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+91-834 161 1955 Mon-Fri 9am - 6pm
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AI With ML Training Key Features

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Advanced AI & ML Project Labs

Get real-time practice building intelligent systems, applying algorithms and frameworks like TensorFlow and PyTorch in our dedicated lab environment.

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Flexible Online and In-Person Classes

Learn at your convenience through our classroom sessions at Ameerpet or Kukatpally, or join live interactive online classes from anywhere in the world.

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Dedicated AI Project Mentorship

Receive personalized assistance for all your AI and ML projects and complex technical queries from our experienced instructors during and after your course.

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Strategic Career & Placement Guidance

We help you prepare for AI/ML interviews with mock sessions, resume optimization, and direct connections to job opportunities in leading tech companies.

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End-to-End AI/ML Solutions

Gain invaluable experience by working on comprehensive AI and Machine Learning projects, from data preprocessing and model training to deployment and optimization.

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Innovative AI/ML Community

Engage with a supportive community of peers and instructors, fostering collaborative learning, knowledge sharing, and networking opportunities in AI and ML.

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AI With ML Training Overview

Value Learning offers cutting-edge AI With ML training courses at both Ameerpet and Kukatpally (KPHB), Hyderabad. Our programs are meticulously designed to equip you with the deep understanding and practical expertise needed to thrive in the rapidly evolving fields of artificial intelligence and machine learning.

AI With ML encompasses foundational concepts of artificial intelligence coupled with various machine learning algorithms, including supervised, unsupervised, and deep learning techniques. This powerful combination allows professionals to build intelligent systems, automate complex decisions, and extract valuable insights from vast datasets. Our expert-led training ensures you grasp both the theoretical underpinnings and the hands-on implementation using industry-standard tools and frameworks, preparing you for impactful roles in AI and ML development.

320

Successful Learners

68k

Training Hours Delivered

540

Enterprise Projects Covered

AI With ML Training Objectives

The AI With ML course at Value Learning, delivered at our Ameerpet and Kukatpally (KPHB) centers in Hyderabad, is designed to give learners a robust understanding of core Artificial Intelligence principles and the practical application of Machine Learning algorithms.

Through this training, you will gain hands-on experience with popular machine learning frameworks, building, training, and evaluating predictive models. You'll delve into the concepts of neural networks and deep learning, understanding how to apply them to solve complex analytical problems.

The primary goal of the training is to empower learners to develop intelligent solutions for real-world challenges across various industries, utilizing advanced AI and ML techniques to drive innovation and efficiency.

To equip learners with comprehensive, practical experience in the end-to-end AI and Machine Learning project lifecycle, from data processing and model development to deployment and optimization, preparing them for specialized roles in the field.

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Course Curriculum - AI with ML

Overview:
  • Defining Artificial Intelligence: Strong AI vs. Weak AI
  • Understanding Machine Learning as a Core AI Subfield
  • Branches of AI: Robotics, NLP, Computer Vision, Expert Systems
  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
  • Real-world Applications of AI and ML Across Industries

  • Setting up the Environment: Anaconda, Jupyter Notebook/VS Code
  • Fundamentals of Python Programming for Data Science
  • Essential Libraries: NumPy for Numerical Operations
  • Data Manipulation with Pandas for DataFrames
  • Introduction to Scikit-learn for Machine Learning

  • Importance of Data Quality in ML
  • Handling Missing Values, Outliers, and Duplicate Data
  • Feature Scaling: Standardization and Normalization
  • Encoding Categorical Data: One-Hot Encoding, Label Encoding
  • Data Visualization with Matplotlib and Seaborn for Insights

  • Probability Concepts and Distributions (Normal, Binomial)
  • Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
  • Inferential Statistics: Hypothesis Testing, p-value, Confidence Intervals
  • Correlation vs. Causation
  • Introduction to Linear Algebra and Calculus for ML (conceptual)

  • Introduction to Regression Problems
  • Simple and Multiple Linear Regression
  • Polynomial Regression for Non-linear Relationships
  • Regularization Techniques: Ridge and Lasso Regression
  • Evaluation Metrics for Regression: MAE, MSE, RMSE, R-squared

  • Introduction to Classification Problems
  • Logistic Regression for Binary and Multi-class Classification
  • K-Nearest Neighbors (KNN) Algorithm
  • Support Vector Machines (SVM)
  • Decision Trees and Random Forests for Classification
  • Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, Confusion Matrix, ROC-AUC

  • Introduction to Unsupervised Learning Paradigms
  • K-Means Clustering: Algorithm, Choosing K, Use Cases
  • Hierarchical Clustering
  • Principal Component Analysis (PCA) for Dimensionality Reduction
  • Applications in Customer Segmentation, Anomaly Detection

  • Concept of Ensemble Learning: Bagging and Boosting
  • Random Forest: Building Multiple Decision Trees
  • Gradient Boosting Machines (GBM)
  • XGBoost, LightGBM, and CatBoost (overview and advantages)
  • Benefits of Ensemble Methods for Model Performance

  • Cross-Validation Techniques (K-Fold, Stratified K-Fold)
  • Understanding Bias-Variance Tradeoff and Overfitting/Underfitting
  • Hyperparameter Tuning: Grid Search, Random Search, Bayesian Optimization (concepts)
  • Feature Selection Techniques
  • Model Persistence: Saving and Loading Trained Models

  • Introduction to Artificial Neural Networks (ANN)
  • Perceptrons and Multi-Layer Perceptrons
  • Activation Functions (ReLU, Sigmoid, Softmax)
  • Loss Functions and Optimizers (Gradient Descent, Adam)
  • Building and Training Neural Networks with Keras (on TensorFlow)

  • Introduction to Computer Vision Tasks
  • Architecture of CNNs: Convolutional Layers, Pooling Layers
  • Building and Training CNNs for Image Classification
  • Transfer Learning and Fine-tuning Pre-trained Models
  • Applications: Object Detection, Image Recognition

  • Introduction to Sequence Data and RNNs
  • Understanding LSTM and GRU Architectures
  • Text Preprocessing: Tokenization, Stemming, Lemmatization
  • Word Embeddings (Word2Vec, GloVe - concepts)
  • Building Basic Models for Sentiment Analysis or Text Classification

  • Introduction to Reinforcement Learning: Agent, Environment, Reward, State, Action
  • The Concept of Policy and Value Function
  • Q-Learning Algorithm (overview)
  • Exploration vs. Exploitation Tradeoff
  • Simple Reinforcement Learning Examples

  • Introduction to MLOps Principles and Practices
  • Version Control for ML Projects (Git/GitHub)
  • Building Simple APIs for Model Serving (Flask/Streamlit)
  • Containerization with Docker for Deployment
  • Monitoring and Updating ML Models in Production

  • Real-world Case Studies: Healthcare, Finance, E-commerce
  • Ethical Considerations in AI: Bias, Fairness, Transparency
  • Future Trends in AI and Machine Learning
  • Career Opportunities: AI Engineer, ML Scientist, Data Scientist
  • AI/ML Job Market Landscape in Hyderabad, Telangana, India
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