Course Curriculum

Fundamentals of Machine Learning and Data Engineering
  • 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
  • Data Cleaning and Feature Engineering
Supervised and Unsupervised ML Algorithms
  • (Regression) Simple Linear Regression
  • (Regression) Multiple Linear Regression
  • Classification Algorithms
  • Logistic Regression
  • KNN Algorithm
  • Decision Tree and Random Forest Algorithm
  • Naive Bayes Algorithm
  • Support Vector Machine Algorithm
Model Performance Analysis and Projects
  • Principal Component Analysis (PCA)
  • (Statistics) Descriptive and Inferential Statistics
  • (Statistics) Hypothesis Testing, P-Value and Z-Score Method, ANOVA
  • Confusion Matrix
  • Accuracy Score
  • Precision, Recall and F1 Score
  • Design a prediction model to classify different types of flowers
  • Create a prediction model to predict whether a person is Diabetic or not
  • Analyze the Spam dataset and design a prediction model to predict whether a mail is spam or not
Introduction to Neural Network and Deep Learning
  • Introduction to TensorFlow
  • Implementing Neural Networks
  • Artificial Neural Network
  • Forward and Backward Propagation
  • Activation Function
  • Loss Function and Optimizers
  • Regularizer and Dropout
CNN (Convolution Neural Network) and OpenCV
  • Feature Extraction
  • Padding and Stride
  • Pooling
  • Transfer Learning
  • CNN Architectures
  • YOLO Algorithm
  • Read and Write Images and Videos using Open CV
  • Project Work: Face and Object detection using OpenCV

Duration: 2 Months

Overview:

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.

Career Opportunities for Machine Learning:

Machine Learning is a good career path considering its industry demand and job prospects. Machine Learning Engineering position is one the top jobs in terms of stipend, growth of postings, and general demand.

Various Job Profiles after the completion of Machine Learning Course:

Senior Data Engineer, Software Engineer, Big Data Engineer, Automation Software Engineer, Senior Machine Learning Engineer, Computer Vision and Deep Learning Engineer and many more.

Certificate

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