AI & ML Online Training Syllabus

AI & ML Online Training

 

SYLLABUS

 

Section 1: Introduction to Artificial Intelligence(AI) and Machine Learning(ML)

  • Understanding AI & ML
  • Applications of AI & ML
  • Setting up Python environment for AI & ML programming

 

Section 2: Python Programming Fundamentals

  • Python basics
  • Control flow statements
  • Functions and modules for data manipulation

 

Section 3: Data Acquisition & Pre-processing

  • Data Collection from different sources
  • Web API, Open Data Sources, Data APIs, Web Scrapping
  • Data Cleaning
  • Exploratory Data Analysis (EDA)

 

Section 4: Data Transformation & Visualization

  • MinMax, Log transform
  • z-score transformation
  • Binning and standardization
  • Data Visualization Techniques

 

Section 5: Supervised Learning Algorithms

  • Linear Regression
  • Logistic Regression
  • k-Nearest Neighbors (k-NN)
  • Decision Trees and Random Forests
  • Model Evaluation Metrics: Accuracy, Precision, Recall, F1-score

 

Section 6: Unsupervised Learning

  • Clustering Techniques: K-Means, Hierarchical Clustering
  • Dimensionality Reduction: Principal Component Analysis (PCA)
  • Anomaly Detection
  • Recommender Systems

 

Section 7: Introduction to Neural Networks and Deep Learning

  • Feedforward Neural Networks
  • Activation Functions
  • Training Neural Networks: Backpropagation
  • Introduction to Convolutional Neural Networks (CNNs)
  • Building a network with TensorFlow/Keras

 

Section 8: Introduction to Natural Language Processing (NLP)

  • Text Pre-processing, Vector space model
  • Sequence tagging, sentence structure
  • Text Classification, Machine Translation
  • Sentiment Analysis
  • Introduction to Recurrent Neural Networks (RNNs) and LSTMs

 

Section 9: Ethics and Future Trends

  • Bias and Fairness in AI
  • Privacy Concerns
  • Explainable AI
  • Future Trends in AI and ML

UPCOMING BATCHES

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