MACHINE LEARNING

About Machine Learning

Machine learning is a type of artificial intelligence that involves training algorithms to learn from and make predictions or decisions based on data. Machine learning algorithms are able to learn and improve over time, without being explicitly programmed, by finding patterns and relationships in data and using them to make predictions or decisions.

There are two main types of machine learning: supervised learning and unsupervised learning. In supervised learning, the algorithm is trained on a labelled dataset, where the correct output is provided for each example in the training set. In unsupervised learning, the algorithm is not provided with labelled examples, and must find patterns and relationships in the data on its own.

Machine learning is used in a wide range of applications, including image and speech recognition, natural language processing, predictive modelling, and self-driving cars. It is an active area of research and development, and is expected to continue to have a significant impact in the coming years.

Usage Of Machine Learning

Machine learning is a type of artificial intelligence that is used in a wide range of applications. Some common areas where machine learning is used include:

1. 1. Image and speech recognition:   Machine learning algorithms can be used to recognize patterns in images and audio data, enabling applications such as facial recognition and voice assistants.


2. 2. Natural language processing:  Machine learning algorithms can be used to understand and process human language, enabling applications such as language translation and text classification.


3. 3. Predictive modelling:   Machine learning algorithms can be used to make predictions based on patterns and relationships in data, enabling applications such as stock market prediction and weather forecasting.


4. 4. Self-driving cars:   Machine learning algorithms can be used to enable self-driving cars to make decisions based on sensory data, such as images and audio.


5. 5. Healthcare:  Machine learning algorithms can be used to analyse medical images, predict patient outcomes, and assist with diagnosis.



Overall, machine learning has a wide range of applications in areas such as image and speech recognition, natural language processing, predictive modelling, self-driving cars, and healthcare. It is an active area of research and development, and is expected to continue to have a significant impact in the coming years.

Scope of Machine Learning

The scope of machine learning is vast and is expected to continue to have a significant impact in a wide range of industries in the coming years. Some of the areas where machine learning is expected to have a significant impact include:

1. 1. Artificial intelligence:   Machine learning algorithms are an important part of the field of artificial intelligence, and are expected to continue to be a key enabling technology for advances in this area.


2. 2. Autonomous vehicles:   Machine learning algorithms can be used to enable autonomous vehicles to make decisions based on sensory data, such as images and audio.


3. 3. Natural language processing:  Machine learning algorithms are expected to continue to improve the ability of machines to understand and process human language, leading to advances in areas such as language translation and text classification.


4. 4. Predictive modelling:  Machine learning algorithms are expected to continue to be used for predictive modelling tasks, such as stock market prediction and disease diagnosis.


5. 5. Healthcare:  Machine learning algorithms are expected to have a significant impact in the healthcare industry, enabling advances in areas such as medical image analysis and patient diagnosis.


6. 6. Robotics:   Machine learning algorithms are expected to enable robots to learn and make decisions based on sensory data, leading to advances in areas such as manufacturing and logistics.



Overall, the scope of machine learning is vast and is expected to continue to have a significant impact in a wide range of industries in the coming years. It is an active area of research and development, and is likely to continue to evolve and expand in the future.

Applications Of Machine Learning

Machine learning is a type of artificial intelligence that involves using algorithms to enable computers to learn from data and improve their performance over time. It has many applications, including:

1. 1. Predictive modeling:   Machine learning is often used to build models that can predict outcomes based on data, such as whether a customer is likely to churn or what the stock market will do.


2. 2. Image and speech recognition  Machine learning is used to enable computers to recognize and interpret images and speech, which has applications in fields such as security and healthcare.


3. 3. Natural language processing:   Machine learning is used to enable computers to understand and generate human language, which has applications in fields such as customer service and language translation.


4. 4. Fraud detection:   Machine learning is used to identify patterns in data that may indicate fraudulent activity, and can help organizations to prevent fraud.


5. 5. Personalization:   Machine learning is used to personalize user experiences, such as recommending products or content based on an individual's preferences.


6. 6. Healthcare:  Machine learning is being used in healthcare to predict patient outcomes, identify potential outbreaks of infectious diseases, and assist in the diagnosis and treatment of diseases.



Overall, machine learning has many applications and is being used in a wide range of industries to automate tasks, improve efficiency, and augment human capabilities.

Career Opportunity After Machine Learning

There are many career opportunities available for individuals with skills in machine learning, which involves using algorithms to enable computers to learn from data and improve their performance over time. Some examples of roles that may be open to individuals with machine learning skills include:

1. 1. Data Scientist:   Data scientists often use machine learning as part of their work, and may be responsible for developing and implementing machine learning models.


2. 2. Machine Learning Engineer:   Machine learning engineers design and build machine learning systems, and may be responsible for optimizing and maintaining these systems.


3. 3. Research Scientist:   Research scientists may use machine learning as part of their research, and may be responsible for developing new machine learning techniques.


4. 4. Artificial Intelligence Engineer:  Artificial intelligence (AI) engineers may work on developing and implementing AI systems that use machine learning.


5. 5. Software Developer:   Software developers may use machine learning as part of the applications they build, and may be responsible for integrating machine learning models into these applications.



Overall, there are many career opportunities available for individuals with skills in machine learning, as the ability to build and work with machine learning models is in high demand across a wide range of industries.

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Course Syllabus

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• •   MODULE 1: INTRODUCTION TO PROGRAMME
  • MOTIVATION FOR PROGRAMME
  • OVERVIEW OF PROGRAMME
  • EXPECTED OUTCOMES OF PROGRAMME
• •   MODULE 2: MACHINE LEARNING TASKS AND APPLICATIONS
  • FUNCTION APPROXIMATION (REGRESSION), CLASSIFICATION, CLUSTERING, RANKING, INFORMATION RETRIEVAL
  • TEXT PROCESSING APPLICATIONS
  • IMAGE AND VIDEO PROCESSING APPLICATIONS
  • SPEECH PROCESSING APPLICATIONS
  • DATA REPRESENTATION
• •   MODULE 3: PARADIGMS OF MACHINE LEARNING
  • SUPERVISED LEARNING & UNSUPERVISED LEARNING
  • SEMI-SUPERVISED LEARNING & ACTIVE LEARNING
  • SELF-SUPERVISED LEARNING & TRANSFER LEARNING
  • DOMAIN ADAPTATION
  • FEDERATED LEARNING
• •   MODULE 4: REVIEW OF BASICS OF MATHEMATICAL TOPICS
  • LINEAR ALGEBRA
  • CALCULUS
  • PROBABILITY AND STATISTICS
• •   MODULE 5: REGRESSION METHODS
  • SUPERVISED LEARNING
  • PARAMETER ESTIMATION - MAXIMUM LIKELIHOOD METHOD
  • OVERFITTING & REGULARISATION
  • RIDGE REGRESSION
  • LASSO
• •   MODULE 6: PROBABILISTIC MODELS FOR CLASSIFICATION
  • K-NEAREST NEIGHBOUR CLASSIFIER
  • BAYES CLASSIFIER
  • NORMAL DENSITY FUNCTION
  • MAXIMUM LIKELIHOOD ESTIMATION
  • GAUSSIAN MIXTURE MODEL
  • NAÏVE BAYES CLASSIFIER
  • DECISION SURFACES
  • DIMENSION REDUCTION METHODS
• •   MODULE 7: ARTIFICIAL NEURAL NETWORKS FOR CLASSIFICATION AND REGRESSION
  • MCCULLOCH-PITTS NEURON
  • PERCEPTRON CONVERGENCE THEOREM
  • SIGMOIDAL NEURON
  • SOFTMAX FUNCTION
  • MULTILAYER FEEDFORWARD NEURAL NETWORK
  • ERROR BACKPROPAGATION METHOD
  • GRADIENT DESCENT METHOD
  • STOPPING CRITERIA
  • LOGISTIC REGRESSION BASED CLASSIFIER
• •   MODULE 8: OPTIMIZATION AND REGULARIZATION METHODS FOR DFNNS
  • DEEP FEEDFORWARD NEURAL NETWORKS (DFNNS)
  • OPTIMIZATION METHODS
  • ADAGRAD, RMSPROP, ADADELTA & ADAM
  • SECOND ORDER METHODS
  • REGULARIZATION METHODS
  • BATCH NORMALIZATION
• •   MODULE 9: AUTOENCODERS
  • ANN & STACKED AUTOENCODER
  • GREEDY LAYER-WISE TRAINING
  • PRE-TRAINING & FINE TUNING A DFNN
  • REGULARIZATION IN AUTOENCODERS
  • DENOISING AUTOENCODER
  • VARIATIONAL AUTOENCODER
• •   MODULE 10: CONVOLUTIONAL NEURAL NETWORKS (CNNS)
  • BASIC CNN ARCHITECTURE
  • RECTILINEAR UNIT (RELU)
  • 2-D DEEP CNNS
  • IMAGE CLASSIFICATION USING 2-D CNNS
  • 3-D CNN FOR VIDEO CLASSIFICATION
  • 1-D CNN FOR TEXT AND AUDIO PROCESSING
  • VLAD METHOD FOR AGGREGATION
• •   MODULE 11: RECURRENT NEURAL NETWORKS (RNNS)
  • ARCHITECTURE OF AN RNN & ITS UNFOLDING
  • BACKPROPAGATION THROUGH TIME
  • VANISHING AND EXPLODING GRADIENT PROBLEMS IN RNNS
  • LONG SHORT TERM MEMORY (LSTM) UNITS
  • GATED RECURRENT UNITS
  • BIDIRECTIONAL & DEEP RNNS
• •   MODULE 12: ENCODER-DECODER PARADIGM BASED DEEP LEARNING MODELS
  • ENCODER-DECODER PARADIGM
  • IMAGE AND VIDEO CAPTIONING MODELS
  • MACHINE TRANSLATION
  • TEXT PROCESSING MODELS
  • REPRESENTATION OF WORDS: WORD2VEC AND GLOVE
• •   MODULE 13: TRANSFORMER MODELS
  • ATTENTION-BASED MODELS
  • POSITION ENCODING
  • ENCODER AND DECODER MODULES IN A TRANSFORMER
  • SEQUENCE TO SEQUENCE MAPPING USING TRANSFORMER
  • MACHINE TRANSLATION USING TRANSFORMER MODEL
  • VISION TRANSFORMER
  • BERT MODEL
• •   MODULE 14: GENERATIVE ADVERSARIAL NETWORKS (GANS)
  • IMAGE GENERATION MODELS
  • ARCHITECTURE AND TRAINING OF A GAN
  • DEEP CONVOLUTIONAL GAN
  • CYCLIC GAN
  • CONDITIONAL GAN
  • SUPER-RESOLUTION GAN
  • APPLICATIONS OF GANS FOR IMAGE PROCESSING
• •   MODULE 15: REINFORCEMENT LEARNING
  • INTRODUCTION TO REINFORCEMENT LEARNING
  • MARKOV DECISION PROCESSES
  • POLICY GRADIENTS
  • TEMPORAL DIFFERENCE LEARNING
  • Q-LEARNING
  • DEEP REINFORCEMENT LEARNING
  • TEXT PROCESSING USING DEEP REINFORCEMENT LEARNING

TYPE	DURATION	HOURS	TRAINING MODE
Regular Track	45-60 Days	2 Hours a Day	Live Classroom
Weekend Track	8 Weekends	3 Hours a Day	Live Classroom
Fast Track	7 Days	6+ Hours a Day	Live Classroom

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