The Complete Machine Learning Developer Certification Course

• Module 1: Introduction to Machine Learning
  • Overview of Machine Learning:
    • Definition, applications, and real-world examples.
  • Types of Machine Learning:
    • Supervised learning, unsupervised learning, reinforcement learning.
  • Mathematics for Machine Learning:
    • Linear algebra, calculus, probability, and statistics basics.
       
• Module 2: Python and Libraries
  • Introduction to Python:
    • Basics of Python programming.
  • NumPy and Pandas:
    • Data manipulation and analysis.
  • Matplotlib and Seaborn:
    • Data visualization.
       
• Module 3: Supervised Learning
  • Linear Regression:
    • Concepts, implementation, and evaluation.
  • Logistic Regression:
    •  Binary and multiclass classification.
  • Decision Trees and Random Forests:
    • Classification and regression.
  • Support Vector Machines (SVM):
    • Basics and applications.
• Module 4: Unsupervised Learning
  • Clustering:
    • K-Means, hierarchical, and DBSCAN.
  • Dimensionality Reduction:
    • Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE).
  • Association Rule Learning:
    • Apriori algorithm.
• Module 5: Neural Networks and Deep Learning
  • Introduction to Neural Networks:
    • Perceptrons, activation functions.
  • Feedforward Neural Networks:
    • Architecture, training, and optimization.
  • Convolutional Neural Networks (CNN):
    • Image processing tasks.
  • Recurrent Neural Networks (RNN):
    • Sequential data processing.
       
• Module 6: Advanced Topics
  • Transfer Learning:
    • Using pre-trained models.
  • Generative Adversarial Networks (GANs):
    • Introduction to generative models.
  • Reinforcement Learning:
    • Markov Decision Processes, Q-learning, policy gradients.
       
• Module 7: Capstone Project and Specialization
  • Capstone Project:
    • Apply machine learning concepts to a real-world problem.
  • Specialization:
    • Dive deeper into a specific area based on interests (e.g., natural language processing, computer vision).
       
• Module 8: Additional Considerations:
  • Ethical and Social Implications:
    • Discuss ethical considerations and biases in machine learning.
  • Tools and Frameworks:
    • Explore popular machine learning frameworks such as TensorFlow or PyTorch.
       

JustAcdemy Training Institute is dedicated to fostering a dynamic learning environment where innovation meets practical application. Join a JustAcdemy near you of like-minded individuals who are passionate about pushing the boundaries of machine learning development. JustAcademy Machine Learning Training Institute Highlights are not available from any other Institute: Established in 2021, this government-recognized and ISO-certified institute has seven branches throughout Mira-Road, five-star ratings, and excellent reviews on Google, JustDial, Sulekha, Shiksha, Urbanpro, and other platforms. Expert faculty members in the field, specialized training specialists, print-only study materials, an online student portal with a mobile app for attendance, jobs, exams, and PDF books, the most recent and updated course materials, training on real data, practice sessions, and doubt clearing, small class sizes of only eight students each for individualized attention, dedicated placement teams for job placements and company tie-ups, and special weekend batches for working professionals are also available.

Key Features of Our Machine Learning Training Course:

Hands-On Experience: Training and testing in machine learning by working on real-world projects, ensuring you are job-ready from day one.
Expert Instructors: Learn from industry experts with extensive experience in machine learning development, providing you with valuable insights and guidance.
Career Support: Benefit from our career support services, including resume workshops, mock interviews, and job placement assistance.