Study Part2 |
9. Kernel Part1 Support Vector Machine (SVM): Kernel Part1 |
10. Kernel Part2 Support Vector Machine (SVM): Kernel Part2 |
11. Case Study 2 Support Vector Machine (SVM): Case Study 2 |
12. Case Study 3: Part1 Support Vector Machine (SVM): Case Study 3: Part1 |
13. Case Study 3: Part2 Support Vector Machine (SVM): Case Study 3: Part2 |
14. Case Study 4 Support Vector Machine (SVM): Case Study 4 |
11. Decision Tree
1. Introduction Decision Tree: Introduction |
2. Example Of DT Decision Tree: Example Of DT |
3. Homogenity Decision Tree: Homogenity |
4. Gini Index Decision Tree: Gini Index |
5. Information Gain Part1 Decision Tree: Information Gain Part1 |
6. Information Gain Part2 Decision Tree: Information Gain Part2 |
7. Advantages and Disadvantages Of DT Decision Tree: Advantages and Disadvantages Of DT |
8. Preventing Overlifting Issues in DT Decision Tree: Preventing Overlifting Issues in DT |
9. DT Case Study Part1 Decision Tree: DT Case Study Part1 |
10. DT Case Study Part2 Decision Tree: DT Case Study Part2 |
12. Ensembling
1. Introduction to Ensembles Ensembling: Introduction to Ensembles |
2. Bagging Ensembling: Bagging |
3. Advantages Ensembling: Advantages |
4. Runtime Ensembling: Runtime |
5. Case study Ensembling: Case study |
6. Introduction to Boosting Ensembling: Introduction to Boosting |
7. Weak Learners Ensembling: Weak Learners |
8. Shallow Decision Tree Ensembling: Shallow Decision Tree |
9. Adaboost Part1 Ensembling: Adaboost Part1 |
10. Adaboost Part2 Ensembling: Adaboost Part2 |
11. Adaboost Case Study Ensembling: Adaboost Case Study |
12. XGboost Ensembling: XGboost |
13. Boosting Part1 Ensembling: Boosting Part1 |
14. Boosting Part2 Ensembling: Boosting Part2 |
15. Xgboost Algorithm Ensembling: Xgboost Algorithm |
16. Case Study Part1 Ensembling: Case Study Part1 |
17. Case Study Part2 Ensembling: Case Study Part2 |
18. Case Study Part3 Ensembling: Case Study Part3 |
13. Model Selection Part2
1. Model Selection Part1 Model Selection Part2: Model Selection Part1 |
2. Model Selection Part2 Model Selection Part2: Model Selection Part2 |
3. Model Selection Part3 Model Selection Part2: Model Selection Part3 |
14. Unsupervised Learning
1. Introduction to Clustering Unsupervised Learning: Introduction to Clustering |
2. Segmentation Unsupervised Learning: Segmentation |
3. Kmeans Unsupervised Learning: Kmeans |
4. Maths Behind Kmeans Unsupervised Learning: Maths Behind Kmeans |
5. More Maths Unsupervised Learning: More Maths |
6. Kmeans Plus Unsupervised Learning: Kmeans Plus |
7. Value of K Unsupervised Learning: Value of K |
8. Hopkins Test Unsupervised Learning: Hopkins Test |
9. Case Study Part1 Unsupervised Learning: Case Study Part1 |
10. Case Study Part2 Unsupervised Learning: Case Study Part2 |
11. More on Segmentation Unsupervised Learning: More on Segmentation |
12. Heirarchical Clustering Unsupervised Learning: Heirarchical Clustering |
13. Case Study Unsupervised Learning: Case Study |
15. Dimension Reduction
1. Introduction Dimension Reduction: Introduction |
2. PCA Dimension Reduction: PCA |
3. Maths Behind PCA Dimension Reduction: Maths Behind PCA |
4. Case Study Part1 Dimension Reduction: Case Study Part1 |
5. Case Study Part2 Dimension Reduction: Case Study Part2 |
16. Advanced Machine Learning Algorithms
1. Introduction Advanced Machine Learning Algorithms: Introduction |
2. Example Part1 Advanced Machine Learning Algorithms: Example Part1 |
3. Example Part2 Advanced Machine Learning Algorithms: Example Part2 |
4. Optimal Solution Advanced Machine Learning Algorithms: Optimal Solution |
5. Case Study Advanced Machine Learning Algorithms: Case Study |
6. Regularization Advanced Machine Learning Algorithms: Regularization |
7. Ridge and Lasso Advanced Machine Learning Algorithms: Ridge and Lasso |
8. Case Study Advanced Machine Learning Algorithms: Case Study |
9. Model Selection Advanced Machine Learning Algorithms: Model Selection |
10. Adjusted R Square Advanced Machine Learning Algorithms: Adjusted R Square |
17. Deep Learning
1. Expectations Deep Learning: Expectations |
2. Introduction Deep Learning: Introduction |
3. History Deep Learning: History |
4. Perceptron Deep Learning: Perceptron |
5. Multi Layered Perceptron Deep Learning: Multi Layered Perceptron |
6. Neural Network Playground Deep Learning: Neural Network Playground |
18. Project - Medical Treatment
1. Introduction to Problem Statement Project - Medical Treatment: Introduction to Problem Statement |
2. Playing with Data Project - Medical Treatment: Playing with Data |
3. Translating the Problem into Machine Learning World Project - Medical Treatment: Translating the Problem into Machine Learning World |
4. Dealing with Text Data Project - Medical Treatment: Dealing with Text Data |
5. Train, Test and Cross Validation Split Project - Medical Treatment: Train, Test and Cross Validation Split |
6. Understanding Evaluation Matrix: Log Loss Project - Medical Treatment: Understanding Evaluation Matrix: Log Loss |
7. Building a Worst Model Project - Medical Treatment: Building a Worst Model |
8. Evaluating a Worst ML Model Project - Medical Treatment: Evaluating a Worst ML Model |
9. First Categorical column Analysis Project - Medical Treatment: First Categorical column Analysis |
10. Response Encoding and One Hot Encoder Project - Medical Treatment: Response Encoding and One Hot Encoder |
11. Laplace Smoothing and Calibrated classifier Project - Medical Treatment: Laplace Smoothing and Calibrated classifier |
12. Significance of first categorical column Project - Medical Treatment: Significance of first categorical column |
13. Second Categorical column Project - Medical Treatment: Second Categorical column |
14. Third Categorical column Project - Medical Treatment: Third Categorical column |
15. Data pre-processing before building machine learning model Project - Medical Treatment: Data pre-processing before building machine learning model |
16. Building Machine Learning model Part1 Project - Medical Treatment: Building Machine Learning model Part1 |
17. Building Machine Learning model Part2 Project - Medical Treatment: Building Machine Learning model Part2 |
18. Building Machine Learning model Part3 Project - Medical Treatment: Building Machine Learning model Part3 |
19. Building Machine Learning model Part4 Project - Medical Treatment: Building Machine Learning model Part4 |
20. Building Machine Learning model Part5 Project - Medical Treatment: Building Machine Learning model Part5 |
21. Building Machine Learning model Part6 Project - Medical Treatment: Building Machine Learning model Part6 |
19. Project - Quora Project
1. Quora Introduction Project - Quora Project: Quora Introduction |
2. Quora Data Project - Quora Project: Quora Data |
3. Quora Understanding ML Project - Quora Project: Quora Understanding ML |
4. Quora Data Distribution Project - Quora Project: Quora Data Distribution |
5. Quora Datalist Project - Quora Project: Quora Datalist |
6. Quora Basic Feature Engineering Project - Quora Project: Quora Basic Feature Engineering |
7. Quora Text Project - Quora Project: Quora Text |
8. Advanced Feature Engineering Part1 Project - Quora Project: Advanced Feature Engineering Part1 |
9. Advanced Feature Engineering Part2 Project - Quora Project: Advanced Feature Engineering Part2 |
10. Advanced Feature Engineering Part3 Project - Quora Project: Advanced Feature Engineering Part3 |
11. Advanced Feature Engineering Part4 Project - Quora Project: Advanced Feature Engineering Part4 |
12. Quora Advance Feature Analysis Project - Quora Project: Quora Advance Feature Analysis |
13. Featuring Text Data with TF-IDF Weighted Word2Vec Project - Quora Project: Featuring Text Data with TF-IDF Weighted Word2Vec |
14. Building Machine Learning Models - Part 1 Project - Quora Project: Building Machine Learning Models - Part 1 |
15. Building Machine Learning Models - Part 2 Project - Quora Project: Building Machine Learning Models - Part 2 |
20. Real World Problem - Investment Requirement Analysis for a Company
1. Investment Project Brief Real World Problem - Investment Requirement Analysis for a Company: Investment Project Brief |
2. Investment Project_Data Cleaning Part 1 Real World Problem - Investment Requirement Analysis for a Company: Investment Project_Data Cleaning Part 1 |
3. Investment Project_Data Cleaning - II Part 2 Real World Problem - Investment Requirement Analysis for a Company: Investment Project_Data Cleaning - II Part 2 |
4. Investment Project_Funding_Country_Sector Analysis Part 1 Real World Problem - Investment Requirement Analysis for a Company: Investment Project_Funding_Country_Sector Analysis Part 1 |
5. Investment Project_Funding_Country_Sector Analysis Part 2 Real World Problem - Investment Requirement Analysis for a Company: Investment Project_Funding_Country_Sector Analysis Part 2 |
21. Loan Analysis Project
1. Problem Statement Loan Analysis Project: Problem Statement |
2. Lending Club Default Analysis - Data Understanding and Data Cleaning Loan Analysis Project: Lending Club Default Analysis - Data Understanding and Data Cleaning |
3. Data Analysis - Univariate & Bivariate Analysis Loan Analysis Project: Data Analysis - Univariate & Bivariate Analysis |
4. Segmented Univariate Analysis Loan Analysis Project: Segmented Univariate Analysis |
22. Car Project
1. Problem Statement Car Project: Problem Statement |
2. Data Understanding and Exploration Car Project: Data Understanding and Exploration |
3. Data Cleaning & Data Preparation Car Project: Data Cleaning & Data Preparation |
4. Model Building and Evaluation Car Project: Model Building and Evaluation |
5. Final Model Evaluation Car Project: Final Model Evaluation |
23. Stack Overflow Project - Facebook Recruitment
1. Problem Statement Stack Overflow Project - Facebook Recruitment: Problem Statement |
2. Performance Metric Stack Overflow Project - Facebook Recruitment: Performance Metric |
3. Hamming Loss Stack Overflow Project - Facebook Recruitment: Hamming Loss |
4. Analysis of Tags Stack Overflow Project - Facebook Recruitment: Analysis of Tags |
5. Problem - Multi Label Part1 Stack Overflow Project - Facebook Recruitment: Problem - Multi Label Part1 |
6. Problem - Multi Label Part2 Stack Overflow Project - Facebook Recruitment: Problem - Multi Label Part2 |
7. Problem_Apply Logistic Regression with OnevsRest Classifier Stack Overflow Project - Facebook Recruitment: Problem_Apply Logistic Regression with OnevsRest Classifier |
8. Problem_Final Stack Overflow Project - Facebook Recruitment: Problem_Final |
Course Content
- Machine Learning with Real World Projects
About The Provider
Packt
Birmingham
Founded in 2004 in Birmingham, UK, Packt’s mission is to help the world put software to work in new ways, through the delivery of effective learning and i...
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