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Python Machine Learning Course

Trainer Speaks
Hi, In this python machine learning course, I will introduce you to the Anaconda Data Science platform and will also cover how to use Python to read, write, load, and preprocess data. I will cover the usage of the pandas library, machine learning, data visualization, time series, and database access in Python.

Training Venue
Classroom Training: H-3/60, III Floor, Sector-18, Rohini, Delhi - 110089
Online Training: Hackveda One2One

Learning Objective
After completing this topic, you should be able to

Describe elements of data science and datasets with various modeling and prediction relationships. Recognize the various pipelines in data science and the stages of the data science cycle. Define and describe the various libraries and packages for data analysis. Perform the key steps involved in installing Anaconda including all the necessary packages for this course. Describe the various Python containers for data management. Create lists, tuples, and dictionaries with Python to drive data. Use Python list comprehensions to create lists.



Describe the IPython shell and shell commands. Run the Jupyter Notebook and familiarize with the basics of its user interface. Capture Python code output in Jupyter Notebook. Run the Jupyter QT Console and familiarize with the basics of its user interface. Use IPython to perform debugging and error management on Python code. Basic access and usage of the NumPy package in a Python development environment. Describe the various components of NumPy.



Describe ndarray object attributes. Describe the various NumPy array operations applicable to data science. Describe different ways of creating NumPy arrays. Describe how Pandas library may be used to read and write various formats of data. Use Pandas library to read data from a CSV file and write data out to a CSV file.

Use Python's standard JSON package to read JSON data. Use the pandas library to generate and parse date values perform data clean up by handling missing and erroneous data. Download and load a sample dataset into Python from a URL. Load a large dataset as smaller chunks by obtaining an iterator for the dataset.

Recognize the main concepts in data science using Python. Use pandas to describe the basic and common functionalities of pandas for Data Science. Use pandas to describe its primary data structures. Use pandas to describe hierarchical indexing perform basic data query operations on a pandas DataFrame.

Perform aggregation operations on a pandas DataFrame. Perform basic merge operations with pandas DataFrames. Describe the functionality and use of core packages and subpackages in the SciPy stack. Use the scikitlearn library to perform basic data standardization. Use the scikitlearn library to perform basic data normalization. Use the scikitlearn library to perform simple linear regression analysis.



Perform supervised learning by using the scikitlearn. Library to perform optical recognition of handwritten digits. Use the Python matplotlib library to plot and display a simple 2D line plot and set its line properties. Use the Python matplotlib library to create and customize multiple plots in a single figure. Use the Python matplotlib library to create and customize a box plot.

Use the Python matplotlib library to create and display a heat map. Use the Python matplotlib library to place legends and annotations on a 2D line plot. Use pandas to create a scatter plot matrix. Use the Python matplotlib library to create a 3D plot. Create, slice, and resample time series data in Python. Use pandas to create and manipulate Timedeltas in Python.

Identify key concepts in Python data cleansing. Perform data preprocessing and text mining in Python. Use pandas to access a MySQL database. Use the SciPy package to describe the various forms of distribution. Manage other concepts and processes in data science.

Python Machine Learning Course in Hackveda, Delhi

Paper Details
Degree: Python Machine Learning Course
University Name: Hackveda
Location: Delhi
Paper Code: Python Machine Learning Course
Credits: L : 50, T : 25, P : 25, C : 100
Training Type: Paid


Please note Python Machine Learning Course preparation course by Hackveda starts with beginner level concepts and escalates each day to design and implement solutions to complex problems available. All the required Python Machine Learning Course tools will be provided by Hackveda, VMDD Technologies within Python Machine Learning Course development kit. People who find it difficult to reach our Delhi centre, but are interested to join our Python Machine Learning Course course can join training online.

Course contents for Python Machine Learning Course

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