Certification Program in Machine Learning & AI
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Master AI & Machine Learning in just 28 weeks with our expert-led online course. Learn both practical and theoretical concepts — from NLP and Deep Learning to CNN, GANs, and more. Designed for beginners and professionals alike, this program offers live weekend interactive sessions, hands-on projects, and placement assistance to help you become a job-ready AI & ML professional.
Unlocking the Potential of Artificial Intelligence and Machine Learning with Skillssetu Advanced Certification Program
Artificial Intelligence (AI) and Machine Learning (ML) have revolutionised industries, driving innovation and efficiency like never before. Skillssetu Advanced Certification Program in Artificial Intelligence and Machine Learning offers a profound exploration into these cutting-edge technologies, providing participants with the skills and knowledge needed to excel in this rapidly evolving field.
AI Training Excellence:
The program equips programmers to endow machines with the ability to comprehend relationships, make informed decisions, and autonomously interpret environments. This skill set extends to error detection and performance iteration—a critical aspect of effective machine learning.
Machine Learning Focus:
As a pivotal subset of AI, machine learning enables machines to autonomously learn from extensive datasets. SkillsSetu’s comprehensive course spans data preprocessing, time series modelling, and text mining. Tailored for beginners and professionals, the curriculum establishes a robust foundation in core AI concepts.
Why Choose SkillsSetu for AI Training?
Opting for SkillsSetu ensures access to world-class instructors and an extensive array of study materials, including videos, quizzes, and capstone projects. With round-the-clock support, SkillsSetu stands as a beacon for individuals seeking a professional education in AI and machine learning.
Curriculum Highlights:
Foundations of Artificial Intelligence: Explore the fundamentals of AI, including neural networks, deep learning, natural language processing, and computer vision.
Machine Learning Algorithms: Dive deep into supervised and unsupervised learning techniques, reinforcement learning, ensemble methods, and neural network architectures.
Data Science Essentials: Learn data preprocessing, feature engineering, model evaluation, and optimization techniques for effective data analysis and prediction.
Advanced Topics in AI: Delve into advanced AI topics such as generative adversarial networks (GANs), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transfer learning.
AI Applications and Case Studies: Gain practical experience by working on real-world AI projects and case studies across various domains, including healthcare, finance, marketing, and autonomous systems.
Key Features:
Hands-On Projects: Apply theoretical concepts to real-world projects, enhancing your practical skills and problem-solving abilities.
Expert-Led Instruction: Learn from industry-leading experts with extensive experience in AI and machine learning, gaining valuable insights and best practices.
Flexible Learning: Choose from flexible learning options, including self-paced online courses and instructor-led virtual classrooms, to accommodate your schedule and learning preferences.
Certification: Earn a prestigious certification upon successful completion of the program, validating your expertise and enhancing your career prospects in AI and machine learning.
Networking Opportunities: Connect with fellow professionals, industry experts, and potential employers through networking events, forums, and alumni networks.
Who Should Enroll:
The program is ideal for:
- Data scientists
- Software engineers
- Data analysts
- AI and machine learning enthusiasts
- Professionals looking to transition into AI and machine learning roles
Prerequisites:
Basic programming skills in languages such as Python
Familiarity with statistics, linear algebra, and calculus concepts
Prior knowledge of machine learning fundamentals is recommended but not required
Duration:
The program can be completed in 7 Months, depending on the chosen learning path and individual pace.
Invest in Your Future:
Join the Advanced Certification Program in Artificial Intelligence and Machine Learning and unlock exciting opportunities in one of the most dynamic and high-demand fields of technology. Take the next step towards becoming a proficient AI and machine learning practitioner and stay ahead in today’s competitive job market.
- 25 Sections
- 0 Lessons
- 39 Weeks
- Introduction to Machine Learning1.1 What is ML 1.2 Why ML 1.3 Types of ML 1.4 Main Challenges – Overfitting, Underfitting, Poor Quality data, Irrelevant Features etc 1.5 What are Hyperparameters 1.6 How to Select ML model0
- Classification MatricsAccuracy 2.2 Recall 2.3 Precision 2.4 F1 Score 2.5 Confusion Matrix 2.6 Classification Report 2.7 Precision/Recall Tradeoff 2.8 ROC Curve 2.9 AOC Curve 2.10 Binary and Multilabel Classification 2.11 Feature Engineering and Feature Importance/Selection0
- Classification Models3.1 Gradient Descent and Stochastic Gradient Descent 3.2 Logistic Regression 3.3 K Nearest Neighbors 3.4 Naive Bayes 3.5 Support Vector Machines 3.6 Linear Discriminant Analysis 3.7 Decision Trees 3.8 Hyperparameter Tuning - GridSearchCV and RandomizedSearchCV0
- Ensemble Techniques4.1 Bagging - Eg: Voting Classifiers 4.2 Boosting - XG Boost, Adaboost, etc 4.3 Cross-Validation 4.4 Random Forest Classifier 4.5 XG Boost Classifier 4.6 Stacking 4.7 Hyperparameter Tuning0
- Regression Techniques5.1 Simple Linear Regression 5.2 Multiple Linear Regression 5.3 Polynomial Regression 5.4 Cost Function and Gradient Descent 5.5 Performance Metrics - MSE, RMSE, MAE etc 5.6 Heteroskedasticity, Non Normality and Correlated Errors 5.7 Hyperparameter Tuning0
- Regression Models6.1 Decision Tree Regressor 6.2 Support Vector Machines 6.3 K Nearest Neighbors 6.4 Random Forest 6.5 Boosting 6.6 Hyperparameter Tuning0
- Unsupervised Learning7.1 Introduction to Unsupervised Learning 7.2 K Means Clustering 7.3 Hierarchical Clustering 7.4 Model-Based Clustering 7.5 DBSCAN 7.6 Anamoly Detection using Gaussian Mixtures0
- Dimensionality Reduction - Principal Component AnalysisDimensionality Reduction - Principal Component Analysis ( 1 Class on this topic )0
- Recommendation SystemsRecommendation Systems ( 2 Class on this topic )0
- Introduction to Artificial Neural Networks10.1 Biological to Artificial Neurons 10.2 The perception 10.3 Multi-layer Perceptrons (MLPs) 10.4 Input Layer, Hidden Layers and Output Layers 10.5 Weights and Biases 10.6 Regression MLPs 10.7 Classification MLPs 10.8 Activation Functions and Optimizers0
- Implementation using TensorFlow and Kera's11.1 Building a Neural Network using Sequential API 11.2 Building a Neural Network using Functional API 11.3 Building a Neural Network using Subclassing API 11.4 Saving and Restoring a Model 11.5 Callbacks0
- Training Deep Neural NetworksVanishing/Exploding Gradients Batch Normalization Gradient Clipping Transfer Learning - Using Pretrained Layers Pretraining on Auxiliary Task Faster Optimizers - RMSprop, AdaGrad, Adam, Nadam, Nesterov Accelerated Gradient Learning Rate Scheduling0
- Fine Tuning ModelsHow to choose number of hidden layers and number of Neurons Learning Rate, Optimizer, Batch size and Activation Functions L1 and L2 Regularization Dropouts and Batch Normalization Max Norm Regularization0
- Introduction to Computer VisionThe Architecture of Visual Cortex Convolutional Layers Feature Maps Pooling Padding Stacking Multiple Feature Maps0
- Hands on Experience - Building an Image Classifier using CNNHands-on Experience - Building an Image Classifier using CNN ( Total 2 Class on this Topic)0
- Object Detection, Image Segmentation, and Semantic SegmentationObject Detection, Image Segmentation, and Semantic Segmentation ( Total 2 class on this topic)0
- CNN ArchitecturesLearning Predefined Architectures - LeNet, AlexNet, GoogleLeNet, ResNet, VGGNet, Xception, SENet Transfer Learning - Using Pretrained Models from Keras Classification and Localization0
- Processing Sequences using Recurrent Neural NetworksIntroduction to Recurrent Neurons and Layers Memory Cells Implementation and Training of Recurrent Neural Networks Time Series using Recurrent Neural Networks Deep RNNs for Time Series Forecasting Several Time Steps Ahead Handling Long Sequences using LSTM and GRU cells0
- AutoencodersIntroduction to Autoencoders Encoder-Decoder Networks Stacked Autoencoders Reconstructing Fashion MNIST Data using Autoencoders Types of Autoencoders - Convolution, Recurrent, Denoising, Sparse and Variational Autoencoders Anamoly Detection using Autoencoders0
- Generative Adversarial NetworksWhat are GANs? Why GANs? Generator and Discriminator Building a Deep Convolutional GAN on Fashion MNIST Data0
- Reinforcement LearningWhat is Reinforcement Learning? Learning to Optimize Rewards Policy Search Hands-on Experience using Open AI Gym The Credit Assignment Problem Q Learning and Deep Q Learning Implementing Deep Q Learning using keras0
- Introduction to Natural Language ProcessingOverview of NLP and its Applications Data Preprocessing for NLP Key Components - Tokenization, Stemming and Lemmatization Hands-on Experience - Generating AI Text Sentiment Analysis in NLP using Keras0
- Neural Machine Translation (NMT)Bidirectional Recurrent Neural Networks Beam Search Sequence to Sequence Model Building a Basic Encoder-Decoder Network for NMT0
- Attention MechanismIntroduction to Attention Mechanisms Visual Attention The Transformer Architecture Fine Tuning NLP Models for NLP Tasks0
- Hands on Experience - Building a Basic ChatbotHands-on Experience - Building a Basic Chatbot (2 Class on this topic)0
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