- Overview of Python
- The Companies using Python
- Different Applications where Python is Used
- Discuss Python Scripts on UNIX/Windows
- Values, Types, Variables
- Operands and Expressions
- Conditional Statements
- Loops
- Command Line Arguments
- Writing to the Screen
- What is Data Science?
- What does Data Science involve?
- Era of Data Science
- Business Intelligence vs Data Science
- Life cycle of Data Science
- Tools of Data Science
Online
6 Weeks
₹ 21,995
Quick facts
particular | details | ||
---|---|---|---|
Medium of instructions
English
|
Mode of learning
Self study, Virtual Classroom
|
Mode of Delivery
Video and Text Based
|
Frequency of Classes
Weekends
|
Course overview
The Python Machine Learning Certification Training programme enables candidates to become skilled data scientists. Hence, the curriculum will teach you all the necessary concepts of Time series, statistics, and ML algorithms such as the unsupervised, supervised, and reinforcement. You will also gain fluency in other ML algorithms like clustering, regression, Random Forest, Decision Trees, Q-Learning, and Naïve Bayes. Moreover, you will experience the industry-relevant practices by solving real-time case studies on aviation, HR, social media, and healthcare.
Edureka’s Python Machine Learning Certification Training offers a feature-rich curriculum. You will attend instructor-led live classes, work on frequent assignments, have a lifetime LMS access, and engage in peer interaction via the community forum. Besides, you will also receive lifetime access to the 24x7 support team to resolve technical queries.
Moreover, the online Python Machine Learning Certification Training course will award you with a prestigious certificate, post-completion. You will also receive a ‘resume-building tool’ to create a professional CV and ace recruitment for top-tier companies like Dell, Honeywell, Cisco, VMware, etc.
The highlights
- Instructor-led sessions
- 36 hours of online classes
- Self-paced learning
- Pre-recorded videos
- Placement assistance
- Lifetime access to LMS and 24x7 support
- Assignments after every class
- Real-life projects
- Case studies
- Community forum
- Trained instructors
- Two-month access to Cloud Lab
- Course certificate
- Hands-on projects
Program offerings
- Lifetime lms access
- 24x7 online support
- Instructor-led classes
- Resume building
- Community forum
- Industry-relevant projects
- Case studies
- Course certificate
Course and certificate fees
Fees information
- Aspirants have to pay the entire course fee of INR 19,995 + GST to join the Machine Learning Certification Training using Python course.
- They can complete the transaction via numerous payment modes such as MasterCard, VISA, UPI, Maestro, American Express, or Net banking.
Python Machine Learning Certification Training fee structure
Course Name | Total Fees |
Python Machine Learning Certification Training | INR 21,995 + GST |
*Students can also avail of a No-Cost EMI, starting at INR 7,332 / month
certificate availability
certificate providing authority
Eligibility criteria
Eligibility Criteria
Candidates with a Python development experience are eligible to join the Python Machine Learning Certification Training by Edureka. While Python experience is more advantageous, Data analysis fundamentals practised over SAS/R or other tools is a plus. Additionally, Edureka will provide you with a complimentary programme, i.e. the “Python Statistics for Data Science”, as a self-paced training. You will receive this post-enrollment.
What you will learn
Completing the Python Machine Learning Certification Training course by Edureka will make you adept in these concepts: -
- Fundamentals and mechanisms of Machine Learning
- Implementing Machine Learning in Python
- Data analysis automation via Python
- A Machine Learning engineer’s roles
- Working with real-time data
- Discussing ML algorithms and their implementations
- Learning the techniques and tools for predictive modelling
- Knowledge about Time Series as well as it’s related concepts
- Validating ML algorithms
- Living in the present, handling the future business
Who it is for
The Python Machine Learning Certification Training course online is an excellent fit for the following: -
- Python professionals who aim to design automatic predictive models
- Analytics Managers who lead analyst teams
- Information Architects who are looking to attain expertise in Predictive Analytics
- Business analysts who wish to gain an in-depth understanding of Machine Learning techniques
- Developers who are pursuing the role of a Machine Learning Engineer
Admission details
Edureka’s Python Machine Learning Certification Training course requires interest candidates to follow these steps for enrollment: -
- Click on https://www.edureka.co/machine-learning-certification-training to visit the programme webpage.
- Scroll to see the “Enroll Now” option and select it.
- Use your email ID and contact information to sign in.
- Select your preferred batch and click on the “Proceed to Payment” button.
- Proceed to pay via your preferred mode.
- Once done, you can start accessing the programme material.
Filling the form
Learners do not have to complete an application form to undertake the Python Machine Learning Certification Training programme. Instead, they have to visit the course page, proceed with their phone number and email address, and select a batch. Once done, they can proceed to complete the fee payment and then, start learning.
The syllabus
Introduction to Data Science and ML using Python
Topics
Hands-on
- Creating “Hello World” code
- Variables
- Demonstrating Conditional Statements
- Demonstrating Loops
Data Handling, Sequences and File Operations
Topics
- Data Analysis Pipeline
- What is Data Extraction?
- Types of Data
- Raw and Processed Data
- Data Wrangling
- Python files I/O Functions
- Numbers
- Strings and related operations
- Tuples and related operations
- Lists and related operations
- Dictionaries and related operations
- Sets and related operations
Hands-on
- Tuple - properties, related operations, compared with the list
- List - properties, related operations
- Dictionary - properties, related operations
- Set - properties, related operations
Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions
Topics
- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values
- Lambda Functions
- Object Oriented Concepts
- Standard Libraries
- Modules Used in Python
- The Import Statements
- Module Search Path
- Package Installation Ways
- Errors and Exception Handling
- Handling Multiple Exceptions
Hands-on
- Lambda function in Python
- Errors and Exceptions in Python
- Packages and Modules in Python
- Functions - Syntax, Arguments, Keyword Arguments, Return Values
- Sorting - Sequences, Dictionaries, Limitations of Sorting
Introduction to NumPy, Pandas, and Matplotlib
Topics
- Data Analysis
- NumPy - arrays
- Operations on arrays
- Indexing, slicing, and iterating
- Reading and writing arrays on files
- Pandas - data structures & index operations
- Reading and Writing data from Excel/CSV formats into Pandas
- Metadata for imported Datasets
- Matplotlib library
- Grids, axes, plots
- Markers, colors, fonts, and styling
- Types of plots - bar graphs, pie charts, histograms
- Contour plots
Hands-on
- NumPy library - Creating NumPy array, operations performed on NumPy array
- Pandas library - Creating series and data frames, Importing and exporting data
- Matplotlib library - Using Scatterplot, histogram, bar graph, a pie chart to show information, Styling of Plot
Data Manipulation
Topics
- Basic Functionalities of a data object
- Merging of Data objects
- Concatenation of data objects
- Types of Joins on data objects
- Exploring and analyzing datasets
- Analysing a dataset
Hands-on
- Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples(), GroupBy operations, Aggregation, Concatenation, Merging and joining
Introduction to Machine Learning with Python
Topics
- What is Machine Learning?
- Machine Learning Use-Cases
- Machine Learning Process Flow
- Machine Learning Categories
- Linear regression
- Gradient descent
Hands-on
- Linear Regression – Boston Dataset
Supervised Learning - I
Topics
- What are Classification and its use cases?
- What is a Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Perfect Decision Tree
- Confusion Matrix
- What is Random Forest?
Hands-on
- Implementation of Logistic Regression, Decision Tree, Random Forest algorithms
Dimensionality Reduction
Topics
- Introduction to Dimensionality
- Why Dimensionality Reduction
- PCA
- Factor Analysis
- Scaling dimensional model
- LDA
Hands-on
- Implementing PCA
- Scaling dimensional model
- Implementing LDA
Supervised Learning - II
Topics
- What is Naïve Bayes?
- How Naïve Bayes works?
- Implementing Naïve Bayes Classifier
- What is a Support Vector Machine?
- Illustrate how Support Vector Machine works
- Hyperparameter Optimization
- Grid Search vs. Random Search
- Implementation of Support Vector Machine for Classification
Hands-on
- Implementation of Naïve Bayes, SVM algorithms
Unsupervised Learning
Topics
- What is Clustering & its Use Cases?
- What is K-means Clustering?
- How does the K-means algorithm works?
- How to do optimal clustering
- What is C-means Clustering?
- What is Hierarchical Clustering?
- How does Hierarchical Clustering work?
Hands-on
- Implementing K-means Clustering
- Implementing Hierarchical Clustering
Association Rules Mining and Recommendation Systems
Topics
- What are Association Rules?
- Association Rule Parameters
- Calculating Association Rule Parameters
- Recommendation Engines
- How do Recommendation Engines work?
- Collaborative Filtering
- Content-Based Filtering
Hands-on
- Implementing Apriori Algorithm
- Performing Market Basket Analysis
Reinforcement Learning (Self-Paced)
Topics
- What is Reinforcement Learning?
- Why Reinforcement Learning?
- Elements of Reinforcement Learning
- Exploration vs. Exploitation dilemma
- Epsilon Greedy Algorithm
- Markov Decision Process (MDP)
- Q values and V values
- Q – Learning
- Values
Hands-on
- Calculating Reward
- Discounted Reward
- Calculating Optimal quantities
- Implementing Q Learning
- Setting up an Optimal Action
Time Series Analysis (Self-Paced)
Topics
- What is Time Series Analysis?
- Importance of TSA
- Components of TSA
- White Noise
- AR model
- MA model
- ARMA model
- ARIMA model
- Stationarity
- ACF & PACF
Hands-on
- Checking Stationarity
- Converting non-stationary data to stationary
- Implementing Dickey-Fuller Test
- Plotting ACF and PACF
- Generating the ARIMA plot
Model Selection and Boosting
Topics
- What is Model Selection?
- Need for Model Selection
- Cross Validation
- What is Boosting?
- How do Boosting Algorithms work?
- Types of Boosting Algorithms
- Adaptive Boosting
Hands-on
- Performing Cross Validation
- Implementing AdaBoost using Python
Statistical Foundations (Self-Paced)
Topics
- What is Exploratory Data Analysis?
- EDA Techniques
- EDA Classification
- Univariate Non-graphical EDA
- Univariate Graphical EDA
- Multivariate Non-graphical EDA
- Multivariate Graphical EDA
- Heat Maps
Hands-on
- Implementing Graphical EDA Techniques
- Implementing Non-Graphical EDA Techniques
Database Integration with Python (Self-Paced)
Topics
- Basics of database management
- Python MySql
- Create database
- Create a table
- Insert into table
- Select query
- Where clause
- OrderBy clause
- Delete query
- Drop table
- Update query
- Limit clause
- Join and Self-Join
- MongoDB (Unstructured)
- Insert_one query
- Insert_many query
- Update_one query
- Update_many query
- Create_index query
- Drop_index query
- Delete and drop collections
- Limit query
Hands-on
- CRUD operations using Python MySql and MongoDB
Data Connection and Visualization in Tableau (Self-Paced)
Topics
- Data Visualization
- Business Intelligence tools
- VizQL Technology
- Connect to data from the File
- Connect to data from the Database
- Basic Charts
- Chart Operations
- Combining Data
- Calculations
Hands-on
- Connecting to data from File, Database, and Server
- Performing operations on Hierarchies, Data Granularity and Highlighting feature
- Creating calculated fields using basic functions
- Defining LOD expressions
- Creating Parameters
- Performing User Input and What-if analysis
Advanced Visualizations (Self-Paced)
Topics
- Trend lines
- Reference lines
- Forecasting
- Clustering
- Geographic Maps
- Using charts effectively
- Dashboards
- Story Points
- Visual best practices
- Publish to Tableau Online
Hands-on
- Analyzing data using techniques including Forecasting, Trend Lines, Reference Lines, Clustering, and Geographic Maps
- Building Dashboard Layout and Formatting
- Building Story points
In-Class Project (Self-Paced)
Topics
- Predict the species of Plant
How it helps
Professional roles such as a Chief Analytics Officer, Chief Data Scientist, or Data Scientist are some of the most lucrative ones in the analytics industry. Organisations strive to employ skilled employees who are experts of this domain, to accrue analytical insights and drive business strategies. Completing this course will help you do the same.
The Python Machine Learning Certification Training course offers real-time case studies, hands-on projects, and instructor-led live classes to help you learn all the core topics. You will also receive lifetime access to the 24x7 support and LMS, to keep learning, whenever, wherever.
Additionally, the online Python Machine Learning Certification Training by Edureka also offers an esteemed certificate upon completion. This will help you shine a light on your acquired techniques and skills in machine learning, Python programming, and analytics. As a result, you’ll be able to ace interviews and secure desired profiles in top-notch organisations.
FAQs
No. You will be using a Cloud LAB environment for practical work. This environment will already house the required software to execute the practicals.
The curriculum will provide you with Jupyter Notebook, pre-installed on your Cloud LAB to work on your practicals. The access details will be on the LMS.
If you miss a lecture or class, don’t fret. You can easily attend another live batch or go through pre-recorded videos.
Yes; Edureka will provide you with a resume-building software. Using this, you will be able to build a strong CV and ace placements.
You can contact Edureka for clearing any query or doubt. You can reach them at 18442306365/ 9870276459, or email them at sales@edureka.co.