Python Programming with Machine Learning training in delhi

Learn Python Programming with Machine Learning :-

Overview:-

Python is a very powerful programming language used for many different applications.Over time, the huge community around this open source language has created quite a few tools to efficiently work with Python. In recent years, a number of tools have been built specifically for data science. As a result, analyzing data with Python has never been easier.

Machine Learning with Python. Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns.

Pre-Requisites:-

It really depends on what you want to do. If you’re looking to be a data engineer, you can get away without knowing a lot of the mathematics behind data science.

But generally speaking, you want to have the following skill set:

  • Probability & Statistics background – you should be comfortable with the basics, but
    also have a solid understanding of statistical inference.
  • Coding background – data science involves a lot of computational tools, so you
    should be very comfortable with at least one language. I recommend Python and R.
  • Linear Algebra background – vectors, matrices, and all the operations that
    accompany them are particularly important for machine learning.

 Duration- 60 Hours

Module 1                                                                    Module 2

1) Introduction to Python                                 1) Introduction to Machine Learning
2) Numpy Basics                                                 2) Data Preprocessing
3) Pandas Basics                                                 3) Creating validation rules
4) Matplotlib basics
5) Seaborn basics

Module 3                                                                       Module 4

1) Introduction to Regression                        1) Introduction to Classification
2) Regularized Regression                             2) Regularized Classification
3) Auto selection of parameters                   3) Auto selection of parameters
4) Evaluation of best models                         4) Evaluation of best models
5) Model representation                                 5) Model representation

Module 5                                                               Module 6

1) Introduction to Decision Tree                 1) Introduction to Random Forest
2) Auto selection of parameters                  2) Auto selection of parameters
3) Evaluation of best models                        3) Bagging and Boosting Models
4) Model representation                              4) Evaluation of best models
5) Model representation

Module 7                                                          Module 8

1) Introduction to SVM                                1) Introduction to Neural Network
2) Auto selection of parameters                 2) Auto selection of parameters
3) Evaluation of best models                       3) Evaluation of best models
4) Model representation                               4) Model representation

Module 9                                                              Module 10

1) Introduction to Unsupervised Learning      1) Introduction to Dimension Reduction
2) Auto selection of parameters                         2) Auto selection of parameters
3) Evaluation of best models                              3) Evaluation of best models
4) Model representation                                      4) Model representation

Module 11

1) Introduction to Nearest Neighbors
2) Auto selection of parameters
3) Evaluation of best models
4) Model representation

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