Machine Learning / AI

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Machine Learning is the study of computer algorithms that can improve user experience automatically through experience and by the use of data. Machine Learning is the science of getting computers to act without being explicitly programmed.

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img Duration

8 Weeks

Course Details

Machine Learning is the study of computer algorithms that can improve user experience automatically through experience and by the use of data. Machine Learning is the science of getting computers to act without being explicitly programmed.

It is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate human behaviour and gradually improving its accuracy. In its implementation across business problems, machine learning is also referred to as predictive analytics.

Tech Booster Institute of Professional Studies is the best for Machine Learning course, we also provide project guidance along with the study materials. And last but not the least Tech Booster also provides guaranteed job placements along with the certification after the completion of the course.

 

Few Examples of Machine Learning Are: 

Face Recognition, Image Recognition, Speech Recognition, Medical Diagnosis, Statistical Arbitrage, Learning Associations, Classification Prediction, Extraction Regression, Financial Services Conclusion.

 

Course Curriculum:

  • Basic understanding of linear algebra
  •  Matrices, vectors Addition and Multiplication of matrices, Geometric Interpretation of Linear Algebra
  •  Fundamentals of Probability, Bayes Theorem
  •  Introduction to Data Analysis and Machine Learning
  •  Introduction to Python - writing programs in Python
  •  Introduction to Pandas, NumPy, Matplotlib, Scikit-learn
  • (Classification) K-nearest neighbour
  •  (Classification) naive Bayes Classifier
  •  (Classification) Entropy, information gain, decision tree, random forest
  •  (Regression) Simple Linear Regression
  •  (Regression) Multiple Linear Regression
  •  (Clustering) K-means
  • (Clustering) Mixture of Gaussians, Expectation Maximization Algorithm
  •  (Clustering) Hierarchical Clustering
  •  Principal Component Analysis (PCA)
  •  (Statistics) Descriptive and Inferential Statistics
  •  (Statistics) Hypothesis Testing, P-Value and Z-Score Method, ANOVA
  • Introduction to Neural Network and Deep Learning
  •  Introduction to TensorFlow
  •  Implementing Neural Networks
  •  Tuning Neural Networkss
  •  Convolution Neural Networks
  •  Recurrent Neural Networks
  • Assignment:
  •  After completion of training
  • Guwahati Branch

    2nd Floor, House No-209, Guwahati

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