Curriculum for this course
0 Lessons
00:00:00 Hours
Topics covered
- 1) Basics Of Machine Learning 2) Linear Regression 3) Logistic Regression 4) Decision Trees 5) Statistics & Distribution
- 6) Hypothesis testing 7) Supervised Learning 8) Unsupervised Learning 9) Reinforcement Learning 10) Probability and classification, Bayes optimal decisions
- 11) Naive Bayes and Gaussian class-conditional distribution . 11) Linear discriminant analysis 12) Principal Component Analysis 13) Learning Vector Quantization 14) Regularization methods:- Ridge,15) LASSO
- 16) K-Nearest Neighbors (KNN) 17) K means clustering 18) Dimensionality Reduction 19) Neural Networks
- 20) Concept of Agent in AI 21) Heuristic Search using Simple and Steepest hill climbing 23) Best First Search Algorithm 24) A* Algorithm
- 22) Apriori Algorithm (Association Rule) 23) Support Vector Machines
- R-PROGRAMMING
- 1) Introducing to R 2) R Data Structures 3) Help functions in R 4) Vectors 5 ) Scalars 6) Declarations 7) recycling 8) Common Vector operations – Using all and any – Vectorized operations 9) NA and NULL values – Filtering 10) Vectorised if-then else 11) Vector Equality 12) Vector Element names
- 13) Creating matrices 14) Matrix operations – Applying Functions to Matrix Rows and Columns 15) Adding and deleting rows and columns – Vector/Matrix Distinction 16) Avoiding Dimension Reduction 17) Higher Dimensional arrays – lists 18) Creating lists – General list operations 19) Accessing list components and values 20) applying functions to lists
- 21) Creating Data Frames – Matrix-like operations in frames 22) Merging Data Frames – Applying functions to Data frames 23) Factors and Tables – factors and levels – Common functions used with factors 24) Working with tables - Other factors and table related functions 25) Control statements – Arithmetic and Boolean operators and values 26) Default values for arguments - Returning Boolean values 27) functions are objects – Environment and Scope issues 28) Writing Upstairs - Recursion – Replacement functions 29) Tools for composing function code – Math and Simulations in R
- 30) S3 Classes – S4 Classes 31) Managing your objects – Input/Output – accessing keyboard and monitor 32) reading and writing files – accessing the internet 33) String Manipulation 34) Graphics – Creating Graphs – Customizing Graphs – Saving graphs to files 35) Creating three-dimensional plots 36) Linear models – Non-linear models – Time Series and Auto-correlation – Clustering
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Description
Learn 16 Algorithms from ML and AI in python programming language and follow it up with your R-Programming course too. An ideal package for any one planning to be a data scientist or analyst.
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About the instructor
- 10 Reviews
- 390 Students
- 166 Courses
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Lead Instructor
Junkminds Team Comprises of highly qualified and skilled instructructors. Each topic is carefullychosen,analyzed and recorded for the maximum benefit of students.
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