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1) Introduction to Machine Learning -[2 Hours]
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2) Exploratory Data Analysis- [4 Hours]
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3) Data Preprocessing -[2 Hours]
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4) Model Building and Evaluation Metrics -[2 Hours]
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5) K-Nearest Neighborhood -[2 Hours]
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6) Linear Regression -[3 Hours]
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7) Multiple Regression -[5 Hours]
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8) Logistic Regression-[3 Hours]
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9) Naïve Bayes -[2 Hours]
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10) Decision Trees -[3 Hours]
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11) Ensemble Techniques -[2 Hours]
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12) Random Forests- [1 Hours]
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13) Support Vector Machine -[2 Hours]
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14) K-Means Clustering -[2 Hours]
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15) Hierarchical Clustering- [2 Hours]
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16) Principal Component Analysis -[2 Hours]
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17) Regularization Techniques -[2 Hours]
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18) Association Mining -[2 Hours]
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19) Recommendation Engines -[2 Hours]
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18) Association Mining -[2 Hours]
18) Association Mining -[2 Hours]
1) Gradient Descent Algorithm -[2 Hours]
1) Gradient Descent Algorithm -[2 Hours]