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 45 min Preview
  • Session 2. Machine Learning Machine Learning techniques and Introduction to Deep Learning 01 hour 20 min

• 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 50 min
  • Session 4. Controls, function, Class Import and Export 01 hour 09 min

• Module 3

  In this module you will experience different tools for Statistics-

  • Session 5. numpy- Arrays, Statistical, manipulation and Linear Algebra 01 hour 03 min
  • Session 6. Pandas 01 hour 03 min
  • Session 7. Statistical Analysis with Pandas 57 min
  • Session 8. Statistical and Visualization 58 min

• 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 53 min
  • Session 10. Variance, Standard Deviation and IQR, Chebyshev’s Theorem 58 min
  • Session 11. Covariance, Correlation, Kurtosis, Skewness, Analyzing the Continuous and Categorical Data, Probability 52 min
  • Session 12. Chi-Square Test & Prediction 01 hour 02 min

• Module 5

  Starting with Machine Learning Algorithms

  • Session 13. Linear Regression 45 min
  • Session 14. Linear Regression Model 55 min
  • Session 15. Linear Regression use cases, Lasso, Ridge 01 hour 04 min
  • Session 16. Logistics Regression and Model 01 hour
  • Session 17. Matrix, Precision, Recall, RFM and model 55 min
  • Session 18. Breast cancer Model using Logistic Regression, Decision Tree 53 min

• 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 52 min
  • Session 21. Artificial Neural Networks, Architecture and Mathematical Modelling of Neural Network 58 min
  • Session 22. Backpropagation algorithm 01 hour 06 min
  • Session 23. Backpropagation algorithm-Model 18 min

• Module 7

  Here, are continue with ANN Example and Introduction with Image Processing

  • Session 24. ANN Model and Image Processing 01 hour 13 min
  • Session 25. Image Processing and Image Manipulation.mp4 01 hour 12 min
  • Session 26. Face Recognition Model 01 hour 04 min
  • Session 27. Nearest Neighbour Predictions and Emotion Detection Model 37 min

• Module 8

  Here you are going to learn forecasting, time series

  • Session 28. Forecasting and Time Series 59 min
  • Session 29. Time Series Components and Model 54 min
  • Session 30. Exponential Smoothing Techniques 49 min
  • Session 31. Support Vector Machine 45 min

• Module 9

  Here you will learn Unsupervised learning

  • Session 32. Clustering and K Means 01 hour 13 min
  • Session 33. Principal Component Analysis 53 min
  • Session 34. Anomaly Detection 40 min

• 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 52 min
  • Session 36. Count Vector, Word Embedding 55 min
  • Session 37. Text Classification, Chatboat Model 01 hour 08 min
  • Session 38. Tensorflow 34 min