Course Curriculum
Module 1 (Demo)
In this Module we will cover, Introduction to AI and Machine Learning. Application of Machine Learning- Supervised, Unsupervised and Reinforcement Learning, Classification, Regression, Clustering, Anomaly Detection Recommendation, System Algorithms in Machine Learning and Introduction to Deep Learning
- Session 1. Introduction to AI Machine Learning and Application
- Session 2. Machine Learning Machine Learning techniques and Introduction to Deep Learning
Module 2
In this module you will be able to popup with Python Programming, Object of this Module to make a fearless Python Coder, You will get to learn, practices and implement use cases, Build expertise in python programming, after learning python you can quick start with AI & ML
- Session 3. Introduction to Python Programming and Controls
- Session 4. Controls, function, Class Import and Export
Module 3
In this module you will experience different tools for Statistics-
- Session 5. numpy- Arrays, Statistical, manipulation and Linear Algebra
- Session 6. Pandas
- Session 7. Statistical Analysis with Pandas
- Session 8. Statistical and Visualization
Module 4
In this module you will learn about Statistics of Data- Data Variable, Mean, Mode, Median, Standard Deviation Variance, Correlation and Probability
- Session 9. Descriptive Statistics, Inferential Statistics, Variable, Median, Percentile, RFM
- Session 10. Variance, Standard Deviation and IQR, Chebyshev’s Theorem
- Session 11. Covariance, Correlation, Kurtosis, Skewness, Analyzing the Continuous and Categorical Data, Probability
- Session 12. Chi-Square Test & Prediction
Module 5
Starting with Machine Learning Algorithms
- Session 13. Linear Regression
- Session 14. Linear Regression Model
- Session 15. Linear Regression use cases, Lasso, Ridge
- Session 16. Logistics Regression and Model
- Session 17. Matrix, Precision, Recall, RFM and model
- Session 18. Breast cancer Model using Logistic Regression, Decision Tree
Module 6
In this module we will starting with Artificial Neural NetWorks
- Session 20. Possibility the survival of passenger in titanic- Model, Random Forest and Introduction to Artificial Neural Networks
- Session 21. Artificial Neural Networks, Architecture and Mathematical Modelling of Neural Network
- Session 22. Backpropagation algorithm
- Session 23. Backpropagation algorithm-Model
Module 7
Here, are continue with ANN Example and Introduction with Image Processing
- Session 24. ANN Model and Image Processing
- Session 25. Image Processing and Image Manipulation.mp4
- Session 26. Face Recognition Model
- Session 27. Nearest Neighbour Predictions and Emotion Detection Model
Module 8
Here you are going to learn forecasting, time series
- Session 28. Forecasting and Time Series
- Session 29. Time Series Components and Model
- Session 30. Exponential Smoothing Techniques
- Session 31. Support Vector Machine
Module 9
Here you will learn Unsupervised learning
- Session 32. Clustering and K Means
- Session 33. Principal Component Analysis
- Session 34. Anomaly Detection
Module 10
In this module you will learn Natural Language Processing and It's Model
- Session 35. Introduction to NLP, Application of NLP, Components of NLP and Simple Text Preprocession Model Regular Expression
- Session 36. Count Vector, Word Embedding
- Session 37. Text Classification, Chatboat Model
- Session 38. Tensorflow