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eZLearnPro Artificial Intelligence Training develops all the skills required for a career as a certified Artificial Intelligence Engineer.
It enables you to gain in-depth knowledge on various aspects of AI including TensorFlow, Machine Learning, Artificial neural networks, data science, python, advanced analytics, and many more. You will also gain hands-on exposure to various real-time use cases and projects during this training. Enroll now and get certified.
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Artificial Intelligence and Machine Learning
120+ Hours course.
1. Python for ML/AI
2. Exploratory data analysis (EDA)
3. Probability and Statistics
4. Linear Algebra
5. Dimensionality reduction and Visualization
6. Text Data Processing
7. Classification and Regression Models: K-Nearest Neighbors
8. Classification algorithms in various situations
9. Performance measurement of models
10. Naive Bayes
11. Linear Regression and Optimization.
12. Logistic Regression
13. Support Vector Machines (SVM)
14. Decision Trees
15. Ensemble Models
16. Featurizations and Feature engineering.
17. Unsupervised learning/Clustering
18. Hierarchical clustering
19. DBSCAN (Density based clustering)
20. Recommender Systems and Matrix Factorization.
21. Time Series Forcasting
22. Deep Learning:Neural Networks.
23. Deep Learning:Deep Multi-layer perceptrons
24. Deep Learning:Tensorflow and Keras.
25. Deep Learning:Convolutional Neural Nets.
26. Deep Learning:Recurrent Neural Networks.
27. Introduction to Tableau
28. How to use Tableau for Data Mining
29. Advanced Data Mining with Tableau
30. Apache Spark for Big Data Analytics
31. Spark Programming with Python
32. Spark SQL
33. Spark Streaming
34. Machine Learning with Spark
35. Getting Started with Azure ML
36. Data Processing
37. Classification Model
38. Regression Model
39. Deploy Webservice
40. Interview Preperation
₹30000
Artificial Intelligence and Machine Learning
120+ Hours course.
1. Python for ML/AI
2. Exploratory data analysis (EDA)
3. Probability and Statistics
4. Linear Algebra
5. Dimensionality reduction and Visualization
6. Text Data Processing
7. Classification and Regression Models: K-Nearest Neighbors
8. Classification algorithms in various situations
9. Performance measurement of models
10. Naive Bayes
11. Linear Regression and Optimization.
12. Logistic Regression
13. Support Vector Machines (SVM)
14. Decision Trees
15. Ensemble Models
16. Featurizations and Feature engineering.
17. Unsupervised learning/Clustering
18. Hierarchical clustering
19. DBSCAN (Density based clustering)
20. Recommender Systems and Matrix Factorization.
21. Time Series Forcasting
22. Deep Learning:Neural Networks.
23. Deep Learning:Deep Multi-layer perceptrons
24. Deep Learning:Tensorflow and Keras.
25. Deep Learning:Convolutional Neural Nets.
26. Deep Learning:Recurrent Neural Networks.
27. Introduction to Tableau
28. How to use Tableau for Data Mining
29. Advanced Data Mining with Tableau
30. Apache Spark for Big Data Analytics
31. Spark Programming with Python
32. Spark SQL
33. Spark Streaming
34. Machine Learning with Spark
35. Getting Started with Azure ML
36. Data Processing
37. Classification Model
38. Regression Model
39. Deploy Webservice
40. Interview Preperation
