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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CURRICULUM & PROJECTS
Machine Learning with Python Training
- 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
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Python Keyword and Identiers
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Arithmetic
- Relational
- Logical
- Assignment
- Membership
- Identity
- 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
- 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.
- Strings
- List
- Tuples
- Dictionary
- Sets
- 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 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
- 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
- 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
- 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)
- 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.)
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- 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
- 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
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- Installing SMTP Python Module
- Sending Email
- Reading from le and sending emails to all users
- 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
- 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
- Categorical Data
- Numerical Data
- Mean
- Median
- Mode
- Outliers
- Range
- Interquartile range
- Correlation
- Standard Deviation
- Variance
- Box plot
- 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)
- 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
- 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
- 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
- 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
- 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
- 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
- Articial Intelligence
- Machine Learning
- Machine Learning Algorithms
- Algorithmic models of Learning
- Applications of Machine Learning
- Large Scale Machine Learning
- Computational Learning theory
- Reinforcement Learning
- Supervised Learning
- Unsupervised Learning
- Semi-supervised and Reinforcement Learning
- Bias and variance Trade-off
- Representation Learning
- Regression and its Types
- Logistic Regression
- Linear Regression
- Polynomial Regression
- 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
- About Clustering
- Clustering Algorithms
- K-means Clustering
- Hierarchical Clustering
- Distribution Clustering
- 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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