Program Overview
In the fast-paced realm of Information Technology (IT), Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative forces, shaping industries and driving innovation. Digicrome's AIMLP Professional Certification Program offers a comprehensive deep learning experience, crafted to equip learners with the skills and knowledge needed to excel in AI and ML.
Course Overview:
Designed for aspiring Artificial Intelligence and Machine Learning Professionals, the AIMLP program aims to:
- Provide a robust understanding of core AI and ML concepts.
- Address major challenges in diverse work environments.
- Offer authentic, industry-relevant knowledge and practical insights.
- Foster global networking and collaboration opportunities.
Focus Areas:
- Technical and theoretical aspects of AI and ML.
- Development of generalizable machine learning models.
- Exploration of reinforcement learning techniques.
- Practical application of tools like Keras and TensorFlow.
- Deployment of AI and ML algorithms across various industries.
- Hands-on experience with ML tools such as support vector machines, artificial neural networks, linear regression, and Python.
Skills Acquired:
The AIMLP certification course enhances learners' proficiency and competency, preparing them for successful careers in AI and ML. Skills acquired include:
- Heuristic programming and cognitive behaviour simulation.
- Targeting roles such as ML Engineer, NLP Expert, AI Engineer, Data Analyst, and more.
- Practical application of AI and ML techniques in real-world scenarios.
Faculty and Experts:
Benefit from Digicrome's team of seasoned experts who deliver authentic knowledge through live interactive sessions, practical projects, and simulation exams. With 80 hours of comprehensive training, students gain valuable insights into the course curriculum and industry best practices.
Eligibility:
Candidates eligible for the AIMLP Professional Certification Program should have:
- A bachelor's degree in Computer Science, Statistics, Physics, or Electrical Engineering.
- Professionals in Analytics, Data Science, E-commerce, and Search Engine domains.
- Software professionals with relevant graduate degrees or certifications.
Job and Placement Opportunities:
Upon completing the program, graduates can pursue a wide range of career opportunities, including Big Data Engineer, ML Engineer, Data Scientist, Business Intelligence Developer, Research Scientist, AI Engineer, Product Manager, and more.
Seize Your Future in AI and ML:
Embrace the dynamic opportunities in AI and ML with Digicrome's AIMLP Professional Certification Program. Propel your career to new heights and become a trailblazer in the future of technology. Are you ready to shape the future? You can join us today.
AI&ML (Artificial Intelligence and Machine Learning Professional)
- ₹70000.00
Features
- 80 Hours
- Live Online
- Student Handouts
- Innovative Curriculum
Key Highlights
80 Hours- Live Online
- Student Handouts
- Innovative Curriculum
- Expert Experienced Trainers
- 1:1 Doubt Session
- Learning Management System
- 24*7 Career Support
Program Objective
- Python Functions and Packages
- Working with Data Structures,
- Arrays, Vectors & Data Frames
- Functions & Methods
- Pandas, NumPy, Matplotlib, Seaborn
- Descriptive Statistics
- Conditional Probability
- Bell curve
- Gaussian Distribution
- Normal Distribution
- Pearson Correlation
- Hypothesis Testing
- Inferential Statistics
- Probability Distributions
- Linear Regression
- Multiple Variable Linear Regression
- Logistic Regression
- Decision Tree Algorithm
- Naive Bayes Classifiers
- K-NN Classification
- Support Vector Machines
- Model Hyperparameter Tuning
- Case Study
- K-means Clustering
- Hierarchical Clustering
- DBSCAN
- Dimension Reduction-Principal Component Analysi (PCA)
- Case Study
- Introduction to Recommendation Systems
- Popularity based model
- Content based
- Recommendation System
- Collaborative Filtering (User similarity & Item similarity)
- Hybrid Models
- Bagging
- Boosting
- Introduction to Perceptron & Neural Networks
- Activation and Loss functions
- Gradient Descent
- Hyper Parameter Tuning
- Tensor Flow & Keras for Neural Networks
- Introduction to Deep Learning
- Shallow Neural Networks Deep Neural Networks
- Introduction to RNN
- Introduction to CNN
- Introduction to ANN
- Introduction to NLP
- Stop Words
- Tokenization
- Stemming and lemmatization
- Bag of Words Model
- Word Vectorizer
- TF-IDF
- POS Tagging
- Named Entity Recognition
- Sequential Models and NLP
- RNNs and its mechanisms
- Vanishing & Exploding gradients in RNNs
- LSTMs - Long short-term memory
- GRUs - Gated recurrent unit
- LSTMs Applications
- Time series analysis
- LSTMs with attention mechanism
- Neural Machine Translation
- Advanced Language Models:
- Transformers, BERT, XLNet
- Introduction to Convolutional Neural Networks
- Convolution, Pooling, Padding & its mechanisms
- Forward Propagation
- Backpropagation for CNNs
- CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
- Transfer Learning
- How to Build and Train Deep Neural networks, and apply it to Computer Vision.
- Introduction to GANs
- Generative Networks
- Adversarial Networks
- How GANs work?
- DCGANs - Deep Convolution GANs
- Applications of GANs
- RL Framework
- Component of RL Framework
- Examples of RL Systems
- Types of RL Systems
- Q-learning
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