Machine Learning for Developers – 5 Day Virtual Workshop → Mathematical modelling of Regression Model

Course Content

Total learning: 112 lessons / 5 quizzes Time: 3 days

Day 01 - Module-1 Introduction to ML 0/8
- Lecture1.1
  
  Artificial Intelligence & Machine Learning Introduction
- Lecture1.2
  
  Who uses AI?
- Lecture1.3
  
  AI and ML for telecom
- Lecture1.4
  
  Supervised & Unsupervised Learning
- Lecture1.5
  
  Regression & Classification Problems
- Lecture1.6
  
  What makes a Machine Learning Expert?
- Lecture1.7
  
  What to learn to become a Machine Learning Developer?
- Lecture1.8
  
  Overview of Machine Learning Algorithms
Module-2 Introduction to Python 0/4
- Lecture2.1
  
  Basic syntax
- Lecture2.2
  
  Data structures in Python
- Lecture2.3
  
  Functions
- Lecture2.4
  
  Object oriented Programming
Module-3 Data Structure & Data Manipulation 0/9
- Lecture3.1
  
  Indexing, Data Processing using Numpy Arrays and Pandas
- Lecture3.2
  
  Mathematical computing basics
- Lecture3.3
  
  Basic statistics
- Lecture3.4
  
  File Input and Output
- Lecture3.5
  
  Getting Started with Dataframes
- Lecture3.6
  
  Data Acquisition (Import & Export)
- Lecture3.7
  
  Selection and Filtering
- Lecture3.8
  
  Combining and Merging Data Frames
- Lecture3.9
  
  Removing Duplicates & String Manipulation
Module-4 Data Visualization 0/6
- Lecture4.1
  
  Data pre processing
- Lecture4.2
  
  Data cleaning and data transformation
- Lecture4.3
  
  Feature extraction
- Lecture4.4
  
  Recursive feature elimination
- Lecture4.5
  
  Feature transformation
- Lecture4.6
  
  Discussion on mining massive data sets
Day 02 - Module-1 Linear Regression - Case Study & Project 0/7
- Lecture5.1
  
  Regression Problem Analysis
- Lecture5.2
  
  Mathematical modelling of Regression Model
- Lecture5.3
  
  Gradient Descent Algorithm
- Lecture5.4
  
  Use cases
- Lecture5.5
  
  Regression Table
- Lecture5.6
  
  Building simple Univariate Linear Regression Model
- Lecture5.7
  
  Multivariate Regression Model
Module-2 Linear Regression - 2 0/3
- Lecture6.1
  
  Polynomial Linear Regression
- Lecture6.2
  
  Ridge Regression
- Lecture6.3
  
  Lasso Regression
Module-3 Logistic Regression 0/13
- Lecture7.1
  
  Assumptions
- Lecture7.2
  
  Reason for the Logit Transform
- Lecture7.3
  
  Logit Transformation
- Lecture7.4
  
  Hypothesis
- Lecture7.5
  
  Variable and Model Significance
- Lecture7.6
  
  Maximum Likelihood Concept
- Lecture7.7
  
  Log Odds and Interpretation
- Lecture7.8
  
  Null Vs Residual Deviance
- Lecture7.9
  
  Chi Square Test
- Lecture7.10
  
  ROC Curve
- Lecture7.11
  
  Model Specification
- Lecture7.12
  
  Confusion Matrix
- Lecture7.13
  
  Accuracy, Recall, Precision and F1 Score
Day 03 - Module-1 Decision Trees 0/4
- Lecture8.1
  
  Forming a Decision Tree
- Lecture8.2
  
  Components of Decision Tree
- Lecture8.3
  
  Mathematics of Decision Tree
- Lecture8.4
  
  Decision Tree Evaluation for use cases
Module-2 Random Forests 0/2
- Lecture9.1
  
  Random Forest Mathematics
- Lecture9.2
  
  Examples & use cases using Random Forests
Module-3 KNN 0/3
- Lecture10.1
  
  KNN Theory
- Lecture10.2
  
  KNN with Python
- Lecture10.3
  
  KNN Exercise with real data
Module-4 Bagging and Boosting 0/5
- Lecture11.1
  
  Bias Variance Tradeoffs
- Lecture11.2
  
  Bagging
- Lecture11.3
  
  Boosting
- Lecture11.4
  
  Bootstrapping
- Lecture11.5
  
  Ensemble models with real world data
Day 04 - Module-1 Naïve Bayes Algorithm 0/8
- Lecture12.1
  
  Bayesian Theorem
- Lecture12.2
  
  Probabilities – The Prior and Posterior Probabilities
- Lecture12.3
  
  Conditional and Joint Probabilities Notion
- Quiz12.1
  
  Traditional Approach – Extract Important Features 0 question10 min
- Quiz12.2
  
  Naive Approach – Independence of Features Assumption 0 question10 min
- Quiz12.3
  
  Data Processing – Discretization of Features 0 question10 min
- Quiz12.4
  
  Advantages & Disadvantages of Naïve Bayes Models 0 question10 min
- Quiz12.5
  
  Examples, Projects & Case Studies 0 question10 min
Module-2 Support Vector Machine 0/4
- Lecture13.1
  
  Concept and Working Principle
- Lecture13.2
  
  Mathematical Modelling
- Lecture13.3
  
  The Kernel Method and Nonlinear Hyperplanes
- Lecture13.4
  
  Use Cases & Programming SVM using Python
Module-3 Recommendation Systems 0/2
- Lecture14.1
  
  Market basket Analysis
- Lecture14.2
  
  Collaborative Filtering
Day 05 - Module-1 Clustering 0/5
- Lecture15.1
  
  Hierarchical Clustering
- Lecture15.2
  
  K Means Clustering
- Lecture15.3
  
  Use Cases for K Means Clustering
- Lecture15.4
  
  Programming for K Means using Python
- Lecture15.5
  
  Cluster Size Optimization vs Definition Optimization
Module-2 Time Series 0/9
- Lecture16.1
  
  Definition of Time Series
- Lecture16.2
  
  Time Series Decomposition
- Lecture16.3
  
  Simple Moving Average Method
- Lecture16.4
  
  Weighted Moving Average Method
- Lecture16.5
  
  Single Exponential Smoothing Method
- Lecture16.6
  
  Double Exponential Smoothing Method
- Lecture16.7
  
  Triple Exponential Smoothing Method
- Lecture16.8
  
  Stationarity of Data
- Lecture16.9
  
  ARIMA Models
Module-3 Reinforcement Learning 0/3
- Lecture17.1
  
  Upper Confidence Bounds
- Lecture17.2
  
  Thompson Sampling
- Lecture17.3
  
  Example and use cases
Day 06 - Module-1 Artificial Neural Networks 0/8
- Lecture18.1
  
  Activation Functions for Neural Networks
- Lecture18.2
  
  Optimization Techniques – SGD, ADAM, LBFGS
- Lecture18.3
  
  Regularization
- Lecture18.4
  
  Momentum in Neural Networks
- Lecture18.5
  
  Neural Network Tuning and Performance Optimization
- Lecture18.6
  
  Perceptrons, Multilayer Perceptrons, Weights, Biases,Backpropagation,Optimization
- Lecture18.7
  
  Training, Validation and Testing
- Lecture18.8
  
  End-to-end development of a multiclass classification problem in Python/MATLAB
Module-2 Convolutional Neural Networks 0/7
- Lecture19.1
  
  CNN Architecture
- Lecture19.2
  
  Introduction to Deep Learning, History, Applications
- Lecture19.3
  
  Shallow Networks and Deep Networks
- Lecture19.4
  
  Convolutional Neural Networks, Pooling layers, Fully Connected layers, Activation functions, ReLU, Dropout, loss function and Optimization with respect to CNN
- Lecture19.5
  
  Introduction to frameworks Tensorflow, Keras
- Lecture19.6
  
  Image classification using convolutional neural networks, visualisation of features
- Lecture19.7
  
  MNIST Data – Digit Classification using CNN
Module-3 Recurrent and Recursive Nets 0/4
- Lecture20.1
  
  Basic concepts of RNN
- Lecture20.2
  
  The Vanishing Gradient Problem
- Lecture20.3
  
  LSTM Networks
- Lecture20.4
  
  Recursive Neural Networks
Module-4 Machine learning at scale 0/3
- Lecture21.1
  
  AWS ML Offerings
- Lecture21.2
  
  Google mL Offerings
- Lecture21.3
  
  On- Premised Deployments

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Machine Learning for Developers – 5 Day Virtual Workshop

Course Content

Day 01 - Module-1 Introduction to ML 0/8

Module-2 Introduction to Python 0/4

Module-3 Data Structure & Data Manipulation 0/9

Module-4 Data Visualization 0/6

Day 02 - Module-1 Linear Regression - Case Study & Project 0/7

Module-2 Linear Regression - 2 0/3

Module-3 Logistic Regression 0/13

Day 03 - Module-1 Decision Trees 0/4

Module-2 Random Forests 0/2

Module-3 KNN 0/3

Module-4 Bagging and Boosting 0/5

Day 04 - Module-1 Naïve Bayes Algorithm 0/8

Module-2 Support Vector Machine 0/4

Module-3 Recommendation Systems 0/2

Day 05 - Module-1 Clustering 0/5

Module-2 Time Series 0/9

Module-3 Reinforcement Learning 0/3

Day 06 - Module-1 Artificial Neural Networks 0/8

Module-2 Convolutional Neural Networks 0/7

Module-3 Recurrent and Recursive Nets 0/4

Module-4 Machine learning at scale 0/3