Machine Learning

1. Anaconda

• About Anaconda
• Why use Anaconda?

1.1 Anaconda Installation.

• How to install it?
• Using Anaconda
• Environment Creating in Anaconda
• Activating Enviourment in Anaconda
• Deleting Environment
• Libraries Installing In Anaconda and more

1.2 Jupyter Notebook Introduction

• What is Jupyter Notebook
• Why use it
• Layout understanding of Jupyter

1.3 Jupyter Notebook Shortcuts and how to work with it.

• Launching Jupyter from Anaconda prompt
• Short Cuts
• Add deleting cells
• Markdown cell Tricks
• How to work fast in jupyter

1.4 Python Introduction

• History of python
• Why is Python king now
• why work on python for AI

2. Using the Python IDE Jupyter (integrated development environment)

2.1. Types of values (int,float,str,bool)

2.2. Argument Passing and what are variable

2.3. Variable creation

2.4. Type method

2.5. Length method

2.6. Input and Output

2.7. Print

3. Data Structure in Python

3.1. List

3.2. Tuple

3.3. Dictionary

3.4. Set

3.5. Operations on List

3.6. The del statement

4. Conditional Statement

4.1. if Statements

4.2. else Statements

4.3. elif Statements

4.4. break and continue Statements

4.5. pass Statements

5. Loops Statements

5.1. Range

5.2. For Loop

5.3. For loop with range function

5.4 While Loop

5.5. While Loop with IF Condition

6. Functions

6.1. Defining Functions

6.2. Default Argument Values

6.3. Arguments passing and return value

6.4. Positional-or-Keyword Arguments

6.5. Lambda Expressions

6.6. Mapping

6.7. Filter

7. Comprehensions

7.1. List Comprehensions

7.2. Nested List Comprehensions

7.3. Comprehensions with Dictionary

7.4. Comprehensions with if the condition

8. File Handling

8.1. Reading and Writing Files

8.2. Errors and Exceptions

8.3. Syntax Errors

8.4. Exceptions

8.5. Handling Exceptions

8.6. Raising Exceptions

8.7. Exception Chaining

9. Classes

9.1. What is class

9.2. What is Objects

9.3. Class Definition Syntax

9.4. Class Objects Creation

9.5. Inheritance

9.6. Multiple Inheritance

9.7. Other Oops Concept

9.8. Iterators 

9.9. Generators

9.10. Generator Expressions

1. Reading the Data
2. Cleaning the Data
3. Data Visualization in Python
4. Summary statistics (mean, median, mode, variance, standard deviation)
5. Seaborn
6. Matplotlib
7. Population VS sample
8. Univariate and Multivariate statistics
9. Types of variables – Categorical and Continuous
10. Coefficient of correlations, Skewness and kurtosis

Stats - Part of Data Science/AI

1. Stats roll in Data Science
2. Population and sample
3. Types of variables
4. Central tendency
5. Coefficient of variance
6. Standard Deviation
7. Variance
8. Covariance
9. Pearson Correlation
10. Spearman correlation
11. Skewness and Kurtosis
12. Inferential statistics
13. Normal distribution
14. Mean mode median
15. Test hypotheses
16. Null Hypotheses
17. Alternate Hypotheses
18. Correlation matrix
19. Central limit theorem
20. Confidence interval
21. T-test
22. Type I and II errors
23. ANOVA
24.  Range
25. Binomial Distribution
26. Black-Scholes model
27. Boxplots
28. Chebyshev's Theorem
29. Chi-squared Distribution
30. Chi-Squared table
31. Cohen's kappa coefficient
32. Combination
33. Combination with replacement
34. Comparing plots
35. Continuous Uniform Distribution
36. Cumulative Frequency
37. Co-efficient of Variation
38. Correlation Co-efficient
39. Cumulative plots
40. Poisson Distribution
41. Frequency Distribution
42. Histograms
43. Kurtosis
44. Normal Distribution
45. Pie Chart
46. Poisson Distribution
47. Probability
48. Probability Additive Theorem
49. Probability Multiplicative Theorem
50. Probability Bayes Theorem
51. Probability Density Function
52. Residual analysis
53. Residual sum of squares
54. Root Mean Square
55. Scatterplots
56. Skewness
57. Standard Deviation
58. Type I & II Error
59. Z-Score
60. MinMax Schaller

What is Machine Learning?

1. Supervised and Unsupervised

2. Classification

3. Regression

4. Clustering

5. Time Series

6. Advance Techniques in Machine Learning and More

1. Introduction To Machine Learning
2. Introduction To Regression
3. Linear Regression- A Brief Introduction
4. Gradient Decent 
5. Convergence Theory
6. Polynomial Regression
7. Metrics of Model performance
8. How To Divide the Data For Training & Testing?
9. Training & Testing Of Model
10. MSE and RMSE
11. Using R^2 to Check the Accuracy of Model
12. Using the adjusted R^2 to compare the model with a different number of independent variables
13. Feature selection
14. Forward and backward selection
15. Parameter tuning and Model Evaluation
16. Data transformations and Normalization
17. Log transformation of dependent and independent variables
18. Dealing with categorical independent variables
19. One hot encoding vs dummy variable
20. Regulisation Technique
21. L1 and L2 Lasso and Rigid 
22. Introduction To Logistic Regression
23. The sigmoid function and odds ratio
24. The concept of logit
25. The failure of OLS in estimating parameters for a logistic regression
26. Introduction to the concept of Maximum likelihood estimation
27. Advantages of the maximum likelihood approach
28. Case study on Linear & Logistic Regression

1. Introduction To Classification

2. Types of Classification

3. Binary classification vs Multi-class classification.

4. Logistic Regression

5. SVM Support Vector Machine

6. KNN K-Nearest Neighbour

7. Naive Bayes

8. Decision Tree

9. Introduction To Decision trees 

10. Decision trees - nodes and splits

11. Working on the Decision tree algorithm.

12. Importance of Entropy and Gini index.

13. Manually calculating entropy using the Gini formula and working out how to split decision nodes

14. How To Evaluate Decision Tree models.

15. Accuracy metrics – precision, recall and confusion matrix

16. Interpretation for accuracy metric.

17. Building a robust decision tree model.

18. k-fold cross-validation.

19. CART - Extending decision trees to regressing problems.

20. Advantages of using CART.

21. The Bayes theorem.

22. Prior probability.

23. The Gaussian NAÏVE’S BAYES Classifier.

24. What are the Assumptions of the Naive Bayes Classifier?

25. Evaluating the model - Precision, Recall, Accuracy metrics 

26. ROC Curve and AUC

27. Extending Bayesian Classification

Ensemble

1. Bagging and Boosting
2. Bagging VS Bosting
3. Bagging
4. RandomForest
5. Regulization in Random Forest
6. Voting Classifier
7. Soft Voting and Hard Voting in Voting Classifier
8. Boosting
9. AdaBoost
10. Gradient Boosting
11. XGBoost Frame Work

1. What is Unsupervised learning?
2. The two major Unsupervised Learning problems - Dimensionality reduction and clustering.
3. Clustering algorithms.
4. The different approaches to clustering – Hierarchical and K means clustering.
5. Hierarchical clustering - The concept of agglomerative and divisive clustering.
6. Agglomerative Clustering – Working on the basic algorithms.
7. Distance matrix - Interpreting dendrograms.
8. Choosing the threshold to determine the optimum number of clusters.
9. Case Study on Agglomerative clustering
10. The K-means algorithm.
11. Measures of distance – Euclidean, Manhattan and Minkowski distance.
12. The concept of within-cluster sums of squares.
13. Using the elbow plot to select an optimum number of clusters.
14. Case study on k-means clustering.
15. Comparison of k means and agglomerative approaches to clustering.
16. Noise in the data and dimensional reduction.
17. Capturing Variance - The concept of principal components.
18. Assumptions in using PCA.
19. The working of the PCA algorithm.
20. Eigenvectors and orthogonality of principal components.
21. What is the complexity curve?
22. Advantages of using PCA.
23. Build a model using Principal components and compare it with the normal model. What is the difference?
24. Putting it all together.
25. The relationship between unsupervised and supervised learning.
26. Other algorithms of Clustering
27. DB-SCAN 
28. Mean shift
29. Case study on Dimensionality reduction followed by a supervised learning model.
30. Case study on Clustering followed by classification model.

1. The technique’s in Machine Learning
2. PCA
3. Dimension Reduction
4. Scaling
5. Z-score
6. Standardization
7. Min Max Scaler
8. Normalization
9. Normalization General Method
10. Feature engineering
11. Model Selection
12. Preprocessing
13. What are NULL
14. Handling NULL
15. Handling Categorical Values
16. Encoding
17. What are Encoding Techniques
18. Encoding for Nominal and Ordinal Data
19. Encoding by Pandas
20. Encoding by Other Lib
21. Encoding by Mapping and core Python

Hyper Tuning

1. What is Hyper Tuning
2. Cross-Validation
3. Grid Search CV
4. Random Search CV

1. Introduction to Model Deployment
2. Introduction to Flask in Python
3. How to deploy Applications in Flask?
4. Types of Model deployment

1. What is Time Series?
2. Regression vs Time Series
3. Examples of Time Series data
4. Trend, Seasonality, Noise and Stationarity
5. Time Series Operations
6. Detrending
7. Successive Differences
8. Moving Average and Smoothing
9. The exponentially weighted forecasting model
10. Lagging
11. Correlation and Auto-correlation
12. Holt-Winters Methods
13. Single Exponential smoothing
14. Holt’s linear trend method
15. Holt’s Winter seasonal method
16. ARIMA and SARIMA