Instructor Led Machine Learning Online Training: Course Overview 

Unlock the Power of AI

Our comprehensive machine learning training program is designed to equip you with the skills and knowledge to build intelligent systems. Learn from industry experts and gain hands-on experience with real-world machine learning projects.

Key Features:

• In-Depth Curriculum: Dive deep into the core concepts of machine learning, including supervised, unsupervised, and reinforcement learning.
• Hands-On Projects: Gain practical experience by building real-world machine learning models.
• Expert Instructors: Learn from experienced machine learning practitioners and researchers.
• Flexible Learning: Study at your own pace with our self-paced online courses and live online classes.
• Career-Focused Training: Prepare for a rewarding career in machine learning and artificial intelligence.

What You’ll Learn:

• Machine Learning Fundamentals:
  • Types of machine learning
  • Model evaluation metrics
  • Bias-variance trade-off
• Supervised Learning:
  • Linear regression
  • Logistic regression
  • Decision trees
  • Random forests
  • Support vector machines
  • Naive Bayes
• Unsupervised Learning:
  • K-means clustering
  • Hierarchical clustering
  • Principal Component Analysis (PCA)
• Deep Learning:
  • Neural networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Generative Adversarial Networks (GANs)
• Natural Language Processing (NLP):
  • Text preprocessing
  • Sentiment analysis  
  • Text classification
  • Text generation
• Reinforcement Learning:
  • Markov Decision Processes (MDPs)
  • Q-learning
  • Policy gradients

Enroll Now and Elevate Your Career in AI and Machine Learning!

To apply for the Machine Learning Training, you need to either:

• You need to have a good foundation in mathematical concepts like linear algebra, calculus, probability and statistics
• You need to know at least one programming language like Python or R.
• You need to have a good understanding of OOPs concepts, algorithms and data structures.
• You need to have some basic data analysis and data visualisation skills.

Why Choose Our Machine Learning Training Program?

• Expert Instructors: Learn from experienced machine learning practitioners and researchers.
• Comprehensive Curriculum: Our curriculum covers all the essential machine learning concepts.
• Hands-On Projects: Gain practical experience by building real-world machine learning models.
• Flexible Learning: Study at your own pace with our self-paced online courses and live online classes.
• Career-Focused Training: Prepare for a rewarding career in machine learning and artificial intelligence.

Join our community of machine learning enthusiasts and unlock the future of AI!

Master Machine Learning: Your Comprehensive Online Training

Module 1: Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning
• The Machine Learning Pipeline: Data Collection, Preparation, Modeling, Evaluation, Deployment
• Key Concepts: Overfitting, Underfitting, Bias-Variance Trade-off

Module 2: Python Programming for Machine Learning

• Python Basics:
  • Variables, data types, operators
  • Control flow (if-else, for, while)
  • Functions
• NumPy and Pandas:
  • NumPy arrays
  • Pandas DataFrames
  • Data manipulation, cleaning, and analysis
• Data Visualization:
  • Matplotlib and Seaborn
  • Creating various visualizations (histograms, scatter plots, box plots)

Module 3: Statistical Concepts

• Descriptive Statistics:
  • Measures of central tendency (mean, median, mode)
  • Measures of dispersion (variance, standard deviation)  
  •  
• Probability Theory:
  • Probability distributions (normal, binomial, Poisson)
  • Bayes’ theorem
• Hypothesis Testing:
  • t-tests, z-tests, chi-square tests
  • ANOVA

Module 4: Supervised Learning

• Linear Regression:
  • Simple linear regression
  • Multiple linear regression
  • Regularization techniques (L1, L2)
• Logistic Regression:
  • Binary logistic regression
  • Multinomial logistic regression
• Decision Trees:
  • Decision tree algorithms (ID3, C4.5, CART)
  • Pruning and overfitting
• Random Forest:
  • Ensemble learning and bagging
  • Random forest algorithm
• Support Vector Machines (SVM):
  • Linear and non-linear SVMs
  • Kernel trick
• Naive Bayes:
  • Bayes’ theorem and its application
  • Text classification

Module 5: Unsupervised Learning

• Clustering:
  • K-means clustering
  • Hierarchical clustering
  • DBSCAN
• Dimensionality Reduction:
  • Principal Component Analysis (PCA)
  • t-SNE

Module 6: Model Evaluation and Deployment

• Model Evaluation Metrics:
  • Confusion matrix
  • Accuracy, precision, recall, F1-score
  • ROC curve and AUC-ROC curve
• Model Selection and Hyperparameter Tuning:
  • Cross-validation
  • Grid search and random search
• Model Deployment:
  • Using tools like Flask and Streamlit
  • Creating REST APIs

                                                  Enroll Now

By the end of this Machine Learning Online training, you will be able to:

• Clean and preprocess data
• Perform statistical analysis
• Visualize data effectively
• Build and evaluate machine learning models
• Deploy machine learning models into production