Master the core concepts of Machine Learning With Python. Join now to get trained by a Machine Learning With Python expert.
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Corporate Served- Machine Learning Training with Python in Delhi offers a unique learning opportunity for those interested in data science and programming. The training focuses on teaching you the basics of Python and how to use it in machine learning, which helps computers learn from data and make predictions. The course is flexible so you can choose how you want to learn - online courses, self-paced modules, or face-to-face sessions.
- Croma Campus understands that many people work alongside their studies. We offer a variety of study methods, including online courses that you can join live and learn at your own pace, as well as traditional classroom sessions, so you can fit your studies around work and other commitments.
- Whether you're new to programming or looking to advance in your career, Machine Learning Training with Python in Delhi will help you develop the skills you need to succeed in today's data-driven world.
Machine Learning with Python Training in Delhi
- Machine Learning With Python Course in Delhi courses provide participants with the knowledge and skills required to effectively configure and manage the various features of the Machine Learning With Python Training Course in Delhi:
- Machine Learning Training with Python in Delhi focuses on practical skills and prepares you for a successful career in today's data-driven world.
Learn the Basics of Python: Understand how to use Python for data processing and writing programs.
Understand the Basics of Machine Learning: Learn the basics of how computers learn from data and make predictions.
Data Processing Skills: Learn how to organize and manage data using tools such as Pandas and NumPy.
Using Statistics for Analysis: Learn how to apply statistical methods to understand and interpret data.
Practice on Real Projects: Work on real projects to apply what you've learned and gain practical experience.
- After completing Machine Learning Training with Python in Delhi, freshers can expect a reasonable starting salary in the field of data science and analytics. Typically, entry-level jobs offer a salary that indicates how much the company needs someone who is good at Python and well versed in machine learning. In Delhi, an entry-level worker with good Python skills and basic knowledge of machine learning can start working with a salary of INR 300,000 to INR 500,000 per year.
- Completing a Machine Learning Training with Python in Delhi course will significantly improve your career prospects.
Start your career as a Junior Data Analyst or Data Associate, helping in data analysis and report creation using Python and Machine Learning learning tools.
Keep learning Python and Machine Learning techniques to stay up to date with what's happening in the industry and improve your skills.
With more experience, you can become a Data Scientist, which means analyzing complex data, building predictive models, and providing data-driven advice.
You can also focus more on becoming a Machine Learning Engineer, where you design and configure systems that use machine learning to help companies improve their operations.
Depending on your preference, you can work in areas such as banking, healthcare, online shopping, telephony, etc.
- Machine Learning Course with Python in Delhi is popular for a few clear reasons. First, Python is known for being easy to learn and powerful for analyzing data. It has tools that make it perfect for Machine Learning, where computers learn from data to make predictions. Many people find Python friendly and useful, so they choose it to study Machine Learning.
- There's a big demand for people who know Machine Learning and Python in Delhi. Companies in finance, healthcare, and more need experts who can use data to help them make smart decisions. Learning these skills can lead to good jobs with competitive salaries, making it a smart choice for anyone interested in a career in technology and data analysis.
- The Machine Learning Training with Python in Delhi prepares professionals for a wide range of roles and responsibilities.
Data Analyst: As a data analyst, your primary job is to interpret data and turn it into information that a company can use to make decisions.
Data Scientist: Data Scientists delve into data and build complex machine learning models using Python. Your responsibilities include identifying data sources, developing algorithms, and applying advanced statistical methods to generate insights and solve business problems.
Machine Learning Engineer: This role focuses on designing and deploying machine learning systems using Python. Tasks include data pre-processing, selecting the right model, and optimizing performance.
AI Specialist: As an AI Specialist, you will specialize in developing artificial intelligence solutions based on machine learning algorithms.
Business Intelligence Developer: In this role, you'll use Python and machine learning to build tools and applications that help companies analyze data more effectively.
- Machine Learning Training with Python in Delhi opens the door to jobs in some of key industries: IT companies use it for software and cybersecurity; the finance industry uses it to understand the market and manage funds better. In the medical sector, it helps in diagnosing and treating patients; and in e-commerce, it is used to predict what customers want to buy. Telecommunication companies use it to improve their networks and understand their customers better. Enroll in our Artificial Intelligence Course in Delhi to gain the skills needed to leverage Python and machine learning for solving problems and enhancing operations across various industries.
- A certificate in Machine Learning Training with Python in Delhi is important because it proves that you can learn and use Python and Machine Learning. It can help you get a job by showing employers that you have the skills they need. The certificate also shows that you are serious about learning and improving, which can help you advance in your career or get admitted into a higher education program.
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Machine Learning with Python Training
- Machine learning is important because it gives enterprises a view of trends in ustomer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations
- In this program you will learn:
Python Training Curriculum
Data Analysis and Visualization using Pandas.
Data Analysis and Visualization using NumPy and MatPlotLib
Introduction to Data Visualization with Seaborn
Machine Learning
- Introduction To Python
Installation and Working with Python
Understanding Python variables
Python basic Operators
Understanding the Python blocks.
- Python Keyword and Identiers
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
Python Keyword and Identiers
Python Comments, Multiline Comments.
Python Indentation
Understating the concepts of Operators
- Introduction To Variables
Variables, expression condition and function
Global and Local Variables in Python
Packing and Unpacking Arguments
Type Casting in Python
Byte objects vs. string in Python
Variable Scope
- Python Data Type
- Strings
- List
- Tuples
- Dictionary
- Sets
Declaring and using Numeric data types
Using string data type and string operations
Understanding Non-numeric data types
Understanding the concept of Casting and Boolean.
- Control Structure & Flow
Statements – if, else, elif
How to use nested IF and Else in Python
Loops
Loops and Control Statements.
Jumping Statements – Break, Continue, pass
Looping techniques in Python
How to use Range function in Loop
Programs for printing Patterns in Python
How to use if and else with Loop
Use of Switch Function in Loop
Elegant way of Python Iteration
Generator in Python
How to use nested Loop in Python
Use If and Else in for and While Loop
Examples of Looping with Break and Continue Statement
How to use IN or NOT IN keyword in Python Loop.
- Python Function, Modules and Packages
Python Syntax
Function Call
Return Statement
Arguments in a function – Required, Default, Positional, Variable-length
Write an Empty Function in Python –pass statement.
Lamda/ Anonymous Function
*args and **kwargs
Help function in Python
Scope and Life Time of Variable in Python Function
Nested Loop in Python Function
Recursive Function and Its Advantage and Disadvantage
Organizing python codes using functions
Organizing python projects into modules
Importing own module as well as external modules
Understanding Packages
Random functions in python
Programming using functions, modules & external packages
Map, Filter and Reduce function with Lambda Function
More example of Python Function
- Python Date Time and Calendar
Day, Month, Year, Today, Weekday
IsoWeek day
Date Time
Time, Hour, Minute, Sec, Microsec
Time Delta and UTC
StrfTime, Now
Time stamp and Date Format
Month Calendar
Itermonthdates
Lots of Example on Python Calendar
Create 12-month Calendar
Strftime
Strptime
Format Code list of Data, Time and Cal
Locale’s appropriate date and time
- List
What is List.
List Creation
List Length
List Append
List Insert
List Remove
List Append & Extend using “+” and Keyword
List Delete
List related Keyword in Python
List Revers
List Sorting
List having Multiple Reference
String Split to create a List
List Indexing
List Slicing
List count and Looping
List Comprehension and Nested Comprehension
- Tuple
What is Tuple
Tuple Creation
Accessing Elements in Tuple
Changing a Tuple
Tuple Deletion
Tuple Count
Tuple Index
Tuple Membership
TupleBuilt in Function (Length, Sort)
- Dictionary
Dict Creation
Dict Access (Accessing Dict Values)
Dict Get Method
Dict Add or Modify Elements
Dict Copy
Dict From Keys.
Dict Items
Dict Keys (Updating, Removing and Iterating)
Dict Values
Dict Comprehension
Default Dictionaries
Ordered Dictionaries
Looping Dictionaries
Dict useful methods (Pop, Pop Item, Str , Update etc.)
- Sets
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
Frozen Sets
- Strings
What is Set
Set Creation
Add element to a Set
Remove elements from a Set
PythonSet Operations
- Python Exception Handling
Python Errors and Built-in-Exceptions
Exception handing Try, Except and Finally
Catching Exceptions in Python
Catching Specic Exception in Python
Raising Exception
Try and Finally
- Python File Handling
Opening a File
Python File Modes
Closing File
Writing to a File
Reading from a File
Renaming and Deleting Files in Python
Python Directory and File Management
List Directories and Files
Making New Directory
Changing Directory
- Python Database Interaction
SQL Database connection using
Creating and searching tables
Reading and Storing cong information on database
Programming using database connections
- Contacting user Through Emails Using Python
Installing SMTP Python Module
Sending Email
Reading from le and sending emails to all users
- Reading an excel
Working With Excel
Reading an excel le using Python
Writing to an excel sheet using Python
Python| Reading an excel le
Python | Writing an excel le
Adjusting Rows and Column using Python
ArithmeticOperation in Excel le.
Play with Workbook, Sheets and Cells in Excel using Python
Creating and Removing Sheets
Formatting the Excel File Data
More example of Python Function
- Complete Understanding of OS Module of Python
Check Dirs. (exist or not)
How to split path and extension
How to get user prole detail
Get the path of Desktop, Documents, Downloads etc.
Handle the File System Organization using OS
How to get any les and folder’s details using OS
- Statistics
Categorical Data
Numerical Data
Mean
Median
Mode
Outliers
Range
Interquartile range
Correlation
Standard Deviation
Variance
Box plot
- Pandas
Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
Using the Excel File class to read multiple sheets More Mapping, Filling
Nonvalue’s
Exploring the Data Plotting, Correlations, and Histograms
Getting statistical information about the data Analysis Concepts, Handle the None Values
Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
Applying formulas on the columns Basic Grouping, Concepts of Aggre
gate Function
Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
Under sting the Properties of Pivot Table in Pandas Advanced Reading
CSVs/HTML, Binning, Categorical Data
Exporting the results to Excel Joins:
Python | Pandas Data Frame Inner Join
Under sting the properties of Data Frame Left Join (Left Outer Join)
Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
Pandas | Find Missing Data and Fill and Drop NA Appending DataFrame and Data
Pandas | How to Group Data How to apply Lambda / Function on Data
Frame
Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- NumPy
Introduction to NumPy: Numerical Python
Importing NumPy and Its Properties
NumPy Arrays
Creating an Array from a CSV
Operations an Array from a CSV
Operations with NumPy Arrays
Two-Dimensional Array
Selecting Elements from 1-D Array
Selecting Elements from 2-D Array
Logical Operation with Arrays
Indexing NumPy elements using conditionals
NumPy’s Mean and Axis
NumPy’s Mode, Median and Sum Function
NumPy’s Sort Function and More
- MatPlotLib
Bar Chart using Python MatPlotLib
Column Chart using Python MatPlotLib
Pie Chart using Python MatPlotLib
Area Chart using Python MatPlotLib
Scatter Plot Chart using Python MatPlotLib
Play with Charts Properties Using MatPlotLib
Export the Chart as Image
Understanding plt. subplots () notation
Legend Alignment of Chart using MatPlotLib
Create Charts as Image
Other Useful Properties of Charts.
Complete Understanding of Histograms
Plotting Different Charts, Labels, and Labels Alignment etc.
- Introduction to Seaborn
Introduction to Seaborn
Making a scatter plot with lists
Making a count plot with a list
Using Pandas with seaborn
Tidy vs Untidy data
Making a count plot with a Dataframe
Adding a third variable with hue
Hue and scattera plots
Hue and count plots
- Visualizing Two Quantitative Variables
Introduction to relational plots and subplots
Creating subplots with col and row
Customizing scatters plots
Changing the size of scatter plot points
Changing the style of scatter plot points
Introduction to line plots
Interpreting line plots
Visualizing standard deviation with line plots
Plotting subgroups in line plots
- Visualizing a Categorical and a Quantitative Variable
Current plots and bar plots
Count plots
Bar plot with percentages
Customizing bar plots
Box plots
Create and interpret a box plot
Omitting outliers
Adjusting the whisk
Point plots
Customizing points plots
Point plot with subgroups
- Customizing Seaborn Plots
Changing plot style and colour
Changing style and palette
Changing the scale
Using a custom palette
Adding titles and labels: Part 1
Face Grids vs. Axes Subplots
Adding a title to a face Grid object
Adding title and labels: Part 2
Adding a title and axis labels
Rotating x-tics labels
Putting it all together
Box plot with subgroups
Bar plot with subgroups and subplots
Well done! What’s next
- Introduction to Machine Learning
Articial Intelligence
Machine Learning
Machine Learning Algorithms
Algorithmic models of Learning
Applications of Machine Learning
Large Scale Machine Learning
Computational Learning theory
Reinforcement Learning
- Techniques of Machine Learning
Supervised Learning
Unsupervised Learning
Semi-supervised and Reinforcement Learning
Bias and variance Trade-off
Representation Learning
- Regression
Regression and its Types
Logistic Regression
Linear Regression
Polynomial Regression
- Classication
Meaning and Types of Classication
Nearest Neighbor Classiers
K-nearest Neighbors
Probability and Bayes Theorem
Support Vector Machines
Naive Bayes
Decision Tree Classier
Random Forest Classier
- Unsupervised Learning: Clustering
About Clustering
Clustering Algorithms
K-means Clustering
Hierarchical Clustering
Distribution Clustering
- Model optimization and Boosting
Ensemble approach
K-fold cross validation
Grid search cross validation
Ada boost and XG Boost
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FAQ's
Some knowledge is required. Programming basics. Already knowing Python is helpful but not required.
Croma Campus regularly updates its courses to reflect and include the latest advancements in Python and Machine Learning. Your teachers are experts who bring real-world experience to the classroom, so you'll learn the latest skills.
After completing their training, graduates often work as data analysts, data scientists, machine learning engineers, AI specialists, or business intelligence developers. You can find jobs in industries such as IT, finance, healthcare, e-commerce, and communications.
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