Curriculum
- 7 Sections
- 103 Lessons
- 20 Weeks
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- Statistics12
- 1.11.0 Descriptive Statistics – Measures of Central Tendency
- 1.2Quiz 1 : Types of Data10 Minutes2 Questions
- 1.32.0 Descriptive Statistics – Measures of Dispersion Part 1
- 1.4Quiz 2: Levels of Measurement10 Minutes2 Questions
- 1.53.0 Measures of Dispersion Part 2
- 1.6Quiz 3: Categorical Variables – Visualization Techniques10 Minutes1 Question
- 1.74.0 Measures Of Dispersion – Part 3
- 1.8Quiz 4: Numerical Variables – Frequency Distribution Table10 Minutes1 Question
- 1.95.0 Shape Measures
- 1.10Quiz 5 : Standard Deviation10 Minutes1 Question
- 1.11Descriptive Statistics Study Material
- 1.12Quiz 6 : Correlation10 Minutes1 Question
- Statistics Blogs2
- Python44
- 3.16 Introduction to Python
- 3.2Quiz : Python Basic20 Questions
- 3.3Quiz 1 : Introduction to Programming10 Minutes2 Questions
- 3.4Python Study Material
- 3.5Quiz 2: Why Python?10 Minutes2 Questions
- 3.6Quiz 3: Using Arithmetic Operators in Python10 Minutes1 Question
- 3.77.1 String Manipulation & Print formatting options in Python
- 3.8Quiz 4: Variables10 Minutes1 Question
- 3.98 Tuple, Dictionary, Sets and Conditional Programming in Python
- 3.10Quiz 6 : Numbers and Boolean Values in Python10 Minutes1 Question
- 3.117 2 List Data Structure in Python
- 3.12Quiz 7: Python Strings10 Minutes3 Questions
- 3.13Quiz 8 : Add Comments10 Minutes1 Question
- 3.14Quiz 9 : Indexing Elements10 Minutes1 Question
- 3.159 Conditional Flow and Control Structures
- 3.16Quiz 10 : Structuring with Indentation10 Minutes1 Question
- 3.179.1 Functions in Python
- 3.18Quiz 11 : Comparison Operators10 Minutes2 Questions
- 3.19Quiz 10 : The IF Statement10 Minutes1 Question
- 3.2010 Lambda Function, Non key var argument, Keyword variable argument in Python
- 3.21Quiz 11 : A Note on Boolean Values10 Minutes1 Question
- 3.2211 Numpy (Arrays, & Matrices) & Introduction to Pandas in Python
- 3.23Python list25 Questions
- 3.24Quiz 12 : Lists10 Minutes1 Question
- 3.25Python Functions25 Questions
- 3.26Quiz 13 : Functions10 Minutes2 Questions
- 3.2712 Data Analytics with Pandas & Data Visualization in Python
- 3.28Quiz 14 : Using Methods10 Minutes1 Question
- 3.29Quiz 15 : Dictionaries10 Minutes1 Question
- 3.30Quiz 16 : For Loops10 Minutes1 Question
- 3.31Quiz 17 : Lists with the range() Function10 Minutes1 Question
- 3.32Quiz 18 : Importing Modules in Python10 Minutes2 Questions
- 3.33Pandas Assignments
- 3.34Dowload Pandas Assignment Data Sets
- 3.35Data pre processing assignments
- 3.36Data exploration assignments
- 3.37DATASETS
- 3.38Pandas Quiz10 Minutes25 Questions
- 3.39Numpy Quiz20 Questions
- 3.40Python Set25 Questions
- 3.41Python Tuples20 Questions
- 3.42QUIZ FOR GLOBAL & LOCAL VARIABLES10 Questions
- 3.43Python Dictionary19 Questions
- 3.44While & For Loop15 Questions
- Inferential Statistics18
- 4.1What is Inferential Statistics
- 4.2Assignment-1
- 4.3Assignment-2
- 4.4Confidence Interval Assignment Solutions , & Anova Test
- 4.5Sampling Study Material
- 4.6Introduction to Hypothesis Testing
- 4.7Central Limit Theorem Study Material
- 4.8One Sample z testing
- 4.9One Sample t test
- 4.10two independent Sample t test Study Material
- 4.11Matched Pair t test
- 4.12Anova Study Material
- 4.13Two Way Anova with and Without Replicatin
- 4.14Two way Anova with and without replication Test in Excel
- 4.15Chi Square (Test of Independence) test Study Material
- 4.16Hypothesis Testing Quiz2 Questions
- 4.17QUIZ FOR INFERENTIAL STATISTICS10 Questions
- 4.18Chisq Test
- Data Science67
- 5.1Introduction to Data Science , Machine Learning , & its use cases
- 5.2What is data science ?
- 5.3Linear Regression Theory and Intuition
- 5.4Implementing Linear Regression Model in Python using Sklearn and Statsmodel packages
- 5.5Linear Regression – Model Evaluation
- 5.6Recap of Linear Regression Model using Excel
- 5.7K Fold Cross Validation
- 5.8Linear Regression – Model Output Interpretation Part-1
- 5.9Linear Regression – Model Output Interpretation Part-2
- 5.10Validating Linear Regression – Model Assumptions
- 5.11Overview of Regularization Technique
- 5.12Linear Regression Quiz10 Minutes37 Questions
- 5.13Gradient Descent Quiz10 Minutes5 Questions
- 5.14K Fold Cross Validation Quiz10 Minutes10 Questions
- 5.15Linear Regression Model Interpretation Study Material
- 5.16Linear Regression- Model Validation Study Material
- 5.17Logistic Regression Study Material
- 5.18Feature Selection Study Material
- 5.19Download Linear Regression Script with data set
- 5.20Case Study Assignment – Linear Regression with data set
- 5.21Case Study Assignment Linear Regression Solution
- 5.22Download Linear Regression case Study Solution script
- 5.23Gradient Descent Study Material
- 5.24Logistic Regression Study Material
- 5.25Logistic Regression10 Minutes12 Questions
- 5.26Model Diagnostic – Classification Study Material
- 5.27Machine Learning life cycle
- 5.28Exploratory Data Analysis
- 5.29Feature Engineering / Data Transformation / Pre processing
- 5.30Feature Scaling
- 5.31Gradient Descent Algorithm
- 5.32Implementation of Gradient Descent and Stochastic Gradient Descent in Python
- 5.33How to Determine the number of iterations to use in Gradient Descent
- 5.34Intuition of Logistic Regression
- 5.35Logistic Regression Model Diagnostic
- 5.36Case Study – Churn Prediction Part 1
- 5.37Churn Prediction in Python Part – 2
- 5.38Churn Prediction in Python – Part 3
- 5.39Recap on Topics Covered part of Logistic Regression Model Diagnostic
- 5.40Interpretation of ROC AUC
- 5.41Concordance and Discordance Metrics
- 5.42Kappa Statistic Metric
- 5.43How to Interpret Logistic Regression model output in Python?
- 5.44Decision Tree
- 5.45Random Forest10 Minutes8 Questions
- 5.46Machine Learning Introduction and Basics10 Minutes23 Questions
- 5.47Intuition of Bagging , & Random Forest model
- 5.48What is Boosting in Machine Learning ?
- 5.49Intuition of Gradient Boosting Machine for Regression
- 5.50Intuition Gradient Boosting for Classification
- 5.51Hyper Parameter Tuning – Random Forest
- 5.52Hyper Parameter Tuning
- 5.53Gradient Boosting for Classification
- 5.54Unsupervised Machine Learning – KMeans & Hierarchical Clustering
- 5.55Implementing KMeans and Hierarchical Cluster in Python
- 5.56Time Series – Arima intuition
- 5.57Implementing Time Series in Python
- 5.58Multi-layer Perceptron – Neural Network
- 5.59Mathematical representation of forward propagation
- 5.60Activation functions in neural network
- 5.61How does Back propagation works
- 5.62Implementation of neural network using keras, & tensorflow
- 5.63Machine Learning Model Deployment both on Flask App server and AWS EC2
- 5.64Machine Learning Model deployment on AWS EC2
- 5.65Computer Vision
- 5.66Convolutional Neural Network
- 5.67implementation of convolutional neural network in Python
- Python Blogs1
- Data Science Blogs2