Supercharge your career growth in Machine Learning
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Begin your learning experience and become a machine learning engineer with certificate courses curated to land your dream job.
Skills Covered in this Path
- NumPy
- Pandas
- Marginal Probability
- Bayes Theorem
- Binomial Distribution
- Normal Distribution
- Poisson Distribution
- Descriptive Statistics
- Measures of Dispersion Range and IQR
- Central Tendency and 3 Ms
- The Empirical Rule and Chebyshev Rule
- Correlation Analysis
- Data Collection
- Statistical Analysis
- Probability
- Central Limit Theorem
- Hypothesis Testing
- Chi-Square Test
- ANOVA
- Central Tendency
- Measures of Variability
- Measure of Skewness
- Kurtosis
- Frequency Distribution Table
- Data Leakage
- Data Balancing
- K-fold Cross Validation
- Model Building
- Introduction to Machine Learning
- Supervised Machine Learning
- Linear Regression
- Pearson's Coefficient
- Coefficient of Determinant
- Machine Learning Basics
- Supervised and unsupervised learning
- Algorithm basics
- K-Nearest Neighbour Algorithm
- Linear Regression Technique
- Naive Bayes Algorithm
- Support Vector Machines
- Random Forest Algorithm
- Types of Linear Regression
- Regression analysis
- Missing Value Detection
- Data handling and prediction
- Scikit Learn Library
- Logistic Regression
- Naïve Bayes
- Entropy
- Heterogeneity
- Shannon's Entropy
- Preventing Overfitting
- Random Forest
- Random Forest Regression
- Hands-on
- Logistic Regression vs Random Forest
- Linear Regression vs Random Forest
- Unsupervised Learning
- Clustering
- k-means Clustering
- Introduction to Hierarchical Clustering
- Agglomerative Hierarchical Clustering
- Euclidean Distance
- Manhattan Distance
- Minkowski Distance
- Jaccard Index
- Cosine Similarity
- Optimal Number of Clusters
- Introduction to Machine Learning
- Understanding the ML Pipeline
- Data Preparation
- Formatting Data
- Data Transformation
- Building ML models
- Analyzing ML models
- Jenkins
- Continuous Integration
- GitHub
- Committing
- Merging
- Branches
- Creating Pull Requests
- Version Control
- Containerising
- Continuous Integration
- Docker
- Docker Best practices
- Optimizing Docker Files
- Docker
- Docker Storage
- Docker Network
- Docker Compose
- Docker
- grafana
- prometheus
- Docker Monitoring
- Docker
- Docker swarm
- Orchestration
- AWS ECR
- AWS ECS
- Docker
- grafana
- prometheus
- Docker Monitoring
- Spring boot
- Deployment
- Containerization
- YAML files
- Kubernetes Architecture
- R Commands
- R Packages
- R Functions
- R Datatypes
- Operators in R
- RStudio
- Big Data basics
- Hadoop
- HDFS
- Hive basics
- Hive querying
- Hive data upload
- Hive simple operations
- Spark
- RDDs
- Hadoop
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