Becoming a Machine Learning Specialist: The path to AI proficiency

Content

Modules Included:

1. Supervised Machine Learning

• Introduction to Artificial intelligence
• Introduction to machine learning
• supervised and unsupervised machine learning
• algorithms for supervised machine learning
• K-Nearest Neighbor Algorithm
• Naïve Bayes Classification
• Decision Trees
• Random forest
• Support Vector Machines
• Linear Regression
• Logistic Regression
• Ridge Regression
• Lasso Regression
• Polynomial Regression
• Bayesian Linear Regression
• Lab-Implementation of K-Nearest Neighbor (K-NN) algorithm used for classification and regression.
• Lab-Implementation of Naïve Bayes Classification module employs Bayes' Theorem to predict the probability of different classes for a given input.
• Lab-Implementation of machine learning algorithm used for classification and regression tasks that model decisions and their possible consequences in a tree-like structure.
• Lab-Implementation of an ensemble learning method for constructing a multitude of decision trees during training and outputting the mode of the classes for classification or mean prediction for regression from the individual trees.
• Lab-Implementation of functionality to train and utilize a Support Vector Machine (SVM) model for classifying data points into two categories based on their features.
• Lab-Implementation of Linear regression to model the relationship between a dependent variable and one or more independent variables
• Lab-Implementation of Logistic regression for binary classification, modeling the probability of a binary outcome as a function of one or more predictor variables.
• Lab-Implementation of Ridge regression  in predicting the target variable.
• Lab-Implementation of Lasso regression for both prediction and feature selection in high-dimensional datasets.
• Lab-Implementation of Polynomial regression for higher-order polynomial functions to capture more complex patterns in the data
• Lab-Implementation of Bayesian Linear Regression models to estimate posterior distributions, enabling uncertainty quantification in predictions.

2. Unsupervised and Reinforcement Learning

• algorithms for unsupervised machine learning
• Explaining Clustering methods
• K-means clustering
• Hierarchical Clustering
• Anomaly detection
• recommender systems
• content based filtering
• principal component analysis
• Apriori Algorithm
• Reinforcement Learning
• Algorithms for reinforcement learning
• Q learning
• R learning
• TD learning
• Life cycle of Machine learning
• Benefits and Real-world Applications
• Lab-Implementation of K-means clustering an unsupervised machine learning algorithm used to partition a dataset into K distinct, non-overlapping clusters by minimizing the within-cluster variance.
• Lab-Implementation of Hierarchical Clustering a bottom-up approach where clusters are recursively merged or top-down where clusters are recursively split.
• Lab-Implementation of Anomaly detection aiding in detecting anomalies or deviations from expected behavior.
• Lab-Implementation of recommender systems employs algorithms like collaborative filtering and content-based filtering to generate accurate recommendations.
• Lab-Implementation of recommendation technique based on the attributes or content of the items.
• Lab-Implementation of PCA a dimensionality reduction technique enabling simplified data analysis and visualization.
• Lab-Implementation of Apriori algorithm a classic data mining technique for finding frequent itemsets in a transaction database and extracting association rules.
• Lab-Implementation of Q-learning a reinforcement learning technique that learns optimal actions by iteratively updating action-values
• Lab-Implementation of reinforcement learning, enabling users to develop and train agents to make sequential decisions in dynamic environments efficiently
• Lab-Implementation of reinforcement learning method bridging the gap between dynamic programming and Monte Carlo methods

3. Neural Networks and Advanced Learning

• Intuition of Neural Networks
• Neural network models
• Python for Neural networks
• Training Neural Networks
• Activation functions
• Backpropagation
• Applying machine learning and evaluation of a model
• Regularization and Bias/Variance
• Machine learning Development progress
• Decision tree learning
• Tree ensembling
• Lab-Implementation of "Training Neural Networks" for understanding Neural networks, covering their architecture, training process, and applications
• Lab-Implementation of Activation functions in neural networks to determine the output of a neuron.
• Lab-Implementation of Backpropagation computational algorithms to train neural networks enabling efficient learning of complex patterns and tasks.
• Lab-Implementation of machine learning techniques for emphasizing model evaluation for robust performance assessment
• Lab-Implementation of "Regularization and Bias/Variance" for foundational understanding and bias variance for overfitting and underfitting problems
• Lab-Implementation of neural network machine learning models using python programming language
• Lab-Implementation of Decision tree learning a machine learning technique to maximize information gain or minimize impurity at each node.
• Lab-Implementation of Tree ensembling computational algorithms such as Random Forest and Gradient Boosting for aggregating predictions

What does a Machine learning developer do?

A machine learning developer possesses a diverse set of skills and knowledge required to perform various tasks and responsibilities related to machine learning models and algorithms to solve problems on machine learning specific problems to enhance the capabilities of the system. With exposure to various machine learning, reinforcement learning algorithms, the developer can optimize model performance, troubleshooting, debugging, and more through various strategies for software development and data analytics etc… Their expertise plays a decisive role in driving innovation, able to practically optimize the projects to deliver a best version of a model with high efficiency

What are the domains where machine learning developer work?

A machine learning developer works across various domains and industries that leverage Machine learning and AI technologies. Some of the common domains where a machine learning developer may work include.

• • Virtual Assistants
  • Healthcare- diagnostic imaging, drug discovery
  • Finance sector
  • AI Research and development
  • Ecommerce recommendations systems and demand forecasting
  • Natural language Generation, Computer vision
  • Social Network, Media, and Communication
  • Industries that provide software as a service
  • Autonomous vehicles
  • Smart agriculture
  • Marketing -customer segmentation, churn prediction

Top Job Roles of machine learning developer 

• •  Machine learning architect
  • Machine learning Developer
  • Machine learning Engineer
  • AI Solution Architect
  • ML data Scientist
  • Machine learning research scientist

Salary of a Machine Learning Developer in India.

According to recent surveys and reports, the average annual pay package for a machine learning developer in India is around ₹9 lakhs per year. However, the salary may vary depending on factors such as years of experience, skills, location, and the organization you work for. An Entry level ML engineer gets an offer of ₹7 lakhs per year. Senior professionals with a considerable amount of experience can earn up to ₹36 lakhs per annum. Some startups and large technology firms offer higher salaries to attract top talent for critical roles in upgraded AI and ML technologies.

Is it worth to pursue a career in Machine Learning developer?

As the Artificial Intelligence and Machine Learning is rapidly evolving, and demand for ML developers are expected to increase in the coming years with respect to the projected growth, salaries for ML developer may continue to rise, making it a lucrative career choice.