Data Science, Machine Learning and Artificial Intelligence Training

Learn Machine learning at the best Machine learning and Artificial intelligence training from top-notch software technology trainer, Shibaji Debnath. If you don’t have clear concepts of algorithms and statistical models, you must join our class to learn machine learning from scratch. First, start with the basics:

What is Machine Learning?

Machine learning can be described as a study of statistical models and algorithms, which are used by computer systems to perform precise work productively without using clear instructions, relying on models and inference instead. It is an application of artificial intelligence (AI).  Generally, it enables systems to learn and develop from reality without being explicitly programmed automatically. Some machine learning applications are as follows:

• Web Search Engine
• Spam Detector
• Photo Tagging Applications
• Stock Analysis
• Business Growth Prediction
• and Many More...

Recently,  as the world is becoming digital, artificial intelligence has had great demand in the market. Similarly, to endure artificial intelligence, the importance of machine learning is immense because AI is applied to machines. So, to move on with Artificial Intelligence, learning machine learning is extremely important.

Join Our Best Machine Learning Course in Kolkata to Learn from Scratch

Are you looking for the best machine learning course, where you can learn machine learning from scratch? Have you often searched online on how to learn machine learning? But couldn’t get satisfactory results? Yes, we can understand it. It is not only yours but also the problem of all students, who are passionate to learn machine learning. No one wants to pay much for machine learning classes. For that reason, we are here. Our team is ready to provide you with the best training on machine learning at affordable expenses. Let’s take a peek at the course that we cover for machine learning:

Course Details:

Basic Programming Training for Python

• Environment Settings
• Basic Programming Code Execution
• Variable Declarations
• Conditions & Loops
• Array, Lists, Multi Array etc
• Functions Creation
• Class / Object Creation.
• Modules Creation and Access
• File Read and Writing process.

Data Science Training Modules:-

• What is Machine Learning? and How Does It work?
• Introduction to neural networks
• Training The Model
• Types of Machine Learning
• The Linear Model
• Linear Model and Multiple Inputs
• Multiple Inputs and Outputs
• Graphical Representation
• The Object Function
• L2-norm loss
• Cross Entropy loss
• One Parameter gradient descent
• N-Parameter gradient descent
• Using Python with Jupyter Notebook
• Installing Anaconda
• Installing TensorFlow
• Introduction of TensorFlow
• Types of file formats in TensorFlow
• Inputs, outputs, targets, weights, biases - model layout
• Loss function and gradient descent - introducing optimizers
• Model output
• Going deeper: Introduction to deep neural networks
• Layers
• What is a deep net?
• Understanding deep nets in depth
• Why do we need non-linearities
• Activation functions
• Softmax activation
• Backpropagation
• Backpropagation - visual representation
• Backpropagation. A peek into the Mathematics of Optimization
• Overfitting
• Underfitting and overfitting
• Underfitting and overfitting - classification
• Training and validation, Test
• N-fold cross-validation
• Early stopping
• Initialization - Introduction
• Types of simple initializations
• Xavier initialization
• Gradient descent and learning rates<
• Stochastic gradient descent
• Gradient descent pitfalls
• Momentum
• Learning rate schedules
• Learning rate schedules. A picture
• Adaptive learning rate schedules
• Adaptive moment estimation
• Preprocessing introduction
• preprocessing
• Standardization
• Dealing with categorical data
• One-hot and binary encoding

Practical Project Implementation