Machine Learning (ML)
Course Description
Dive into the fascinating world of Machine Learning with this comprehensive course. Learn to build predictive models, analyze data, and develop smart AI solutions using real-world applications. Gain expertise in algorithms, data preprocessing, and model evaluation techniques. Perfect for beginners and professionals looking to advance their AI skills.
What You’ll Learn From This Course
- Understand the fundamentals of Machine Learning.
- Master data preprocessing techniques.
- Explore supervised and unsupervised learning.
- Hands-on projects with real-world datasets.
- Learn model evaluation and optimization.
- Gain industry-recognized certification upon completion.
Certification
Upon successful completion of the Machine Learning course, you will receive a professional certification that validates your expertise in core machine learning concepts and techniques. This certification demonstrates your proficiency in supervised and unsupervised learning, data preprocessing, model evaluation, and optimization. It highlights your ability to apply machine learning to real-world datasets and solve complex problems. This credential enhances your resume, strengthens your career prospects, and opens doors to opportunities in data-driven industries. It is an essential step for anyone aspiring to excel in the field of machine learning.
About Master Machine Learning
Course Content
The Master Machine Learning course is designed to provide a comprehensive understanding of machine learning concepts and techniques. Learn data preprocessing, supervised and unsupervised learning, and advanced algorithms through hands-on projects. Master model evaluation, optimization, and real-world application of ML tools. Gain practical experience with real datasets and industry-relevant skills. Upon completion, earn a professional certification that enhances your resume and opens career opportunities in the rapidly growing field of machine learning.
1. Introduction to Machine Learning and Python
Course Content
- Case study based discussion and problem solving thought process initiation
- Different Analytical Domain
- Industry mentors guest talk
2.Python
Course Content
- Python, Anaconda and relevant packages installations
- Structure of python program (comments, indentations)
- Operators
- Data type, variable
- User input, string methods
3. Data Structure
Course Content
- Mutable / Immutable
- List
- Tuple
- Sets
- Dictionary
- List, sets and dictionary comprehension
4. Loop & Control Statements
Course Content
- If & Else and Nested if else
- While and For loop
- Break, Continue and Pass
- Keywords in python
- Pattern making
5. Functions
Course Content
- Basic functions
- Built-in
- User-defined functions
- *args, **kwargs
6. Advanced Functions
Course Content
- Maps, filter, reduce
- Iterators and iterables
- Closures / decorators
- Generators
- File Handling and Exception Handling
7. Object Oriented Programming
Course Content
- Class & Object
- Data abstraction, encapsulation, Inheritance, Dunder methods
- Customized modules
8. Django
Course Content
- Starting your First Web Application
- Developing Standard Web Template
- Django Admin
- Models
- Views and URLconfs
- Forms
9. Numpy
Course Content
- Indexing/slicing
- Appending /Inserting on axis
- Mathematical and statistical operations
- Sort/Condition
- Transpose Operations
- Joining/splitting
10. Pandas
Course Content
- Data Extraction
- Series Dataframe and Plane
- Indexing and Slicing
- Conditions/Grouping/Imputations
- Append/concat/merge/join
- Date time functionalities and resampling
- Excel functions
11. Data Visualization with Matplotlib and Seaborn
Course Content
- Customization of matplotlib and seaborn
- Scatterplots/barplot/histogram/density plots
- Box Plot and outlier detection
- Visualization Linear relationship
- Univariate, Bivariate and Multivariate analysis
12. Database
Course Content
- MySQL
13. Statistics
Course Content
- Linear algebra
- Matrix operation and properties
- Introduction to calculus
- Theory of optimization
- Probability
- Conditional Probability
- Dependent and Independent events
- Bayes Theorem
- Descriptive / Inferential
- Variance / standard deviation
- Covariance And correlation
- Central Limit theorem
- Types of distributions
- pdf, cdf , pmf
- Confidence Intervals
- Hypothesis testing
- Z Test, t test, chi-2 test
- F-test/Anova
14. Introduction to Machine Learning
Course Content
- Introduction to Artificial Intelligence (DS, ML & DL)
- Applications of Machine Learning
- Categorization of Machine Learning
- Supervised / Unsupervised / Semi Supervised
- Parametric vs Non-Parametric
15. Supervised Learning (Regression/ Classification)
Course Content
- Linear Regression
- Polynomial Regression
- Lasso Regression
- Ridge Regression
- Stepwise Regression
- Bayesian Regression
16. Classification
Course Content
- Logistic Regression
- KNN
- SVM (Support Vector Machines)
- Decision Tree
- Naive Bayes
- LDA
- Classification for Imbalanced Dataset
17. Ensemble Learning
Course Content
- Random forest
- Adaboost
- Gradient boosting
- Decision Tree
- Xgboost
8. Other Support
Support Content
- Resume building
- Mock interviews
- LinkedIn account creation
- GitHub repository creation
- Migration of projects to GitHub
- Duration5 Months
- Enrolled70
- Lectures50
- Skill LevelEasy
- LanguageEnglish/Hindi
- Quizzes8
- CertificateYes
Pass Percentage80%
Reviews
Abhishek Tiwari
Honestly, this Machine Learning course was awesome! I went from knowing nothing about ML to actually building models myself. The lessons were easy to follow, and the hands-on projects made it fun to learn. Totally recommend it if you want to get into ML
Nishant Thakur
I was super new to Machine Learning, but this course made everything click. The real-world projects gave me so much confidence, and now I actually feel like I understand how algorithms work. It's been such a great experience