Discover the full potential of Machine Learning with our comprehensive training program in Bangalore. Led by experienced industry professionals, our course is designed to give you the skills & knowledge you need to excel in the field. Join now and take your career to the next level with the best Machine Learning training in Bangalore.
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- Our Machine Learning Courses are well crafted and designed for candidates for all backgrounds and experiences.
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NearLearn is located in Bangalore and has trained thousands of students and holding years of experience in professionally providing training.Our industry experts teach and train you cutting edge machine learning techniques with python from fundamental to advance level.
We Provide Best Machine Learning Training in Bangalore, as per the modern industry standards. Our coaching plans will allow professionals to achieve placements. We are one of the Top Machine Learning Training Institute in Bangalore that contributes hands-on practical experience / practical attempt on live projects and will guarantee the job with the guidance of advance level Machine Learning Courses. At NearLearn Machine Learning Training in Bangalore is wielded by expert serving certified experts having 10+ years of expertise in performing real-time Machine Learning designs.
Who should take the Machine Learning Course?
The Machine Learning training in Bangalore will make the candidate professional, for this role requires the perfect amalgam of experience, data science knowledge, and using the correct tools and technologies. It is a good career choice for both new and experienced professionals. Aspiring professionals of any educational background with an analytical frame of mind are most suited to pursue this Program a basic understanding of 10+2 level mathematics and programming will be an added advantage, including:
- Any Fresher or Jobseeker who has 10+2 level mathematics and basic programming knowledge.
- Students in UG/ PG Programs with 10+2 level mathematics and basic programming knowledge
Machine Learning Training Requirements
Computer skills, Basic knowledge of Mathematics and Basic knowledge of Data Science concepts.
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Introduction to Data Science with AI Preview
- Lecture1.1 Data science & its importance
- Lecture1.2 Key Elements of Data Science
- Lecture1.3 Data Warehousing
- Lecture1.4 Business Intelligence
- Lecture1.5 Data Visualization
- Lecture1.6 Data Mining
- Lecture1.7 Machine Learning
- Lecture1.8 Artificial Intelligence
- Lecture1.9 Cloud Computing
- Lecture1.10 Big Data
- Lecture1.11 Artificial Intelligence: A preview
- Lecture1.12 What is Artificial Intelligence & its importance.
Introduction to Machine Learning
Lecture2.1 What is Machine Learning (ML)?
Lecture2.2 How machines learn
Lecture2.3 Types of learning: Supervised, Semi-supervised, Unsupervised, Reinforcement
Lecture2.4 Basics of Classification, Regression and Clustering algorithms
Lecture2.5 Creating your first Prediction Model
Lecture2.6 Training & Model evaluation
Lecture2.7 Choosing Machine Learning algorithm
Back to Basics (MATHS WITH STATISTICS)
Lecture3.1 A quick refresh on basic intermediate maths
Lecture3.2 Linear Algebra (Vectors, Matrix, Eigen Values)
Lecture3.3 Probability and Statistics
Lecture3.4 Hypothesis testing
Lecture3.5 Optimization
Python For Data Science
Lecture4.1 A quick crash course on basics of Python
Lecture4.2 Python Object and Data Structure
Lecture4.3 Python Comparison Operators
Lecture4.4 Python Statements
Lecture4.5 Methods and Functions
Lecture4.6 Milestone Project – 1
Lecture4.7 Object-Oriented Programming
Lecture4.8 Modules and Packages
Lecture4.9 Errors and Exception Handling
Lecture4.10 Milestone Project – 2
Lecture4.11 Built-in Functions
Lecture4.12 Python Decorators
Lecture4.13 Python Generators
Lecture4.14 Final Capstone Python Project
Lecture4.15 Advanced Python Modules
Lecture4.16 Advanced Python Objects and Data Structures
Lecture4.17 Advanced OOP
Lecture4.18 Parallel Processing
Lecture4.19 Python Packages [Statsmodels, Sci-kit Learn, Scipy, Numpy, Pandas, Matplotlib, Seaborn, etc.]
Data processing for Machine Learning
Lecture5.1 Data Collection & Preparation
Lecture5.2 Data Mugging
Lecture5.3 Outlier Analysis
Lecture5.4 Missing value treatment
Lecture5.5 Feature Engineering
Lecture5.6 Data Transformation
Lecture5.7 Normalization vs Standardization
Lecture5.8 Creating Dummies
Lecture5.9 Dimensionality Reduction
Lecture5.10 Principal Component Analysis
Advanced Machine Learning Algorithms
Lecture6.1 Supervised Machine Learning algorithms
Lecture6.2 Linear Regression
Lecture6.3 Logistic Regression
Lecture6.4 Decision/Classification
Lecture6.5 Tree Ensemble Models
Lecture6.6 Bagging
Lecture6.7 Boosting
Lecture6.8 Random Forest
Lecture6.9 K-Nearest Neighbours (KNN)
Lecture6.10 Naive Bayes
Lecture6.11 Neural Network (Deep Learning)
Lecture6.12 Support Vector Machine
Lecture6.13 Unsupervised Machine Learning algorithms
Lecture6.14 Clustering with K-means Clustering
Lecture6.15 Bias-Variance Trade off
Lecture6.16 Regularization
Lecture6.17 Parameter tuning & grid search optimization
Case Study and Projects
Lecture7.1 Real life cases with Python
Assignments
Lecture8.1 Students would be given challenging real life cases to solve – just to augment their learning skills.
Course Schedule
Sample Certificates
IABAC Certificate
AI & Machine Learning Video
Programming Languages & Tools Covered
Why NearLearn?
Experience Elevated Learning
Expert Guidance, Industry Insights
Tailored Learning Paths
Career Development Support
Flexible Learning Options
Industry-Aligned Certifications
Community Engagement
Unparalleled Support
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