Machine Learning Training in Dehradun:
AIMS IT - Professional hits the target by imparting job-oriented Online Machine Learning Training in Noida. Machine learning is that branch of computer science that makes computers capable of learning without being openly programmed. ML is a fragment of artificial intelligence (AI) that allows software applications to become more exclusive in calculating aftermaths. According to the experts, Machine Learning and Artificial Intelligence will boom the digital industry by changing the methodology of how we interact with everything in our environment in future.
Students and Professionals seeking knowledge in ML are trained in all basic and advanced concepts of Machine Learning with realistic experience in working on real-time practical conception. Machine Learning using Python assists the students in gaining complete endwise knowledge in relation to entire theoretical and practical concepts. Moreover, we aid students in evolving knowledge in all Machine Learning real-time projects and case studies.
Why Choose AIMS IT - Professional for Machine Learning Training?
- Comprehensive Curriculum – Covers foundational to advanced ML concepts
- Hands-on Python Implementation – Master ML algorithms using industry-standard tools
- Real-world Projects & Case Studies – Gain practical experience with actual datasets
- Expert-Led Training – Learn from seasoned AI/ML professionals
- Future-Ready Skills – Prepare for the AI-driven job
Course Content
- Introduction to ML
- What is ML? Why ML?
- Introduction to Supervised ML
- Introduction to Unsupervised ML
- Difference Between AI|DLIML
- Application and Use.
- ML Libraries
- NumPy: Introduction to NumPy
- Pandas: Introduction| Data Frame/ Loading datasets /Loading data from database/ Pandas Operation.
- Matplotlib: Introduction Line Chart Pie Chart| Scatter Plot /Bar chart |Histogram
- Sklearn:: Introduction|Sklearn-API|
- Statsmodels.API.
- ML Glossary
- Variable types, k-fold CV, AUC, F1 score, Overfitting/Underfitting,
- Generalization, ROC|Confusion Matrix
- Mathematical Background for ML- Matrix ops Probability Theory (Bayes’ Theorem)
- Statistical Knowledge for ML- Mean, Median, Mode, Z-Scores, Bias -Variance Dichotomy Exploratory Data Analysis Using Visualisation Scikit-Learn Library for ML Code Exercises
- Data Collection. The Quantity & Quality of your data dictate how accurate our model is….
- Data Preparation. Wrangle data and prepare it for Training Data Wrangling using Pandas | Pre-processing
- Data and Feature Engineering | Data Split
- Choose a Model.
- Train the Model….
- Evaluate the Model….
- 6- Parameter Tuning | hyper parameter training
- Make Predictions.
- Introduction/Maths behind Supervised Machine Learning and Algo.
- Linear Regression
- Multi-Linear Regression
- Lasso/Rigde
- Decision Tree Regressor
- Support Vector Regressor
- Logistic Regression
- KNN-K Nearest Neighbours
- Support Vector Classifier (SVM-SVC)
- Decision Tree Classifier (DTC)
- Random Forest
- Naïve Bayes
- Ensemble Learning
- Introduction: Mathematics behind Clustering
- Implementation of K-mean Clustering
- Implementation of H-clustering
- Code Exercises
- Apiori rule
- IBM ATTRITION RATE PREDICTION USING MACHINE LEARNING
- COVD-19 PATIENT OUTCOME PREDICTION USING ML
- ESTIMATE THE ONLINE SALES OF ANY E-COMMERCE RETAIL FIRM USING ML
- GUI BASED MACHINE LEARNING APPLICATION TO CLASSIFY THE PLANT SPECIES OF IRIS FLOWER
- PREDICT THE CHURN RATE IN A TELECOM COMPANY USING ML
- MALL CUSTOMER SEGMENTATION USING ML
- MARKET BASKET ANALYSIS AND ASSIT A SHOPPING MALL TO STACK PRODUCT
- PREDICT AND ESTIMATE CAR RE-SALE VALUE USING MACHINE LEARNING
- WORKING ON INBULIT DATASETS
- PREDICTION! CLASSIFICATION OF HANDWRITTEN DIGITS