TOLL FREE No : 1800-103-4583|

Python for Machine Learning

Register Now

Go to Training Calendar
Request In-house Training
Become a Trainer

DURATION: 5 Day (40 hours)
Target Audience:
Data Analyst, Business Analysts, Data Scientist.
Course Outcomes:

  • Master Python fundamentals for data manipulation and analysis.
  • Explore data types, control flows, and operators in Python.
  • Gain proficiency in data pre-processing and cleaning techniques.
  • Perform exploratory data analysis using Pandas and NumPy.
  • Develop skills in data visualization with Matplotlib.

Module: Introduction to Python and Basics:

  • Definition & Applications.
  • Features, Versions & Working.
  • Anaconda & Different IDEs for Python.
  • Introduction to IDE’s – Jupiter Notebook, Spider & Google Colab.

Module: Data Types and Control Flows:

  • Literals, reserved words and input functions.
  • Data Types: into, float, bool, star.
  • Decision Control Flows: If / Nested If / If-else / If-elif-else.
  • Control Flow Loops: While loop, For loop, While-else, For-else.
  • Operators: Arithmetic, Relational or Comparison, Logical.

Module: Lists, Tuples, Sets, and Dictionaries:

  • Bitwise Operators, Assignment Operators, Ternary Operator.
  • List: Ways of Accessing Values, Traversing Elements, List Operations, List Methods, Membership Operator, List Comprehension.
  • Tuples: Creating Tuples, Ways of Accessing Values, Tuple Vs Immutability, Tuple Comprehension.
  • Sets: Creating Sets, Ways of Accessing Values, Manipulating and Accessing Sets, Set Operations.
  • Dictionary: Why Dictionary, creating a Dictionary, Accessing Values, Updating Dictionaries, Functions of Dictionary.

Module: File Handling and Strings:

  • File Handling: Types of Files, Opening and Closing Files, Writing, Appending, and Reading Files.
  • Strings: String Literals, Single (”) & Double Quotes (“”), Triple Quotes (”’), Raw Strings (“r’…’ “) and Operations on strings.
  • Dictionary: Accessing Values, Updating Dictionaries, Functions of Dictionary.

Module: Iterators & Generators:

  • Iterator vs Inerrable, Containers, Generators in Python.

Module: Regular Expressions:

  • Uses of Regular Expressions – Text Analytics, import re, Character Classes, Backslash, Alteration, Quantifiers.

Module: OOPS Concept:

  • Class, Classes and Object, Creating Object, Accessing Objects, Need and Use of Self, Class Method, __in it__() constructor.

Module: Introduction to NumPy, Pandas & Matplotlib:

  • Introduction to NumPy, Install NumPy.
  • Array Creation, Array Reshaping, Indexing, Operations.
  • Introduction to Pandas, Slicing Data, Slicing Data Frame.
  • Data Visualization with Matplotlib.

Module: Introduction to Data Pre-processing:

  • Filtering Data Frame, Transforming Data Frame, Advanced Indexing.
  • Data Cleaning & Data Pre-processing.

Module: Exploratory Data Analysis (EDA):

  • Data Cleaning Techniques, Handling Missing Data, Handling Categorical Data.
  • Introduction to EDA, 2D Scatter-plot, 3D Scatter-plot, Pair plots.
  • Univariate, Bivariate, and Multivariate Analysis, Box-plot.
  • Variance and Standard Deviation, Median, IQR (Interquartile Range).

Advanced Pandas and Data Visualization:

  • Advanced Pandas, Advanced Indexing, Data Preparation.
  • Handling Missing Data, handling Categorical Data, Data Cleaning.

Data Visualization:

  • Introduction to Data Visualization, Plotting with Matplotlib.
  • Scatter Plots, Line Plots, Bar Plots, Pie Charts, Heat maps.

Project Work:

  • Problem Statement, Data Collection, Data pre-processing (Exploratory Data Analysis), Feature Engineering (optional), Data visualizations (Pandas, NumPy & Matplotlib), Project Final Outcome & findings.
Get 10% discount on a group of 4 or more nominations! (Discount will be applied during checkout)
Only applicable for selected batches and courses.

Python for Machine Learning

TrainingCourseLocationPriceQuantityAdd to Cart Button
SKU: N/A Category:
Our Clients