Machine Learning

Program Overview

Data science is a multidisciplinary field that focuses on studying and analyzing data to extract valuable insights and make informed decisions. It involves various processes such as data collection, cleaning, visualization, and statistical analysis to uncover patterns and trends within the data. Data scientists often use tools like Python and R for data manipulation and analysis.

Machine learning, on the other hand, is a subfield of artificial intelligence (AI) that focuses on developing algorithms and models that can learn from historical data and make predictions or decisions without being explicitly programmed. Machine learning techniques include supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning.

Don't miss the chance to join the ranks of professionals shaping the future with data. Invest in your education, and you'll unlock a world of opportunities in Data Science and Machine Learning. Enrol today to make your mark in this exciting and rapidly evolving field!

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Machine Learning

  1. 30000.00
Features
  • Live Online Program
  • E-Learning Material
  • Flexible Fee-Plan
  • Industry Projects

Key Highlights

  • Live OnlineLive Online Program
  • Experienced FacultiesE-Learning Material
  • Students HandoutsFlexible Fee-Plan
  • Solid FoundationIndustry Projects
  • Solid FoundationExpert Experienced Trainers
  • Solid Foundation1:1 Doubt Session
  • Solid FoundationLearning Management System
  • Solid FoundationLive Projects

Program Objective

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

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$360.80 US Dollar


1325.56 Dirham


₹30000.00 + 18% GST


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