Introduction to Machine Learning

 Certification



 Training

 Internship


Enroll Now

About Machine Learning(ML) Course

Machine Learning (ML) is a subset of artificial intelligence that enables computers to learn from and make decisions based on data. By identifying patterns and insights from large datasets, ML models can improve their performance over time without explicit programming. These models are used in various fields, from healthcare to finance, powering applications like image recognition, recommendation systems, and predictive analytics. ML techniques include supervised, unsupervised, and reinforcement learning, each with unique approaches to problem-solving. As data grows, ML continues to be a key driver of innovation, automating processes and enhancing decision-making capabilities. .

Course Details

  Name of course  :  Machine Learning basic to Advance
  Length  :  2 months
  Effort  :  2 hours per day
  Field  :  Artificial Intelligence
  Level  :  Beginners (Freshers & Working)
  Pricing  :  Will be upadted soon

What you will learn

  •  Introduction to Machine Learning
    •  Overview and Applications of ML
    •  Types of ML: Supervised, Unsupervised, Semi-supervised, Reinforcement Learning
  •  Data Preprocessing and Feature Engineering
    •  Data Cleaning and Transformation
    •  Feature Scaling and Encoding
    •  Feature Selection and Dimensionality Reduction (PCA, LDA)
  •  Supervised Learning Algorithms
    •  Linear Regression, Logistic Regression
    •  Decision Trees, Random Forests
    •  Support Vector Machines (SVM)
    •  k-Nearest Neighbors (k-NN)
    •  Naive Bayes
  •  Unsupervised Learning Algorithms
    •  Clustering (k-Means, Hierarchical Clustering, DBSCAN)
    •  Association Rules (Apriori, FP-Growth)
    •  Dimensionality Reduction (t-SNE, Autoencoders)
  •  Model Evaluation and Tuning
    •  Cross-validation, Train-Test Split
    •  Confusion Matrix, Precision, Recall, F1-Score, ROC-AUC
    •  Hyperparameter Tuning (Grid Search, Random Search)
  •  Introduction to Deep Learning
    •  Overview of Neural Networks
    •  Activation Functions (ReLU, Sigmoid, Tanh)
  •  Deep Learning Architectures
    •  Feedforward Neural Networks (FNN)
    •  Convolutional Neural Networks (CNN) for Image Processing
    •  Recurrent Neural Networks (RNN) for Sequential Data
  •  Advanced Deep Learning Techniques
    •  Long Short-Term Memory (LSTM)
    •  Generative Adversarial Networks (GANs)
    •  Attention Mechanisms and Transformers (BERT, GPT)
  •  Optimization Techniques
    •  Gradient Descent, Backpropagation
    •  Regularization (L1, L2), Dropout
  •  Deployment and Scaling of ML/DL Models
    •  Model Deployment (TensorFlow, PyTorch)
    •  Cloud-based ML Solutions (AWS, GCP, Azure)
  •  Reinforcement Learning (RL)
    •  Markov Decision Process
    •  Q-Learning, Deep Q-Networks (DQN)
  •  Libraries and Tools
    •  Scikit-learn, TensorFlow, PyTorch, Keras
Enroll Now