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AI & ML

Information Technology


Provider Name : Apson

Duration(Hrs) :90

Hourse/day :2

Training Type :Virtual Session

Certification :Yes

No of Slots :650

  1. Introduction to Artificial Intelligence and Machine Learning:
  1. Overview of AI and ML.
  2. Historical perspective and milestones.
  3. Applications of AI and ML in various domains.
  4. Mathematics and Statistics for Machine Learning:
  1. Linear algebra (vectors, matrices, operations).
  2. Calculus (derivatives, gradients).
  3. Probability theory (probability distributions, Bayes’ theorem).
  4. Statistics (descriptive statistics, hypothesis testing).
  5. Python Programming for Machine Learning:
  1. Basics of Python programming language.
  2. Data structures (lists, tuples, dictionaries).
  3. Control flow (if statements, loops).
  4. Functions and modules.
  5. File handling.
  6. Data Preprocessing and Exploratory Data Analysis (EDA):
  1. Data cleaning and handling missing values.
  2. Feature engineering and transformation.
  3. Exploratory data analysis techniques.
  4. Data visualization with libraries like Matplotlib and Seaborn.
  5. Supervised Learning Algorithms:
  1. Linear regression.
  2. Logistic regression.
  3. Decision trees and ensemble methods (random forests, gradient boosting).
  4. Unsupervised Learning Algorithms:
  1. K-means clustering.
  2. Hierarchical clustering.
  3. Dimensionality reduction techniques (PCA).
  4. Model Evaluation and Validation:
  1. Cross-validation techniques.
  2. Evaluation metrics (accuracy, precision, recall, F1-score).
  3. Overfitting and underfitting.
  4. Hyperparameter tuning.
  5. Deep Learning Fundamentals:
  1. Introduction to neural networks.
  2. Basics of TensorFlow
  3. Building and training neural network models.
  4. Convolutional Neural Networks (CNNs) for image recognition.
  5. Recurrent Neural Networks (RNNs) for sequence data.
  6. Natural Language Processing (NLP):
  1. Introduction to NLP.
  2. Text preprocessing (tokenization, stemming, lemmatization).
  3. Sentiment analysis.
  4. Named Entity Recognition (NER).
  5. Reinforcement Learning:
  1. Introduction to reinforcement learning concepts.
  2. Markov decision processes (MDPs).
  3. Q-learning and policy gradients.
  4. Advanced Topics in Machine Learning:
  1. Support Vector Machines (SVMs).
  2. Bayesian methods.
  3. Ensemble learning techniques.
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