Artificial Intelligence online training in Hyderabad India
Artificial Intelligence (AI) is a branch of computer science focused on creating intelligent machines that can mimic human-like behaviors and perform tasks that typically require human intelligence. AI systems are designed to perceive their environment, reason about it, learn from past experiences, and make decisions or take actions to achieve specific goals. AI encompasses various subfields, including machine learning, natural language processing, computer vision, robotics, and expert systems. Machine learning, in particular, plays a crucial role in AI, enabling systems to improve their performance on tasks over time through experience and data analysis.
SNS Tech Academy offers comprehensive Artificial Intelligence online training in Hyderabad, India, catering to individuals eager to delve into the fascinating world of intelligent machines and algorithms. The training program covers a wide spectrum of AI concepts, including machine learning, deep learning, natural language processing, computer vision, and robotics. Participants engage in hands-on projects, real-world applications, and personalized mentoring sessions, gaining practical experience in developing AI models, algorithms, and applications. Whether aspiring data scientists, AI engineers, or business analysts, learners receive expert guidance and certification preparation to excel in today's competitive job market. SNS Tech Academy's Artificial Intelligence online training equips individuals with the skills and expertise needed to harness the power of AI, drive innovation, and make a meaningful impact in diverse industries and domains.
Artificial Intelligence Online Training course content :-
Introduction to Data Science Deep Learning & Artificial Intelligence Introduction to Deep Learning & AI
Deep Learning: A revolution in Artificial Intelligence
- Limitations of Machine Learning
- Need for Data Scientists
- Foundation of Data Science
- What is Business Intelligence
- What is Data Analysis
- What is Data Mining
- Value Chain
- Types of Analytics
- Lifecycle Probability
- Analytics Project Lifecycle
- Advantage of Deep Learning over Machine learning
- Reasons for Deep Learning
- Real-Life use cases of Deep Learning
- Review of Machine Learning
- Basis of Data Categorization
- Types of Data
- Data Collection Types
- Forms of Data & Sources
- Data Quality & Changes
- Data Quality Issues
- Data Quality Story
- What is Data Architecture
- Components of Data Architecture
- OLTP vs OLAP
- How is Data Stored?
- What is Big Data?
- 5 Vs of Big Data
- Big Data Architecture
- Big Data Technologies
- Big Data Challenge
- Big Data Requirements
- Big Data Distributed Computing & Complexity
- Hadoop
- Map Reduce Framework
- Hadoop Ecosystem
- What Data Science is
- Why Data Scientists are in demand
- What is a Data Product
- The growing need for Data Science
- Large Scale Analysis Cost vs Storage
- Data Science Skills
- Data Science Use Cases
- Data Science Project Life Cycle & Stages
- Data Acuqisition
- Where to source data
- Techniques
- Evaluating input data
- Data formats
- Data Quantity
- Data Quality
- Resolution Techniques
- Data Transformation
- File format Conversions
- Annonymization
- Python Overview
- About Interpreted Languages
- Advantages/Disadvantages of Python pydoc.
- Starting Python
- Interpreter PATH
- Using the Interpreter
- Running a Python Script
- Using Variables
- Keywords
- Built-in Functions
- StringsDifferent Literals
- Math Operators and Expressions
- Writing to the Screen
- String Formatting
- Command Line Parameters and Flow Control.
- Lists
- Tuples
- Indexing and Slicing
- Iterating through a Sequence
- Functions for all Sequences
- The xrange() function
- List Comprehensions
- Generator Expressions
- Dictionaries and Sets.
- Learning NumPy
- Introduction to Pandas
- Creating Data Frames
- GroupingSorting
- Plotting Data
- Creating Functions
- Slicing/Dicing Operations.
- Functions
- Function Parameters
- Global Variables
- Variable Scope and Returning Values. Sorting
- Alternate Keys
- Lambda Functions
- Sorting Collections of Collections
- Classes & OOPs
- What is Statistics
- Descriptive Statistics
- Central Tendency Measures
- The Story of Average
- Dispersion Measures
- Data Distributions
- Central Limit Theorem
- What is Sampling
- Why Sampling
- Sampling Methods
- Inferential Statistics
- What is Hypothesis testing
- Confidence Level
- Degrees of freedom
- what is pValue
- Chi-Square test
- What is ANOVA
- Correlation vs Regression
- Uses of Correlation & Regression
Machine Learning, Deep Learning & AI using Python
Introduction- ML Fundamentals
- ML Common Use Cases
- Understanding Supervised and Unsupervised Learning Techniques
- Similarity Metrics
- Distance Measure Types: Euclidean, Cosine Measures
- Creating predictive models
- Understanding K-Means Clustering
- Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
- Case study
- What is Association Rules & its use cases?
- What is Recommendation Engine & it’s working?
- Recommendation Use-case
- Case study
- How to build Decision trees
- What is Classification and its use cases?
- What is Decision Tree?
- Algorithm for Decision Tree Induction
- Creating a Decision Tree
- Confusion Matrix
- Case study
- What is Random Forests
- Features of Random Forest
- Out of Box Error Estimate and Variable Importance
- Case study
- Case study
- Various approaches to solve a Data Science Problem
- Pros and Cons of different approaches and algorithms.
- Case study
- Introduction to Predictive Modeling
- Linear Regression Overview
- Simple Linear Regression
- Multiple Linear Regression
- Case study
- Logistic Regression Overview
- Data Partitioning
- Univariate Analysis
- Bivariate Analysis
- Multicollinearity Analysis
- Model Building
- Model Validation
- Model Performance Assessment AUC & ROC curves
- Scorecard
- Case Study
- Introduction to SVMs
- SVM History
- Vectors Overview
- Decision Surfaces
- Linear SVMs
- The Kernel Trick
- Non-Linear SVMs
- The Kernel SVM
- Describe Time Series data
- Format your Time Series data
- List the different components of Time Series data
- Discuss different kind of Time Series scenarios
- Choose the model according to the Time series scenario
- Implement the model for forecasting
- Explain working and implementation of ARIMA model
- Illustrate the working and implementation of different ETS models
- Forecast the data using the respective model
- What is Time Series data?
- Time Series variables
- Different components of Time Series data
- Visualize the data to identify Time Series Components
- Implement ARIMA model for forecasting
- Exponential smoothing models
- Identifying different time series scenario based on which different Exponential Smoothing model can be applied
- Implement respective model for forecasting
- Visualizing and formatting Time Series data
- Plotting decomposed Time Series data plot
- Applying ARIMA and ETS model for Time Series forecasting
- Forecasting for given Time period
- Case Study
Machine Learning Project
Machine learning algorithms Python- Various machine learning algorithms in Python
- Apply machine learning algorithms in Python
- How to select the right data
- Which are the best features to use
- Additional feature selection techniques
- A feature selection case study
- Preprocessing
- Preprocessing Scaling Techniques
- How to preprocess your data
- How to scale your data
- Feature Scaling Final Project
- Highly efficient machine learning algorithms
- Bagging Decision Trees
- The power of ensembles
- Random Forest Ensemble technique
- Boosting – Adaboost
- Boosting ensemble stochastic gradient boosting
- A final ensemble technique
- Introduction Model Tuning
- Parameter Tuning GridSearchCV
- A second method to tune your algorithm
- How to automate machine learning
- Which ML algo should you choose
- How to compare machine learning algorithms in practice
- Sentimental Analysis
- Case study
- Introduction to Spark Core
- Spark Architecture
- Working with RDDs
- Introduction to PySpark
- Machine learning with PySpark – Mllib
Deep Learning & AI using Python
Deep Learning & AI- Case Study
- Deep Learning Overview
- The Brain vs Neuron
- Introduction to Deep Learning
- The Detailed ANN
- The Activation Functions
- How do ANNs work & learn
- Gradient Descent
- Stochastic Gradient Descent
- Backpropogation
- Understand limitations of a Single Perceptron
- Understand Neural Networks in Detail
- Illustrate Multi-Layer Perceptron
- Backpropagation – Learning Algorithm
- Understand Backpropagation – Using Neural Network Example
- MLP Digit-Classifier using TensorFlow
- Building a multi-layered perceptron for classification
- Why Deep Networks
- Why Deep Networks give better accuracy?
- Use-Case Implementation
- Understand How Deep Network Works?
- How Backpropagation Works?
- Illustrate Forward pass, Backward pass
- Different variants of Gradient Descent
- Convolutional Operation
- Relu Layers
- What is Pooling vs Flattening
- Full Connection
- Softmax vs Cross Entropy
- ” Building a real world convolutional neural network
- for image classification”
- Recurrent neural networks rnn
- LSTMs understanding LSTMs
- long short term memory neural networks lstm in python
- Restricted Boltzmann Machine
- Applications of RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- Building a Autoencoder model
- Introducing Tensorflow
- Introducing Tensorflow
- Why Tensorflow?
- What is tensorflow?
- Tensorflow as an Interface
- Tensorflow as an environment
- Tensors
- Computation Graph
- Installing Tensorflow
- Tensorflow training
- Prepare Data
- Tensor types
- Loss and Optimization
- Running tensorflow programs
- Tensors
- Tensorflow data types
- CPU vs GPU vs TPU
- Tensorflow methods
- Introduction to Neural Networks
- Neural Network Architecture
- Linear Regression example revisited
- The Neuron
- Neural Network Layers
- The MNIST Dataset
- Coding MNIST NN
- Deepening the network
- Images and Pixels
- How humans recognise images
- Convolutional Neural Networks
- ConvNet Architecture
- Overfitting and Regularization
- Max Pooling and ReLU activations
- Dropout
- Strides and Zero Padding
- Coding Deep ConvNets demo
- Debugging Neural Networks
- Visualising NN using Tensorflow
- Tensorboard
- Transfer Learning Introduction
- Google Inception Model
- Retraining Google Inception with our own data demo
- Predicting new images
- Transfer Learning Summary
- Extending Tensorflow
- Keras
- TFLearn
- Keras vs TFLearn Comparison
Artificial Intelligence (AI) is a branch of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. AI is important because it enables machines to automate complex tasks, make data-driven decisions, and solve problems in various domains, leading to increased efficiency, productivity, and innovation.
The main categories of AI are narrow or weak AI and general or strong AI. Narrow AI is designed to perform specific tasks, such as speech recognition or image classification, within a limited domain. General AI, on the other hand, refers to machines with human-like intelligence and the ability to understand, learn, and apply knowledge across different domains.
Supervised learning involves training a model on labeled data, where the desired output is provided along with the input features. The model learns to make predictions based on this labeled data. Unsupervised learning, on the other hand, involves training a model on unlabeled data, where the algorithm learns patterns and relationships within the data without explicit guidance on the output.
Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to learn from large amounts of data. Deep learning algorithms can automatically discover and extract features from raw data, whereas traditional machine learning algorithms often require manual feature engineering.
Some popular deep learning frameworks include TensorFlow, PyTorch, Keras, and Caffe. These frameworks provide high-level APIs for building and training deep neural networks, as well as efficient implementations of common deep learning algorithms.
Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. The goal of the agent is to maximize cumulative rewards over time by learning optimal strategies or policies.
Artificial Intelligence has numerous applications in real life, including virtual assistants (e.g., Siri, Alexa), recommendation systems (e.g., Netflix, Amazon), autonomous vehicles, healthcare diagnostics, fraud detection, language translation, and robotic process automation.
Natural Language Processing (NLP) is a subfield of AI that focuses on enabling computers to understand, interpret, and generate human language. NLP techniques are used in applications such as speech recognition, sentiment analysis, language translation, chatbots, and text summarization.
Ethical considerations in AI include issues related to bias and fairness in algorithms, privacy concerns with data collection and usage, job displacement due to automation, the potential for misuse of AI technologies (e.g., surveillance, autonomous weapons), and accountability and transparency in AI decision-making.
Machine learning model performance can be evaluated using various metrics, depending on the task and type of model. Common evaluation metrics include accuracy, precision, recall, F1 score, mean squared error (MSE), and area under the receiver operating characteristic curve (AUC-ROC). Cross-validation techniques such as k-fold cross-validation are also used to assess model generalization performance.
