Artificial Intelligence Machine Learning Training
January 22, 2021 2021-03-06 10:36Artificial Intelligence Machine Learning Training
ONLINE ARTIFICIAL INTELLIGENCE
Training & Certification
DELHI | NOIDA | LAXMI NAGAR | KERALA | PUNE | BANGALORE | HYDERABAD
Artificial Intelligence Online Training Program
Machine learning Training Program
The machine learning we study about the such type of machine which takes the raw data from the their environment and
from these data and apply these information in future need. The machine learning is a part of artificial intelligence that uses delay life data to learn themselves.
The machine learning use the techniques of pattern recognition. The machine observes the thing in a pattern manner and tries to understand these patterns according their learning techniques. The machine learns and collects the knowledge without using any kind of algorithms.
In the early days the machine learning is a very good field for the career opportunity. nowadays many organization works on machine learning programs and hire the employees for it. For machine learning and artificial intelligence, the most used languages are R language and python. The machine learning also used the concept of IoT.
The chess game is the example of an Intelligence machine in which first design the approx all test cases which can occur during the execution then apply it, at the time of game playing at any move machine analyses all possible moves and choose most appropriate move. There is also many examples like eight puzzle, water jug problem and n queen problem which uses the AI concept.
A new project in design by DRDO team for the transporting the food and health treatment by automated balloons, in this system we drop the foods in packets from the helicopter in those areas for example in hill station areas, and when these packets are near to rock it can sense it and try to save from damage collide in rocks.
AI Training Program Details
PYTHON COURSE
- Literals
- Keywords
- Identifier
- Variables
- Exercise for Practice
- Statement
- Expression
- Python Indentation
- Exercise for practice
- Single Line Comment
- Multi Line Comment
- Boolean
- Number
- Strings
- Bytes
- Lists
- Tuples
- Sets
- Dictionaries
- Exercise for practice
- Arithmetic Operator
- Assignment Operator
- Comparison Operator
- Logical Operator
- Bitwise Operator
- Identity Operator
- Membership Operator
- Exercise for practice
- Python if, if-else, else-if, nested-if
- Python Loop ( For, While )
- Python Break Keyword
- Python Continue Keyword
- Python Pass
- Exercise for Practice
- Creating String
- String Operators
- Unicode String
- Use the Built-in-String Functions
- Exercise for Practice
- Create the List
- Accessing the List
- Slicing List
- Iterate a List in Python
- List Operator
- Searching Elements in a List
- Sorting a List in Python
- Built-in List Function
- Exercise for Practice
- What is Tuple
- Tuple Operation
- Difference between List and Tuple
- Exercise for Practice
- What is Dictionary
- Dictionary Operation
- Exercise for Practice
- Introduction to Function
- Defining a Function
- Calling a Function
- Function Arguments/Parameters
- What is *args and *kwargs in Python
- Keyword Arguments
- Default Parameter Values
- Passing a List as an Argument
- Return Values
- Pass Argument
- Recursion Concept
- Lambda
- Built-in Functions
- Exercise for Practice
- How to Create File
- Writing data to a File
- Reading data from a file using various Read methods
- Additional File Methods
- Pickling and Unpickling in Python
- Exercise for Practice
- Class and Object
- Use of self in OOPs
- Constructors
- Accessor and Mutator in Python
- Inheritance
- Polymorphism
- Exercise for Practice
Data Science
- Benefits of Numpy
- Basic Array Operations
- Slicing/Stacking Array
- Indexing with Boolean Array
- Use Pre-written functions
- Exercise for Practice
- What is Pandas
- Dataframe in Pandas
- Different ways of Creating Dataframe
- Read Excel and CSV files
- Handling Missing Data: Dropna, Fillna, Interpolate
- Handling Missing Data: Replace Function
- Group by: Split, Apply, Combine
- Concat Dataframe
- Merge Dataframe
- Reshape Dataframe
- Date Time Index
- Exercise for Practice
- Bar Chart
- Histogram
- Pie-Chart
- Axes, Label, Legend
- Saving Chart to a File
MACHINE LEARNING
- Introduction to Machine Learning
- Linear Regression
- Multiple Linear Regression
- Gradient Descent
- Saving Model to a File
- Dummy Variables & One-Hot Encoding
- Train-Test-Split
- Logistic Regression
- Multiple Logistic Regression
- Decision Tree
- Random Forest
- K-fold Cross Validation
- SVM
- K-Means Clustering
- Naïve Bias
Artificial Intelligence
- Perceptron
- Working of Neurons
- 1D & 2D Data
- Filters
- Strides
- Maxpooling
- Padding
- Batch Normalization
- Dropout
- Image Processing
- Activation Functions
- Loss Function
- Optimizers
- Flatten
- Dense Layer
- Early Stopping
- Model Checkpoint
- Reduce LR on Plateau
- Units
- Strides
- Maxpooling
- Batch Normalization
- Dropout
- Activation
- Optimizer
- Dense Layer
- Flatten
- Early Stopping
- Model Checkpoint
- Reduce LP on Plateau
- Taking Live feed from camera
- Face Extraction from Image
- Resize Image
- Flipping image
- Draw Rectangle
- Draw Text
- Image Colour Conversion