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Begin your learning experience and become a software developer (ai/ml) with certificate courses curated to land your dream job.
Skills Covered in this Path
- Programming Concepts
- Python Basics
- Variables and Data types in Python
- Operators and Strings in Python
- Python Data Structures
- Control Flow Statements and Functions
- OOPs
- NumPy
- Pandas
- Probability
- Statistics
- Normal Distribution
- Sampling Distribution
- Hypothesis
- Central Limit Theorem
- Advanced Statistics
- Hypothesis testing
- Type-I and Type-II error
- Python
- Statistics
- Reinforcement learning
- Machine learning
- Forecasting using Python
- Exponential Smoothing
- ARIMA
- Time Series in R
- Introduction to Machine Learning
- Understanding the ML Pipeline
- Data Preparation
- Formatting Data
- Data Transformation
- Building ML models
- Analyzing ML models
- 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
