Mastering Machine Learning: From Fundamentals to Advanced Techniques

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About Course

Looking to jumpstart your career in the field of Machine Learning? Our Machine Learning Professional Course is designed to equip you with the skills and knowledge to become an expert in this rapidly growing field. With a focus on practical applications, this course covers topics ranging from the basics of data analytics to advanced machine learning techniques.

Our comprehensive curriculum includes everything you need to succeed, including hands-on experience with popular tools and platforms like Python, TensorFlow, and Keras. You’ll learn to work with big data, and develop the skills necessary to design and implement complex algorithms. Our experienced instructors will guide you through real-world case studies and projects, giving you valuable hands-on experience and preparing you for success in the industry.

With a career in machine learning, you can work with some of the biggest and most innovative companies in the world. From healthcare to finance, and from transportation to e-commerce, machine learning has applications in every industry. With high demand and lucrative salaries, a career in machine learning can open up doors to endless opportunities.

Our course also includes additional resources like resume building, interview preparation, and job referral services to help you take the next step in your career. With lifetime access to course materials and continuous support from our instructors, you can be confident in your journey to becoming a machine learning professional.

Don’t wait to start your journey in the exciting field of machine learning. Enroll in our Machine Learning Professional Course today and start your journey towards a rewarding and fulfilling career.

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What Will You Learn?

  • Course Curriculum
  • Foundations/Introduction:
  • MODULE 1
  • 1. Introduction to Python
  • a. Python Basics
  • b. Python Functions and Packages
  • c. Working with Data Structures, Arrays, Vectors & Data Frames
  • d. Jupyter Notebook – Installation & Function
  • e. Pandas, NumPy, Matplotlib, Seaborn
  • MODULE 2
  • 2. Applied Statistics
  • a. Descriptive Statistics
  • b. Probability & Conditional Probability
  • c. Hypothesis Testing
  • d. Inferential Statistics
  • e. Probability Distributions
  • Machine Learning
  • MODULE 1
  • 1. Supervised Learning
  • a. Linear Regression
  • b. Multiple Variable Linear Regression
  • c. Logistic Regression
  • d. Naive Bayes Classifiers
  • e. k-NN Classification
  • f. Support Vector Machines
  • g.
  • MODULE 2
  • 2. Ensemble Techniques
  • a. Decision Trees
  • b. Bagging
  • c. Random Forests
  • d. Boosting
  • MODULE 3
  • 3. Unsupervised Learning
  • a. K-means Clustering
  • b. Hierarchical Clustering
  • c. Dimension Reduction-PCA MODULE 4 Featurisation, Model Selection & Tuning
  • MODULE 4
  • 4. Featurisation, Model Selection & Tuning
  • a. Feature Engineering
  • b. Model Selection and Tuning
  • c. Model Performance Measures
  • d. Regularising Linear Models
  • e. Ml Pipeline
  • f. Bootstrap Sampling
  • g. Grid Search Cv
  • h. Randomized Search Cv
  • i. K Fold Cross-validation
  • MODULE 5
  • 5. Recommendation Systems
  • a. Introduction to Recommendation Systems
  • b. Popularity Based Model
  • c. Content based Recommendation System
  • d. Collaborative Filtering (User similarity & Item similarity)
  • e. Hybrid Models

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